BEHAVIORAL MALWARE DETECTION IN DELAY TOLERANT NETWORKS

By

A

PROJECT REPORT

Submitted to the Department of Computer Science & Engineering in the                                                  FACULTY OF ENGINEERING & TECHNOLOGY

In partial fulfillment of the requirements for the award of the degree

Of

MASTER OF TECHNOLOGY

IN

COMPUTER SCIENCE & ENGINEERING

APRIL 2015

BONAFIDE CERTIFICATE

Certified that this project report titled “BEHAVIORAL MALWARE DETECTION IN DELAY TOLERANT NETWORKS” is the bonafide work of Mr. _____________Who carried out the research under my supervision Certified further, that to the best of my knowledge the work reported herein does not form part of any other project report or dissertation on the basis of which a degree or award was conferred on an earlier occasion on this or any other candidate.

Signature of the Guide                                                                             Signature of the H.O.D

Name                                                                                                           Name

CHAPTER 1

1.0 ABSTRACT:

The delay-tolerant-network (DTN) model is becoming a viable communication alternative to the traditional infrastructural model for modern mobile consumer electronics equipped with short-range communication technologies such as Bluetooth, NFC, and Wi-Fi Direct. Proximity malware is a class of malware that exploits the opportunistic contacts and distributed nature of DTNs for propagation. Behavioral characterization of malware is an effective alternative to pattern matching in detecting malware, especially when dealing with polymorphic or obfuscated malware.

 In this paper, we first propose a general behavioral characterization of proximity malware which based on naive Bayesian model, which has been successfully applied in non-DTN settings such as filtering email spams and detecting botnets. We identify two unique challenges for extending Bayesian malware detection to DTNs (“insufficient evidence versus evidence collection risk” and “filtering false evidence sequentially and distributedly”), and propose a simple yet effective method, look ahead, to address the challenges. Furthermore, we propose two extensions to look ahead, dogmatic filtering, and adaptive look ahead, to address the challenge of “malicious nodes sharing false evidence.” Real mobile network traces are used to verify the effectiveness of the proposed methods.

1.1 INTRODUCTION

MALWARE:

Malware, short for malicious software, is any software used to disrupt computer operation, gather sensitive information, or gain access to private computer systems. It can appear in the form of executable code, scripts, active content, and other software. ‘Malware’ is a general term used to refer to a variety of forms of hostile or intrusive software. The term badware is sometimes used, and applied to both true (malicious) malware and unintentionally harmful software, viruses, worms, trojan  horses, ransomware, spyware, adware, scareware and other malicious programs of active malware threats were worms or trojans rather than viruses. In law, malware is sometimes known as a computer contaminant, as in the legal codes of several malware is often disguised as, or embedded in, non-malicious files.

Spyware or other malware is sometimes found embedded in programs supplied officially by companies, e.g., downloadable from websites, that appear useful or attractive, but may have, for example, additional hidden tracking functionality that gathers marketing statistics. An example of such software, which was described as illegitimate, is the Sony rootkit, a Trojan embedded into CDs sold by Sony, which silently installed and concealed itself on purchasers’ computers with the intention of preventing illicit copying; it also reported on users’ listening habits, and created vulnerabilities that were exploited by unrelated malware.

PURPOSES:

Many early infectious programs, including the first Internet Worm, were written as experiments or pranks. Today, malware is used by both black hat hackers and governments, to steal personal, financial, or business information and sometimes for sabotage (e.g., Stuxnet). Malware is sometimes used broadly against government or corporate websites to gather guarded information, or to disrupt their operation in general. However, malware is often used against individuals to gain information such as personal identification numbers or details, bank or credit card numbers, and passwords. Left unguarded, personal and networked computers can be at considerable risk against these threats. (These are most frequently defended against by various types of firewall, anti-virussoftware, and network hardware).

Since the rise of widespread broadband Internet access, malicious software has more frequently been designed for profit. Since 2003, the majority of widespread viruses and worms have been designed to take control of users’ computers for illicit purposes. Infected “zombie computers” are used to send email spam, to host contraband data such as child pornography, or to engage in distributed denial-of-service attacks as a form of extortion.

Programs designed to monitor users’ web browsing, display unsolicited advertisements, or redirect affiliate marketing revenues are called spyware. Spyware programs do not spread like viruses; instead they are generally installed by exploiting security holes. They can also be packaged together with user-installed software, such as peer-to-peer applications.

Ransomware affects an infected computer in some way, and demands payment to reverse the damage. For example, programs such as CryptoLocker encrypt files securely, and only decrypt them on payment of a substantial sum of money.

INFECTIOUS MALWARE: VIRUSES AND WORMS:

The best-known types of malware, viruses and worms, are known for the manner in which they spread, rather than any specific types of behavior. The term computer virus is used for a program that embeds itself in some other executable software (including the operating system itself) on the target system without the users consent and when that is run causes the virus to spread to other executables. On the other hand, a worm is a stand-alone malware program that actively transmits itself over a network to infect other computers. These definitions lead to the observation that a virus requires the user to run an infected program or operating system for the virus to spread, whereas a worm spreads itself.

CONCEALMENT: Viruses, trojan horses, rootkits, and backdoors

TROJAN HORSES

For a malicious program to accomplish its goals, it must be able to run without being detected, shut down, or deleted. When a malicious program is disguised as something normal or desirable, users may unwittingly install it. This is the technique of the Trojan horse or trojan. In broad terms, a Trojan horse is any program that invites the user to run it, concealing harmful or malicious executable code of any description. The code may take effect immediately and can lead to many undesirable effects, such as encrypting the user’s files or downloading and implementing further malicious functionality.[citation needed]

In the case of some spyware, adware, etc. the supplier may require the user to acknowledge or accept its installation, describing its behavior in loose terms that may easily be misunderstood or ignored, with the intention of deceiving the user into installing it without the supplier technically in breach of the law.[citation needed]

ROOTKITS

Once a malicious program is installed on a system, it is essential that it stays concealed, to avoid detection. Software packages known as rootkits allow this concealment, by modifying the host’s operating system so that the malware is hidden from the user. Rootkits can prevent a malicious process from being visible in the system’s list of processes, or keep its files from being read.

Some malicious programs contain routines to defend against removal, not merely to hide them. An early example of this behavior is recorded in the Jargon File tale of a pair of programs infesting a Xerox CP-V time sharing system:

Each ghost-job would detect the fact that the other had been killed, and would start a new copy of the recently-stopped program within a few milliseconds. The only way to kill both ghosts was to kill them simultaneously (very difficult) or to deliberately crash the system.

BACKDOORS

A backdoor is a method of bypassing normal authentication procedures, usually over a connection to a network such as the Internet. Once a system has been compromised, one or more backdoors may be installed in order to allow access in the future,[30] invisibly to the user.

The idea has often been suggested that computer manufacturers preinstall backdoors on their systems to provide technical support for customers, but this has never been reliably verified. It was reported in 2014 that US government agencies had been diverting computers purchased by those considered “targets” to secret workshops where software or hardware permitting remote access by the agency was installed, considered to be among the most productive operations to obtain access to networks around the world. Backdoors may be installed by Trojan horses, worms, implants, or other methods.

Malware authors target bugs, or loopholes, to exploit. A common method is exploitation of vulnerability, where software designed to store data in a specified region of memory does not prevent more data than the buffer can accommodate being supplied. Malware may provide data that overflows the buffer, with malicious executable code or data after the end; when this payload is accessed it does what the attacker, not the legitimate software, determines.

INSECURE DESIGN OR USER ERROR

Early PCs had to be booted from floppy disks; when built-in hard drives became common the operating system was normally started from them, but it was possible to boot from another boot device if available, such as a floppy disk, CD-ROM, DVD-ROM, or USB flash drive. It was common to configure the computer to boot from one of these devices when available. Normally none would be available; the user would intentionally insert, say, a CD into the optical drive to boot the computer in some special way, for example to install an operating system. Even without booting, computers can be configured to execute software on some media as soon as they become available, e.g. to autorun a CD or USB device when inserted.

Malicious software distributors would trick the user into booting or running from an infected device or medium; for example, a virus could make an infected computer add autorunnable code to any USB stick plugged into it; anyone who then attached the stick to another computer set to autorun from USB would in turn become infected, and also pass on the infection in the same way. More generally, any device that plugs into a USB port-—”including gadgets like lights, fans, speakers, toys, even a digital microscope”—can be used to spread malware. Devices can be infected during manufacturing or supply if quality control is inadequate.

This form of infection can largely be avoided by setting up computers by default to boot from the internal hard drive, if available, and not to autorun from devices. Intentional booting from another device is always possible by pressing certain keys during boot.

Older email software would automatically open HTML email containing potentially malicious JavaScript code; users may also execute disguised malicious email attachments and infected executable files supplied in other ways.

1.2 SCOPE OF THE PROJECT:

We present a general behavioral characterization of proximity malware, which captures the functional but imperfect nature in detecting proximity malware characterization, and with a simple cut-off malware containment strategy, we formulate the malware detection process as a distributed decision problem. We analyze the risk associated with the decision, and design a simple, yet effective, strategy, look ahead, which naturally reflects individual nodes’ intrinsic risk inclinations against malware infection. Look ahead extends the naive Bayesian model, and addresses the DTN specific, malware-related, “insufficient evidence versus evidence collection risk” problem.

We consider the benefits of sharing assessments among nodes, and address challenges derived from the DTN model: liars (i.e., bad-mouthing and false praising malicious nodes) and defectors (i.e., good nodes that have turned rogue due to malware infections). We present two alternative techniques, dogmatic filtering and adaptive look ahead, that naturally extend look ahead to consolidate evidence provided by others, while containing the negative effect of false evidence. A nice property of the proposed evidence consolidation methods is that the results will not worsen even if liars are the majority in the neighborhood traces are used to verify the effectiveness of the methods.

CHAPTER 2

2.0 SYSTEM ANALYSIS

2.1 EXISTING SYSTEM:

Existing worms, spam, and phishing exploit gaps in traditional threat models that usually revolve around preventing unauthorized access and information disclosure. The new threat landscape requires security researchers to consider a wider range of attacks: opportunistic attacks in addition to targeted ones; attacks coming not just from malicious users, but also from subverted (yet otherwise benign) hosts; coordinated/distributed attacks in addition to isolated, single-source methods; and attacks blending flaws across layers, rather than exploiting a single vulnerability. Some of the largest security lapses in the last decade are due to designers ignoring the complexity of the threat landscape.

The increasing penetration of wireless networking, and more specifically wifi, may soon reach critical mass, making it necessary to examine whether the current state of wireless security is adequate for fending off likely attacks.

Three types of threats that seem insufficiently addressed by existing technology and deployment techniques. The first threat is wildfire worms, a class of worms that spreads contagiously between hosts on neighboring APs. We show that such worms can spread to a large fraction of hosts in a dense urban setting, and that the propagation speed can be such that most existing defenses cannot react in a timely fashion. Worse, such worms can penetrate through networks protected by WEP and other security mechanisms. The second threat we discuss is large-scale spoofing attacks that can be used for massive phishing and spam campaigns. We show how an attacker can easily use a botnet by acquiring access to wifi-capable zombie hosts, and can use these zombies to target not just the local wireless LAN, but any LAN within range, greatly increasing his reach across heterogeneous networks.

2.2 DISADVANTAGES:

• Viruses can cause many problems on your computer. Usually, they display pop-up ads on your desktop or steal your information. Some of the more nasty ones can even crash your computer or delete your files.

• Your computer gets slowed down. Many “hackers” get jobs with software firms by finding and exploiting problems with software.

• Some the applications won’t start (ex: I hate mozilla virus won’t let you start the mozilla) you cannot see some of the settings in your OS. (Ex one kind of virus disables hide folder options and you will never be able to set it).

To quantify these threats, we rely on real-world data extracted from wifi maps of large metropolitan areas in the country. Existing  results suggest that a carefully crafted wireless worm can infect up to 80% of all wifi connected hosts in some metropolitan areas within 20 minutes, and that an attacker can launch phishing attacks or build a tracking system to monitor the location of 10-50% of wireless users in these metropolitan areas with just 1,000 zombies under his control.

2.3 PROPOSED SYSTEM:

In this paper, we present a simple, yet effective solution, look ahead, which naturally reflects individual nodes’ intrinsic risk inclinations against malware infection, to balance between these two extremes. Essentially, we extend the naive Bayesian model, which has been applied in filtering email, spams detecting botnets, and designing IDSs.

We analyze the risk associated with the decision, and design a simple, yet effective, strategy, look ahead, which naturally reflects individual nodes’ intrinsic risk inclinations against malware infection. Look ahead extends the naive Bayesian model, and addresses the DTN specific, malware-related, “insufficient evidence versus evidence collection risk” Proximity malware is a malicious program that disrupts the host node’s normal function and has a chance of duplicating itself to other nodes during (opportunistic) contact opportunities between nodes in the DTN.

We consider the benefits of sharing assessments among nodes, and address challenges derived from the DTN model: liars (i.e., bad-mouthing and false praising malicious nodes) and defectors (i.e., good nodes that have turned rogue due to malware infections). We present two alternative techniques, dogmatic filtering and adaptive look ahead, that naturally extend look ahead to consolidate evidence provided by others, while containing the negative effect of false evidence. A nice property of the proposed evidence consolidation methods is that the results will not worsen even if liars are the majority in the neighborhood traces are used to verify the effectiveness of the methods.

2.4 ADVANTAGES:

Two DTN specific, malware-related:

1. Insufficient evidence versus evidence collection risk. In DTNs, evidence (such as Bluetooth connection or SSH session requests) is collected only when nodes come into contact. But contacting malware-infected nodes carries the risk of being infected. Thus, nodes must make decisions (such as whether to cut off other nodes and, if yes, when) online based on potentially insufficient evidence.

2. Filtering false evidence sequentially and distributedly. Sharing evidence among opportunistic acquaintances helps alleviating the aforementioned insufficient evidence problem; however, false evidence shared by malicious nodes (the liars) may negate the benefits of sharing. In DTNs, nodes must decide whether to accept received evidence sequentially and distributedly.

2.5 HARDWARE & SOFTWARE REQUIREMENTS:

HARDWARE REQUIREMENT:

v    Processor                                 –    Pentium –IV

• Speed                                      –    1.1 GHz
  • RAM                                       –    256 MB (min)
  • Hard Disk                               –   20 GB
  • Floppy Drive                           –    1.44 MB
  • Key Board                              –    Standard Windows Keyboard
  • Mouse                                     –    Two or Three Button Mouse
  • Monitor                                   –    SVGA

 

SOFTWARE REQUIREMENTS:

• Operating System                   :           Windows XP
• Front End                                :           JAVA JDK 1.7
• Document                               :           MS-Office 2007

CHAPTER 3

3.0 SYSTEM DESIGN

ARCHITECTURE DIAGRAM / DATA FLOW DIAGRAM / UML DIAGRAM:

• The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of the input data to the system, various processing carried out on these data, and the output data is generated by the system

• The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. These components are the system process, the data used by the process, an external entity that interacts with the system and the information flows in the system.

• DFD shows how the information moves through the system and how it is modified by a series of transformations. It is a graphical technique that depicts information flow and the transformations that are applied as data moves from input to output.

• DFD is also known as bubble chart. A DFD may be used to represent a system at any level of abstraction. DFD may be partitioned into levels that represent increasing information flow and functional detail.

NOTATION:

SOURCE OR DESTINATION OF DATA:

External sources or destinations, which may be people or organizations or other entities

DATA SOURCE:

Here the data referenced by a process is stored and retrieved.

 

PROCESS:

People, procedures or devices that produce data. The physical component is not identified.

DATA FLOW:

Data moves in a specific direction from an origin to a destination. The data flow is a “packet” of data.

MODELING RULES:

There are several common modeling rules when creating DFDs:

1. All processes must have at least one data flow in and one data flow out.
2. All processes should modify the incoming data, producing new forms of outgoing data.
3. Each data store must be involved with at least one data flow.
4. Each external entity must be involved with at least one data flow.
5. A data flow must be attached to at least one process.

3.0 ARCHITECTURE DIAGRAM

DATAFLOW DIAGRAM:

LEVEL 1

        Server                                                                                                             Client

LEVEL 2

 

UML DIAGRAMS:                                                     

USE CASE DIAGRAM:

CLASS DIAGRAM:

SEQUENCE DIAGRAM:

ACTIVITY DIAGRAMS:

CHAPTER 4

4.0 IMPLEMENTATION

4.1 MODULES:

DELAY TOLERANT NETWORKS:

PROXIMITY MALWARE:

MALWARE PROPAGATION:

BAYESIAN FILTERING:

4.1 MODULE DESCRIPTION:

DELAY TOLERANT NETWORKS:

Delay-tolerant networking (DTN) is an approach to computer network architecture that seeks to address the technical issues in heterogeneous networks that may lack continuous network connectivity. Examples of such networks are those operating in mobile or extreme terrestrial environments, or planned networks in space.

Recently, the term disruption-tolerant networking has gained currency in the United States due to support from DARPA, which has funded many DTN projects. Disruption may occur because of the limits of wireless radio range, sparsity of mobile nodes, energy resources, attack, and noise.

DTNs, including a growing number of academic conferences on delay and disruption-tolerant networking, and growing interest in combining work from sensor networks and MANETs with the work on DTN. This field saw many optimizations on classic ad hoc and delay-tolerant networking algorithms and began to examine factors such as security, reliability, verifiability, and other areas of research that are well understood in traditional computer networking.

PROXIMITY MALWARE:

Proximity malware is a malicious program that disrupts the host node’s normal function and has a chance of duplicating itself to other nodes during (opportunistic) contact opportunities between nodes in the DTN. When duplication occurs, the other node is infected with the malware. In our model, we assume that each node is capable of assessing the other party for suspicious actions after each encounter, resulting in a binary assessment. For example, a node can assess a Bluetooth connection or an SSH session for potential Cabir or Ikee infection.

The watchdog components in previous works on malicious behavior detection in MANETs and distributed reputation systems are other examples. A node is either evil or good, based on if it is or is not infected by the malware. The suspiciousaction assessment is assumed to be an imperfect but functional indicator of malware infections: It may occasionally assess an evil node’s actions as “nonsuspicious” or a good node’s actions as “suspicious,” but most suspicious actions are correctly attributed to evil nodes. A previous work on distributed IDS presents an example for such imperfect but functional binary classifier on nodes’ behaviors.

MALWARE PROPAGATION:

We analyzed malware propagation through proximity channels in social networks. Akritidis et al. quantified the threat of proximity malware in wide-area wireless networks in optimal malware signature distribution in heterogeneous, resource-constrained mobile networks in traditional, non-DTN, networks and Bayer et al.

We proposed to detect malware with learned behavioral model, in terms of system call and program flow. We extend the Naive Bayesian model, which has been applied in filtering email spams detecting botnets and designing IDSs and address DTN-specific, malware-related, problems. In the context of detecting slowly propagating Internet worm, Dash et al. presented a distributed IDS architecture of local/global detector that resembles the neighborhood-watch model, with the assumption of attested/honest evidence, i.e., without liars.

BAYESIAN FILTERING:

Naive Bayes classifiers can be trained very efficiently in a supervised learning setting. In many practical applications, parameter estimation for naive Bayes models uses the method of maximum likelihood; in other words, one can work with the naive Bayes model without accepting Bayesian probability or using any Bayesian methods.

The implications are as follows:

. Given enough assessments, honest nodes are likely to obtain a close estimation of a node’s suspiciousness (suppose they have not cut the node off yet), even if they only use their own assessments.

. The liars have to share a significant amount of false evidence to sway the public’s opinion on a node’s suspiciousness.

. The most susceptible victims of liars are the nodes that have little evidence Dogmatic filtering. Dogmatic filtering is based on the observation that one’s own assessments are truthful and, therefore, can be used to bootstrap the evidence consolidation process. A node shall only accept evidence that will not sway its current opinion too much. We call this observation the dogmatic principle.

We extend the Naive Bayesian model, which has been applied in filtering email, spams detecting botnets and designing IDSs and address DTN-specific, malware-related, problems. In the context of detecting slowly propagating Internet worm, Dash et al. presented a distributed IDS architecture of local/global detector that resembles the neighborhood-watch model,

CHAPTER 5

5.0 SYSTEM STUDY:

5.1 FEASIBILITY STUDY:

The feasibility of the project is analyzed in this phase and business proposal is put forth with a very general plan for the project and some cost estimates. During system analysis the feasibility study of the proposed system is to be carried out. This is to ensure that the proposed system is not a burden to the company.  For feasibility analysis, some understanding of the major requirements for the system is essential.

Three key considerations involved in the feasibility analysis are      

• ECONOMICAL FEASIBILITY
• TECHNICAL FEASIBILITY
• SOCIAL FEASIBILITY

5.1.1 ECONOMICAL FEASIBILITY:                  

This study is carried out to check the economic impact that the system will have on the organization. The amount of fund that the company can pour into the research and development of the system is limited. The expenditures must be justified. Thus the developed system as well within the budget and this was achieved because most of the technologies used are freely available. Only the customized products had to be purchased.

5.1.2 TECHNICAL FEASIBILITY:

This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.  

5.1.3 SOCIAL FEASIBILITY:  

The aspect of study is to check the level of acceptance of the system by the user. This includes the process of training the user to use the system efficiently. The user must not feel threatened by the system, instead must accept it as a necessity. The level of acceptance by the users solely depends on the methods that are employed to educate the user about the system and to make him familiar with it. His level of confidence must be raised so that he is also able to make some constructive criticism, which is welcomed, as he is the final user of the system.

5.2 SYSTEM TESTING:

Testing is a process of checking whether the developed system is working according to the original objectives and requirements. It is a set of activities that can be planned in advance and conducted systematically. Testing is vital to the success of the system. System testing makes a logical assumption that if all the parts of the system are correct, the global will be successfully achieved. In adequate testing if not testing leads to errors that may not appear even many months. This creates two problems, the time lag between the cause and the appearance of the problem and the effect of the system errors on the files and records within the system. A small system error can conceivably explode into a much larger Problem. Effective testing early in the purpose translates directly into long term cost savings from a reduced number of errors. Another reason for system testing is its utility, as a user-oriented vehicle before implementation. The best programs are worthless if it produces the correct outputs.

5.2.1 UNIT TESTING:

A program represents the logical elements of a system. For a program to run satisfactorily, it must compile and test data correctly and tie in properly with other programs. Achieving an error free program is the responsibility of the programmer. Program  testing  checks  for  two  types  of  errors:  syntax  and  logical. Syntax error is a program statement that violates one or more rules of the language in which it is written. An improperly defined field dimension or omitted keywords are common syntax errors. These errors are shown through error message generated by the computer. For Logic errors the programmer must examine the output carefully.

UNIT TESTING:

Description	Expected result
Test for application window properties.	All the properties of the windows are to be properly aligned and displayed.
Test for mouse operations.	All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions.

5.1.3 FUNCTIONAL TESTING:

Functional testing of an application is used to prove the application delivers correct results, using enough inputs to give an adequate level of confidence that will work correctly for all sets of inputs. The functional testing will need to prove that the application works for each client type and that personalization function work correctly.When a program is tested, the actual output is compared with the expected output. When there is a discrepancy the sequence of instructions must be traced to determine the problem.  The process is facilitated by breaking the program into self-contained portions, each of which can be checked at certain key points. The idea is to compare program values against desk-calculated values to isolate the problems.

FUNCTIONAL TESTING:

Description	Expected result
Test for all modules.	All peers should communicate in the group.
Test for various peer in a distributed network framework as it display all users available in the group.	The result after execution should give the accurate result.

5.1. 4 NON-FUNCTIONAL TESTING:

 The Non Functional software testing encompasses a rich spectrum of testing strategies, describing the expected results for every test case. It uses symbolic analysis techniques. This testing used to check that an application will work in the operational environment. Non-functional testing includes:

• Load testing
• Performance testing
• Usability testing
• Reliability testing
• Security testing

5.1.5 LOAD TESTING:

An important tool for implementing system tests is a Load generator. A Load generator is essential for testing quality requirements such as performance and stress. A load can be a real load, that is, the system can be put under test to real usage by having actual telephone users connected to it. They will generate test input data for system test.

Load Testing

Description	Expected result
It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received.	Should designate another active node as a Server.

5.1.5 PERFORMANCE TESTING:

Performance tests are utilized in order to determine the widely defined performance of the software system such as execution time associated with various parts of the code, response time and device utilization. The intent of this testing is to identify weak points of the software system and quantify its shortcomings.

PERFORMANCE TESTING:

Description	Expected result
This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management.	Should handle large input values, and produce accurate result in a  expected time.

5.1.6 RELIABILITY TESTING:

The software reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time and it is being ensured in this testing. Reliability can be expressed as the ability of the software to reveal defects under testing conditions, according to the specified requirements. It the portability that a software system will operate without failure under given conditions for a given time interval and it focuses on the behavior of the software element. It forms a part of the software quality control team.

RELIABILITY TESTING:

Description	Expected result
This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application.	In case of failure of  the server an alternate server should take over the job.

5.1.7 SECURITY TESTING:

Security testing evaluates system characteristics that relate to the availability, integrity and confidentiality of the system data and services. Users/Clients should be encouraged to make sure their security needs are very clearly known at requirements time, so that the security issues can be addressed by the designers and testers.

SECURITY TESTING:

Description	Expected result
Checking that the user identification is authenticated.	In case failure it should not be connected in the framework.
Check whether group keys in a tree are shared by all peers.	The peers should know group key in the same group.

5.1.7 WHITE BOX TESTING:

White  box  testing,  sometimes called  glass-box  testing is  a test  case  design method  that  uses  the  control  structure  of the procedural  design  to  derive  test  cases. Using  white  box  testing  method,  the software  engineer  can  derive  test  cases. The White box testing focuses on the inner structure of the software structure to be tested.

5.1.8 WHITE BOX TESTING:

Description	Expected result
Exercise all logical decisions on their true and false sides.	All the logical decisions must be valid.
Execute all loops at their boundaries and within their operational bounds.	All the loops must be finite.
Exercise internal data structures to ensure their validity.	All the data structures must be valid.

5.1.9 BLACK BOX TESTING:

Black box testing, also called behavioral testing, focuses on the functional requirements of the software.  That  is,  black  testing  enables  the software engineer  to  derive  sets  of  input  conditions  that  will  fully  exercise  all  functional requirements  for  a  program.  Black box testing is not alternative to white box techniques.  Rather  it  is  a  complementary  approach  that  is  likely  to  uncover  a different  class  of  errors  than  white box  methods. Black box testing attempts to find errors which focuses on inputs, outputs, and principle function of a software module. The starting point of the black box testing is either a specification or code. The contents of the box are hidden and the stimulated software should produce the desired results.

5.1.10 BLACK BOX TESTING:

Description	Expected result
To check for incorrect or missing functions.	All the functions must be valid.
To check for interface errors.	The entire interface must function normally.
To check for errors in a data structures or external data base access.	The database updation and retrieval must be done.
To check for initialization and termination errors.	All the functions and data structures must be initialized properly and terminated normally.

All the above system testing strategies are carried out in as the development, documentation and institutionalization of the proposed goals and related policies is essential.

CHAPTER 6

7.0 SOFTWARE DESCRIPTION:

 

7.1 JAVA TECHNOLOGY:

Java technology is both a programming language and a platform.

 

The Java Programming Language

 

The Java programming language is a high-level language that can be characterized by all of the following buzzwords:

• Simple
  • Architecture neutral
  • Object oriented
  • Portable
  • Distributed     
  • High performance
  • Interpreted     
  • Multithreaded
  • Robust
  • Dynamic
  • Secure     

With most programming languages, you either compile or interpret a program so that you can run it on your computer. The Java programming language is unusual in that a program is both compiled and interpreted. With the compiler, first you translate a program into an intermediate language called Java byte codes —the platform-independent codes interpreted by the interpreter on the Java platform. The interpreter parses and runs each Java byte code instruction on the computer. Compilation happens just once; interpretation occurs each time the program is executed. The following figure illustrates how this works.

You can think of Java byte codes as the machine code instructions for the Java Virtual Machine (Java VM). Every Java interpreter, whether it’s a development tool or a Web browser that can run applets, is an implementation of the Java VM. Java byte codes help make “write once, run anywhere” possible. You can compile your program into byte codes on any platform that has a Java compiler. The byte codes can then be run on any implementation of the Java VM. That means that as long as a computer has a Java VM, the same program written in the Java programming language can run on Windows 2000, a Solaris workstation, or on an iMac.

7.2 THE JAVA PLATFORM:

A platform is the hardware or software environment in which a program runs. We’ve already mentioned some of the most popular platforms like Windows 2000, Linux, Solaris, and MacOS. Most platforms can be described as a combination of the operating system and hardware. The Java platform differs from most other platforms in that it’s a software-only platform that runs on top of other hardware-based platforms.

The Java platform has two components:

• The Java Virtual Machine (Java VM)
• The Java Application Programming Interface (Java API)

You’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported onto various hardware-based platforms.

The Java API is a large collection of ready-made software components that provide many useful capabilities, such as graphical user interface (GUI) widgets. The Java API is grouped into libraries of related classes and interfaces; these libraries are known as packages. The next section, What Can Java Technology Do? Highlights what functionality some of the packages in the Java API provide.

The following figure depicts a program that’s running on the Java platform. As the figure shows, the Java API and the virtual machine insulate the program from the hardware.

Native code is code that after you compile it, the compiled code runs on a specific hardware platform. As a platform-independent environment, the Java platform can be a bit slower than native code. However, smart compilers, well-tuned interpreters, and just-in-time byte code compilers can bring performance close to that of native code without threatening portability.

7.3 WHAT CAN JAVA TECHNOLOGY DO?

The most common types of programs written in the Java programming language are applets and applications. If you’ve surfed the Web, you’re probably already familiar with applets. An applet is a program that adheres to certain conventions that allow it to run within a Java-enabled browser.

However, the Java programming language is not just for writing cute, entertaining applets for the Web. The general-purpose, high-level Java programming language is also a powerful software platform. Using the generous API, you can write many types of programs.

An application is a standalone program that runs directly on the Java platform. A special kind of application known as a server serves and supports clients on a network. Examples of servers are Web servers, proxy servers, mail servers, and print servers. Another specialized program is a servlet.

A servlet can almost be thought of as an applet that runs on the server side. Java Servlets are a popular choice for building interactive web applications, replacing the use of CGI scripts. Servlets are similar to applets in that they are runtime extensions of applications. Instead of working in browsers, though, servlets run within Java Web servers, configuring or tailoring the server.

How does the API support all these kinds of programs? It does so with packages of software components that provides a wide range of functionality. Every full implementation of the Java platform gives you the following features:

• The essentials: Objects, strings, threads, numbers, input and output, data structures, system properties, date and time, and so on.
• Applets: The set of conventions used by applets.
• Networking: URLs, TCP (Transmission Control Protocol), UDP (User Data gram Protocol) sockets, and IP (Internet Protocol) addresses.
• Internationalization: Help for writing programs that can be localized for users worldwide. Programs can automatically adapt to specific locales and be displayed in the appropriate language.
• Security: Both low level and high level, including electronic signatures, public and private key management, access control, and certificates.
• Software components: Known as JavaBeans^TM, can plug into existing component architectures.
• Object serialization: Allows lightweight persistence and communication via Remote Method Invocation (RMI).
• Java Database Connectivity (JDBC^TM): Provides uniform access to a wide range of relational databases.

The Java platform also has APIs for 2D and 3D graphics, accessibility, servers, collaboration, telephony, speech, animation, and more. The following figure depicts what is included in the Java 2 SDK.

 

7.4 HOW WILL JAVA TECHNOLOGY CHANGE MY LIFE?

We can’t promise you fame, fortune, or even a job if you learn the Java programming language. Still, it is likely to make your programs better and requires less effort than other languages. We believe that Java technology will help you do the following:

• Get started quickly: Although the Java programming language is a powerful object-oriented language, it’s easy to learn, especially for programmers already familiar with C or C++.
• Write less code: Comparisons of program metrics (class counts, method counts, and so on) suggest that a program written in the Java programming language can be four times smaller than the same program in C++.
• Write better code: The Java programming language encourages good coding practices, and its garbage collection helps you avoid memory leaks. Its object orientation, its JavaBeans component architecture, and its wide-ranging, easily extendible API let you reuse other people’s tested code and introduce fewer bugs.
• Develop programs more quickly: Your development time may be as much as twice as fast versus writing the same program in C++. Why? You write fewer lines of code and it is a simpler programming language than C++.
• Avoid platform dependencies with 100% Pure Java: You can keep your program portable by avoiding the use of libraries written in other languages. The 100% Pure Java^TM Product Certification Program has a repository of historical process manuals, white papers, brochures, and similar materials online.
• Write once, run anywhere: Because 100% Pure Java programs are compiled into machine-independent byte codes, they run consistently on any Java platform.
• Distribute software more easily: You can upgrade applets easily from a central server. Applets take advantage of the feature of allowing new classes to be loaded “on the fly,” without recompiling the entire program.

 

7.5 ODBC:

 

Microsoft Open Database Connectivity (ODBC) is a standard programming interface for application developers and database systems providers. Before ODBC became a de facto standard for Windows programs to interface with database systems, programmers had to use proprietary languages for each database they wanted to connect to. Now, ODBC has made the choice of the database system almost irrelevant from a coding perspective, which is as it should be. Application developers have much more important things to worry about than the syntax that is needed to port their program from one database to another when business needs suddenly change.

Through the ODBC Administrator in Control Panel, you can specify the particular database that is associated with a data source that an ODBC application program is written to use. Think of an ODBC data source as a door with a name on it. Each door will lead you to a particular database. For example, the data source named Sales Figures might be a SQL Server database, whereas the Accounts Payable data source could refer to an Access database. The physical database referred to by a data source can reside anywhere on the LAN.

The ODBC system files are not installed on your system by Windows 95. Rather, they are installed when you setup a separate database application, such as SQL Server Client or Visual Basic 4.0. When the ODBC icon is installed in Control Panel, it uses a file called ODBCINST.DLL. It is also possible to administer your ODBC data sources through a stand-alone program called ODBCADM.EXE. There is a 16-bit and a 32-bit version of this program and each maintains a separate list of ODBC data sources.

From a programming perspective, the beauty of ODBC is that the application can be written to use the same set of function calls to interface with any data source, regardless of the database vendor. The source code of the application doesn’t change whether it talks to Oracle or SQL Server. We only mention these two as an example. There are ODBC drivers available for several dozen popular database systems. Even Excel spreadsheets and plain text files can be turned into data sources. The operating system uses the Registry information written by ODBC Administrator to determine which low-level ODBC drivers are needed to talk to the data source (such as the interface to Oracle or SQL Server). The loading of the ODBC drivers is transparent to the ODBC application program. In a client/server environment, the ODBC API even handles many of the network issues for the application programmer.

The advantages of this scheme are so numerous that you are probably thinking there must be some catch. The only disadvantage of ODBC is that it isn’t as efficient as talking directly to the native database interface. ODBC has had many detractors make the charge that it is too slow. Microsoft has always claimed that the critical factor in performance is the quality of the driver software that is used. In our humble opinion, this is true. The availability of good ODBC drivers has improved a great deal recently. And anyway, the criticism about performance is somewhat analogous to those who said that compilers would never match the speed of pure assembly language. Maybe not, but the compiler (or ODBC) gives you the opportunity to write cleaner programs, which means you finish sooner. Meanwhile, computers get faster every year.

7.6 JDBC:

In an effort to set an independent database standard API for Java; Sun Microsystems developed Java Database Connectivity, or JDBC. JDBC offers a generic SQL database access mechanism that provides a consistent interface to a variety of RDBMSs. This consistent interface is achieved through the use of “plug-in” database connectivity modules, or drivers. If a database vendor wishes to have JDBC support, he or she must provide the driver for each platform that the database and Java run on.

To gain a wider acceptance of JDBC, Sun based JDBC’s framework on ODBC. As you discovered earlier in this chapter, ODBC has widespread support on a variety of platforms. Basing JDBC on ODBC will allow vendors to bring JDBC drivers to market much faster than developing a completely new connectivity solution.

JDBC was announced in March of 1996. It was released for a 90 day public review that ended June 8, 1996. Because of user input, the final JDBC v1.0 specification was released soon after.

The remainder of this section will cover enough information about JDBC for you to know what it is about and how to use it effectively. This is by no means a complete overview of JDBC. That would fill an entire book.

 

7.7 JDBC Goals:

Few software packages are designed without goals in mind. JDBC is one that, because of its many goals, drove the development of the API. These goals, in conjunction with early reviewer feedback, have finalized the JDBC class library into a solid framework for building database applications in Java.

The goals that were set for JDBC are important. They will give you some insight as to why certain classes and functionalities behave the way they do. The eight design goals for JDBC are as follows:

SQL Level API

The designers felt that their main goal was to define a SQL interface for Java. Although not the lowest database interface level possible, it is at a low enough level for higher-level tools and APIs to be created. Conversely, it is at a high enough level for application programmers to use it confidently. Attaining this goal allows for future tool vendors to “generate” JDBC code and to hide many of JDBC’s complexities from the end user.

SQL Conformance

SQL syntax varies as you move from database vendor to database vendor. In an effort to support a wide variety of vendors, JDBC will allow any query statement to be passed through it to the underlying database driver. This allows the connectivity module to handle non-standard functionality in a manner that is suitable for its users.

JDBC must be implemental on top of common database interfaces

The JDBC SQL API must “sit” on top of other common SQL level APIs. This goal allows JDBC to use existing ODBC level drivers by the use of a software interface. This interface would translate JDBC calls to ODBC and vice versa.

1. Provide a Java interface that is consistent with the rest of the Java system

Because of Java’s acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system.

• Keep it simple

This goal probably appears in all software design goal listings. JDBC is no exception. Sun felt that the design of JDBC should be very simple, allowing for only one method of completing a task per mechanism. Allowing duplicate functionality only serves to confuse the users of the API.

• Use strong, static typing wherever possible

Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.

• Keep the common cases simple

Because more often than not, the usual SQL calls used by the programmer are simple SELECT’s, INSERT’s, DELETE’s and UPDATE’s, these queries should be simple to perform with JDBC. However, more complex SQL statements should also be possible.

Finally we decided to precede the implementation using Java Networking.

And for dynamically updating the cache table we go for MS Access database.

Java ha two things: a programming language and a platform.

Java is a high-level programming language that is all of the following

Simple                                     Architecture-neutral

Object-oriented                       Portable

Distributed                              High-performance

Interpreted                              Multithreaded

Robust                                     Dynamic Secure

Java is also unusual in that each Java program is both compiled and interpreted. With a compile you translate a Java program into an intermediate language called Java byte codes the platform-independent code instruction is passed and run on the computer.

Compilation happens just once; interpretation occurs each time the program is executed. The figure illustrates how this works.

7.7 NETWORKING TCP/IP STACK:

The TCP/IP stack is shorter than the OSI one:

TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.

IP datagram’s:

The IP layer provides a connectionless and unreliable delivery system. It considers each datagram independently of the others. Any association between datagram must be supplied by the higher layers. The IP layer supplies a checksum that includes its own header. The header includes the source and destination addresses. The IP layer handles routing through an Internet. It is also responsible for breaking up large datagram into smaller ones for transmission and reassembling them at the other end.

UDP:

UDP is also connectionless and unreliable. What it adds to IP is a checksum for the contents of the datagram and port numbers. These are used to give a client/server model – see later.

TCP:

TCP supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.

Internet addresses

In order to use a service, you must be able to find it. The Internet uses an address scheme for machines so that they can be located. The address is a 32 bit integer which gives the IP address.

Network address:

Class A uses 8 bits for the network address with 24 bits left over for other addressing. Class B uses 16 bit network addressing. Class C uses 24 bit network addressing and class D uses all 32.

Subnet address:

Internally, the UNIX network is divided into sub networks. Building 11 is currently on one sub network and uses 10-bit addressing, allowing 1024 different hosts.

Host address:

8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.

Total address:

The 32 bit address is usually written as 4 integers separated by dots.

Port addresses

A service exists on a host, and is identified by its port. This is a 16 bit number. To send a message to a server, you send it to the port for that service of the host that it is running on. This is not location transparency! Certain of these ports are “well known”.

Sockets:

A socket is a data structure maintained by the system to handle network connections. A socket is created using the call socket. It returns an integer that is like a file descriptor. In fact, under Windows, this handle can be used with Read File and Write File functions.

#include <sys/types.h>

#include <sys/socket.h>

int socket(int family, int type, int protocol);

Here “family” will be AF_INET for IP communications, protocol will be zero, and type will depend on whether TCP or UDP is used. Two processes wishing to communicate over a network create a socket each. These are similar to two ends of a pipe – but the actual pipe does not yet exist.

7.8 JFREE CHART:

JFreeChart is a free 100% Java chart library that makes it easy for developers to display professional quality charts in their applications. JFreeChart’s extensive feature set includes:

A consistent and well-documented API, supporting a wide range of chart types;

A flexible design that is easy to extend, and targets both server-side and client-side applications;

Support for many output types, including Swing components, image files (including PNG and JPEG), and vector graphics file formats (including PDF, EPS and SVG);

JFreeChart is “open source” or, more specifically, free software. It is distributed under the terms of the GNU Lesser General Public Licence (LGPL), which permits use in proprietary applications.

 

7.8.1. Map Visualizations:

Charts showing values that relate to geographical areas. Some examples include: (a) population density in each state of the United States, (b) income per capita for each country in Europe, (c) life expectancy in each country of the world. The tasks in this project include: Sourcing freely redistributable vector outlines for the countries of the world, states/provinces in particular countries (USA in particular, but also other areas);

Creating an appropriate dataset interface (plus default implementation), a rendered, and integrating this with the existing XYPlot class in JFreeChart; Testing, documenting, testing some more, documenting some more.

7.8.2. Time Series Chart Interactivity

Implement a new (to JFreeChart) feature for interactive time series charts — to display a separate control that shows a small version of ALL the time series data, with a sliding “view” rectangle that allows you to select the subset of the time series data to display in the main chart.

7.8.3. Dashboards

There is currently a lot of interest in dashboard displays. Create a flexible dashboard mechanism that supports a subset of JFreeChart chart types (dials, pies, thermometers, bars, and lines/time series) that can be delivered easily via both Java Web Start and an applet.

 

7.8.4. Property Editors

The property editor mechanism in JFreeChart only handles a small subset of the properties that can be set for charts. Extend (or reimplement) this mechanism to provide greater end-user control over the appearance of the charts.

CHAPTER 7

APPENDIX

7.1 SAMPLE SOURCE CODE

7.2 SAMPLE OUTPUT

CHAPTER 8

8.1 CONCLUSION

Behavioral characterization of malware is an effective alternative to pattern matching in detecting malware, especially when dealing with polymorphic or obfuscated malware. Naive Bayesian model has been successfully applied in non-DTN settings, such as filtering email spams and detecting botnets.

We propose a general behavioral characterization of DTN-based proximity malware. We present look ahead, along with dogmatic filtering and adaptive look ahead, to address two unique challenging in extending Bayesian filtering to DTNs: “insufficient evidence versus evidence collection risk” and “filtering false evidence sequentially and distributedly.” In prospect, extension of the behavioral characterization of proximity malware to account for strategic malware detection evasion with game theory is a challenging yet interesting future work.

CHAPTER 9

9.0 REFERENCES:

[1] C. Kolbitsch, P. Comparetti, C. Kruegel, E. Kirda, X. Zhou, and X. Wang, “Effective and Efficient Malware Detection at the End Host,” Proc. 18th Conf. USENIX Security Symp., 2009.

[2] U. Bayer, P. Comparetti, C. Hlauschek, C. Kruegel, and E. Kirda, “Scalable, Behavior-Based Malware Clustering,” Proc. 16th Ann. Network and Distributed System Security Symp. (NDSS), 2009.

[3] G. Zyba, G. Voelker, M. Liljenstam, A. Me´hes, and P. Johansson, “Defending Mobile Phones from Proximity Malware,” Proc. IEEE INFOCOM, 2009.

[4] F. Li, Y. Yang, and J. Wu, “CPMC: An Efficient Proximity Malware Coping Scheme in Smartphone-Based Mobile Networks,” Proc. IEEE INFOCOM, 2010.

[6] J. Zdziarski, Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification. No Starch Press, 2005.

[7] R. Villamarı´n-Salomo´n and J. Brustoloni, “Bayesian Bot Detection Based on DNS Traffic Similarity,” Proc. ACMymp. Applied Computing (SAC), 2013.

AUTOMATIC SCALING OF INTERNET APPLICATIONS FOR CLOUD

COMPUTING SERVICES

By

A

PROJECT REPORT

Submitted to the Department of Computer Science & Engineering in the                                                  FACULTY OF ENGINEERING & TECHNOLOGY

In partial fulfillment of the requirements for the award of the degree

Of

MASTER OF TECHNOLOGY

IN

COMPUTER SCIENCE & ENGINEERING

APRIL 2015

CERTIFICATE

Certified that this project report titled “Automatic Scaling of Internet Applications for Cloud Computing Services” is the bonafide work of Mr. _____________Who carried out the research under my supervision Certified further, that to the best of my knowledge the work reported herein does not form part of any other project report or dissertation on the basis of which a degree or award was conferred on an earlier occasion on this or any other candidate.

Signature of the Guide                                                                             Signature of the H.O.D

Name                                                                                                           Name

DECLARATION

I hereby declare that the project work entitled “Automatic Scaling of Internet Applications for Cloud Computing Services” Submitted to BHARATHIDASAN UNIVERSITY in partial fulfillment of the requirement for the award of the Degree of MASTER OF SCIENCE IN COMPUTER SCIENCE is a record of original work done by me the guidance of  Prof.A.Vinayagam M.Sc., M.Phil., M.E., to the best of my knowledge, the work reported here is not a part of any other thesis or work on the basis of which a degree or award was conferred on an earlier occasion to me or any other candidate.

                                                                                                        (Student Name)

                                                                                                             (Reg.No)

Place:

Date:

ACKNOWLEDGEMENT

I am extremely glad to present my project “Automatic Scaling of Internet Applications for Cloud Computing Services” which is a part of my curriculum of third semester Master of Science in Computer science. I take this opportunity to express my sincere gratitude to those who helped me in bringing out this project work.

I would like to express my Director, Dr. K. ANANDAN, M.A.(Eco.), M.Ed.,  M.Phil.,(Edn.), PGDCA., CGT., M.A.(Psy.) of who had given me an opportunity to undertake this project.

I am highly indebted to Co-Ordinator Prof. Muniappan Department of Physics and thank from my deep heart for her valuable comments I received through my project.

I wish to express my deep sense of gratitude to my guide                                                  
Prof. A.Vinayagam M.Sc., M.Phil., M.E., for her immense help and encouragement for successful completion of this project.

I also express my sincere thanks to the all the staff members of Computer science for their kind advice.

And last, but not the least, I express my deep gratitude to my parents and friends for their encouragement and support throughout the project.

CHAPTER 1

1.1 ABSTRACT:

Many Internet applications can benefit from an automatic scaling property where their resource usage can be scaled up and down automatically by the cloud service provider. We present a system that provides automatic scaling for Internet applications in the cloud environment. We encapsulate each application instance inside a virtual machine (VM) and use virtualization technology to provide fault isolation. We model it as the Class Constrained Bin Packing (CCBP) problem where each server is a bin and each class represents an application. The class constraint reflects the practical limit on the number of applications a server can run simultaneously.

We develop an efficient semi-online color set algorithm that achieves good demand satisfaction ratio and saves energy by reducing the number of servers used when the load is low. Experiment results demonstrate that our system can improve the throughput an open source implementation of restore the normal QoS five times as fast during flash crowds. Large scale simulations demonstrate that our algorithm is extremely scalable applications. This is an order of magnitude improvement over traditional application placement algorithms in enterprise environments.

1.2 INTRODUCTION

One of the often cited benefits of cloud computing service is the resource elasticity: a business customer can scale up and down its resource usage as needed without upfront capital investment or long term commitment. The Amazon EC2 service, for example, allows users to buy as many virtual machine (VM) instances as they want and operate them much like physical hardware. However, the users still need to decide how much resources are necessary and forhow long. We believe many Internet applications can benefit from an auto scaling property where their resource usage can be scaled up and down automatically by the cloud service provider. A user only needs to upload the application onto a single server in the cloud, and the cloud service will replicate the application onto more or fewer servers as its demand comes and goes. The users are charged only for what they actually use—the so-called “pay as you go” model.

The typical architecture of data center servers for Internet applications it consists of a load balancing switch, a set of application servers, and a set of backend storage servers. The front end switch is typically a Layer 7 switch which parses application level information in Web requests and forwards them to the servers with the corresponding applications running. The switch sometimes runs in a redundant pair for fault tolerance. Each application can run on multiple server machines and the set of their running instances are often managed by some clustering software such as Web Logic.

Each server machine can host multiple applications. The applications store their state information in the backend storage servers. It is important that the applications themselves are stateless so that they can be replicated safely. The storage servers may also become overloaded, but the focus of this work is on the application tier. The Google AppEngine service, for example, requires that the applications be structured in such a two tier architecture and uses the BigTable as its scalable storage solution. A detailed comparison with AppEngine is deferred to Section 7 so that sufficient background can be established. Some distributed data processing applications cannot be mapped into such a tiered architecture easily and thus are not the target of this work. We believe our architecture is representative of a large set of Internet services hosted in the cloud computing environment.

Even though the cloud computing model is sometimes advocated as providing infinite capacity on demand, the capacity of data centers in the real world is finite. The illusion of infinite capacity in the cloud is provided through statistical multiplexing. When a large number of applications experience their peak demand around the same time, the available resources in the cloud can become constrained and some of the demand may not be satisfied. We define the demand satisfaction ratio as the percentage of application demand that is satisfied successfully. The amount of computing capacity available to an application is limited by the placement of its running instances on the servers.

The more instances an application has and the more powerful the underlying servers are, the higher the potential capacity for satisfying the application demand. On the other hand, when the demand of the applications is low, it is important to conserve energy by reducing the number of servers used. Various studies have found that the cost of electricity is a major portion of the operation cost of large data centers. At the same time, the average server utilization in many Internet data centers is very low: real world estimates range from 5% to 20%. Moreover, work has found that the most effective way to conserve energy is to turn the whole server off. The application placement problem is essential to achieving a high demand satisfaction ratio without wasting energy.

In this paper, we present a system that provides automatic scaling for Internet applications in the cloud environment. Our contributions include the following.

• We summarize the automatic scaling problem in the cloud environment, and model it as a modified Class Constrained Bin Packing (CCBP) problem where each server is a bin and each class represents an application. We develop an innovative auto scaling algorithm to solve the problem and present a rigorous analysis on the quality of it with provable bounds. Compared to the existing Bin Packing solutions, we creatively support item departure which can effectively avoid the frequent placement changes1 caused by repacking.

• We support green computing by adjusting the placement of application instances adaptively and putting idle machines into the standby mode. Experiments and simulations show that our algorithm is highly efficient and scalable which can achieve high demand satisfaction ratio, low placement change frequency, short request response time, and good energy saving.

• We build a real cloud computing system which supports our auto scaling algorithm. We compare the performance of our system with an open source implementation of the Amazon EC2 auto scaling system in a testbed of 30 Dell Power Edge blade servers. Experiments show that our system can restore the normal QoS five times as fast when a flash crowd happens.

• We use a fast restart technique based on virtual machine (VM) suspend and resume that reduces the application start up time dramatically for Internet services. 

1.3 LITRATURE SURVEY

AUTHOR AND PUBLICATION: C. Tang, M. Steinder, M. Spreitzer, and G. Pacifici, “A SCALABLE APPLICATION PLACEMENT CONTROLLER FOR ENTERPRISE DATA CENTERS,” in Proc. Int. World Wide Web Conf. (WWW’07), May 2007, pp. 331–340.

EXPLANATION:

Given a set of machines and a set of Web applications with dynamically changing demands, an online application placement controller decides how many instances to run for each application and where to put them, while observing all kinds of resource constraints. This NP hard problem has real usage in commercial middleware products. Existing approximation algorithms for this problem can scale to at most a few hundred machines, and may produce placement solutions that are far from optimal when system resources are tight. In this paper, we propose a new algorithm that can produce within 30seconds high-quality solutions for hard placement problems with thousands of machines and thousands of applications. This scalability is crucial for dynamic resource provisioning in large-scale enterprise data centers. Our algorithm allows multiple applications to share a single machine, and strivesto maximize the total satisfied application demand, to minimize the number of application starts and stops, and to balance the load across machines. Compared with existing state-of-the-art algorithms, for systems with 100 machines or less, our algorithm is up to 134 times faster, reduces application starts and stops by up to 97%, and produces placement solutions that satisfy up to 25% more application demands. Our algorithm has been implemented and adopted in a leading commercial middleware product for managing the performance of Web applications.

AUTHOR AND PUBLICATION: C. Adam and R. Stadler, “SERVICE MIDDLEWARE FOR SELF-MANAGING LARGE-SCALE SYSTEMS,” IEEE Trans. Netw. Serv. Manage., vol. 4, no. 3, pp. 50–64, Dec. 2007.

EXPLANATION:

Resource management poses particular challenges in large-scale systems, such as server clusters that simultaneously process requests from a large number of clients. A resource management scheme for such systems must scale both in the in the number of cluster nodes and the number of applications the cluster supports. Current solutions do not exhibit both of these properties at the same time. Many are centralized, which limits their scalability in terms of the number of nodes, or they are decentralized but rely on replicated directories, which also reduces their ability to scale. In this paper, we propose novel solutions to request routing and application placement- two key mechanisms in a scalable resource management scheme.

Our solution to request routing is based on selective update propagation, which ensures that the control load on a cluster node is independent of the system size. Application placement is approached in a decentralized manner, by using a distributed algorithm that maximizes resource utilization and allows for service differentiation under overload. The paper demonstrates how the above solutions can be integrated into an overall design for a peer-to-peer management middleware that exhibits properties of self-organization. Through complexity analysis and simulation, we show to which extent the system design is scalable. We have built a prototype using accepted technologies and have evaluated it using a standard benchmark. The testbed measurements show that the implementation, within the parameter range tested, operates efficiently, quickly adapts to a changing environment and allows for effective service differentiation by a system administrator.

AUTHOR AND PUBLICATION: J. Famaey, W. D. Cock, T. Wauters, F. D. Turck, B. Dhoedt, and P. Demeester, “A LATENCY-AWARE ALGORITHM FOR DYNAMIC SERVICE PLACEMENT IN LARGE-SCALE OVERLAYS,” in Proc. IFIP/IEEE Int. Conf. Symp. Integrat. Netw. Manage. (IM’09), 2009, pp. 414–421.

EXPLANATION:

A generic and self-managing service hosting infrastructure, provides a means to offer a large variety of services to users across the Internet. Such an infrastructure provides mechanisms to automatically allocate resources to services, discover the location of these services, and route client requests to a suitable service instance. In this paper we propose a dynamic and latency-aware algorithm for assigning resources to services. Additionally, the proposed service hosting architecture and its protocols to support the service placement algorithm, are described in detail. Extensive simulations were performed to compare the solution of our latency-aware algorithm to the latency-unaware variant, in terms of system efficiency and scalability.

AUTHOR AND PUBLICATION: E. Caron, L. Rodero-Merino, F. Desprez, and A.Muresan, “AUTOSCALING, LOAD BALANCING AND MONITORING IN COMMERCIAL AND OPENSOURCE CLOUDS,” INRIA, Rapport de recherche RR-7857, Feb. 2012.

EXPLANATION:

Now – a – days because of increased use of internet the associate resource are increasing rapidly resulting generation of high work load. To provide the reliable service to client with QOS the load balancing mechanism is necessary in cloud environment, to prevent system from overloading and crash an autoscaling mechanism must also be provided according to the application and incoming user traffic. load balancing mechanism provides the distribution of load among one or more nodes of cloud system, for efficient service model autoscaling feature also enabled with the load balancer to handle the excess load. Auto scaling scaled-up and scaled down the platform dynamically according to the clients incoming traffic this save money and physical resources. Latency based routing is the new concept in cloud computing which provide the load balancing based on DNS latency to global client by mapping domain name system (DNS) through the different hosted zone .provide the load balancing based on geographical service region. To achieve above mentioned we use the public cloud services such as amazons’EC2. ELB. This research is divided in four part such as: i) load balancing ii) auto scaling iii)latency based routing iv) resource monitoring . while discussing each topic in detailed we will implement the individual service and test while providing load from external software tool putty we will produce result for efficient load balancing.

CHAPTER 2

2.0 SYSTEM ANALYSIS

2.1 EXISTING SYSTEM:

Existing algorithms for the online and offline class-constrained bin packing problem is motivated by applications in the data-placement problem to video-on-demand servers and applications in the cutting and packing area. For the online problem we provide lower bounds for any bounded space algorithm and we also present an algorithm for the unbounded version with approximation factor low value.

For the offline problem we present practical approximation algorithms for two special cases of the problem, with conditions already considered in the literature: when all items have the same size and the parameterized version of the problem. We also perform several tests with these practical algorithms. For the instances we considered representing practical ones, the algorithms optimal solutions an CCBP for the special case where the number of different classes of the input instance is bounded by a constant.

Therefore, in order to solve our problem, we modified the CCBP model to support the “Minimize the placement change frequency” goal and provide a new enhanced semionline approximation algorithm to solve it in the next section. Note that the equations above are just a formal presentation of the goals and constraints of our problem.

2.1.1 DISADVANTAGES:

Automatic scaling problem in the cloud environment, and model it as a modified Class Constrained Bin Packing (CCBP) problem where each server is a bin and each class represents an application.

In the traditional bin packing problem, a series of items of different sizes need to be packed into a minimum number of bins. The class constrained version of this problem divides the items into classes or colors.

Each bin has capacity v and can accommodate items from at most c distinct classes. It is “class constrained” because the class diversity of items packed into the same bin is constrained. The goal is to pack the items into a minimum number of bins.

Existing algorithm is the support for item departure which is essential to maintaining not performance in a cloud computing environment where the resource demands of Internet applications can vary dynamically.

2.2 PROPOSED SYSTEM:

We develop an efficient semi-online color set algorithm that achieves good demand satisfaction ratio and saves energy by reducing the number of servers used each class of items with a color and organize them into color sets as they arrive in the input sequence. The number of distinct colors in a color set is at most c (i.e., the maximum number of distinct classes in a bin). This ensures that items in a color set can always be packed into the same bin without violating the class constraint. The packing is still subject to the capacity constraint of the bin. All color sets contain exactly c colors except the last one which may contain fewer colors. Items from different color sets are packed independently.

A greedy algorithm is used to pack items within each color set: the items are packed into the current bin until the capacity is reached. Then the next bin is opened for packing. Thus each color set has at most one unfilled (i.e., non-full) bin. Note that a full bin may contain fewer than c colors. When a new item from a specific color set arrives, it is packed into the corresponding unfilled bin. If all bins of that color set are full, then a new bin is opened to accommodate the item. The load increase of an application is modeled as the arrival of items with the corresponding color. A naive algorithm is to always pack the item into the unfilled bin if there is one. If the unfilled bin does not contain that color already, then a new color is added into the bin.

We allocate the new colors to the unfilled sets first using the following add_new_colors procedure.

Procedure add_new_colors:

Sort the list of unfilled color sets in descending order of their cardinality. Use a greedy algorithm to add the new colors into those sets according to their positions in the list.

If we run out of the new colors before filling up all but the last unfilled sets, use the consolidate_unfilled_sets procedure below to consolidate the remaining unfilled sets until there is only one left.

If there are still new colors left after filling up all unfilled sets in the system, we partition the remaining new colors into additional color sets using a greedy algorithm.

The consolidate_unfilled_sets procedure below consolidates unfilled sets in the system until there is only one left.

Procedure consolidate_unfilled_sets:

Sort the list of unfilled color sets in descending order of their cardinality Use the last set in the list (with the fewest colors) to fill the first set in the list (with the most colors) through the fill procedure below. Remove the resulting full set or empty set from the list.

2.2.1 ADVANTAGES:

We support green computing by adjusting the placement of application instances adaptively and putting idle machines into the standby mode. Experiments and simulations show that our algorithm is highly efficient and scalable which can achieve high demand satisfaction ratio, low placement change frequency, short request response time, and good energy saving.

We build a real cloud computing system which supports our auto scaling algorithm. We compare the performance of our system with an open source implementation of the internet auto scaling system in a testbed of 30 Dell PowerEdge blade servers.

Experiments show that our system can restore the normal QoS five times as fast when a flash crowd happens. We use a fast restart technique based on virtual machine (VM) suspend and resume that reduces the application start up time dramatically for Internet services.

2.3 HARDWARE & SOFTWARE REQUIREMENTS:

2.3.1 HARDWARE REQUIREMENT:

v    Processor                                 –    Pentium –IV

• Speed                                      –    1.1 GHz
  • RAM                                       –    256 MB (min)
  • Hard Disk                               –   20 GB
  • Floppy Drive                           –    1.44 MB
  • Key Board                              –    Standard Windows Keyboard
  • Mouse                                     –    Two or Three Button Mouse
  • Monitor                                   –    SVGA

 

2.3.2 SOFTWARE REQUIREMENTS:

• Operating System                   :           Windows XP or Win7
• Front End                                :           JAVA JDK 1.7
• Back End                                :           MYSQL Server
• Server                                      :           Apache Tomact Server
• Script                                       :           JSP Script
• Document                               :           MS-Office 2007

CHAPTER 3

3.0 SYSTEM DESIGN:

Data Flow Diagram / Use Case Diagram / Flow Diagram:

• The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of the input data to the system, various processing carried out on these data, and the output data is generated by the system

• The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. These components are the system process, the data used by the process, an external entity that interacts with the system and the information flows in the system.

• DFD shows how the information moves through the system and how it is modified by a series of transformations. It is a graphical technique that depicts information flow and the transformations that are applied as data moves from input to output.

• DFD is also known as bubble chart. A DFD may be used to represent a system at any level of abstraction. DFD may be partitioned into levels that represent increasing information flow and functional detail.

NOTATION:

SOURCE OR DESTINATION OF DATA:

External sources or destinations, which may be people or organizations or other entities

DATA SOURCE:

Here the data referenced by a process is stored and retrieved.

PROCESS:

People, procedures or devices that produce data’s in the physical component is not identified.

DATA FLOW:

Data moves in a specific direction from an origin to a destination. The data flow is a “packet” of data.

MODELING RULES:

There are several common modeling rules when creating DFDs:

1. All processes must have at least one data flow in and one data flow out.
2. All processes should modify the incoming data, producing new forms of outgoing data.
3. Each data store must be involved with at least one data flow.
4. Each external entity must be involved with at least one data flow.
5. A data flow must be attached to at least one process.

3.1 ARCHITECTURE DIAGRAM

3.2 DATAFLOW DIAGRAM

ADMIN:

USER:

UML DIAGRAMS:

3.2 USE CASE DIAGRAM:

ADMIN:

USER:

3.3 CLASS DIAGRAM:

3.4 SEQUENCE DIAGRAM:

ADMIN:

USER:

3.5 ACTIVITY DIAGRAM:

ADMIN:

USER:

CHAPTER 4

4.0 IMPLEMENTATION:

4.1 ALGORITHM:

Our algorithm belongs to the family of color set algorithms with significant modification to adapt to our problem. A detailed comparison with the existing algorithm is deferred to Section 7 so that sufficient background can be established. We label each class of items with a color and organize them into color sets as they arrive in the input sequence. The number of distinct colors in a color set is at most c (i.e., the maximum number of distinct classes in a bin). This ensures that items in a color set can always be packed into the same bin without violating the class constraint. The packing is still subject to the capacity constraint of the bin. All color sets contain exactly c colors except the last one which may contain fewer colors.

Items from different color sets are packed independently. A greedy algorithm is used to pack items within each color set: the items are packed into the current bin until the capacity is reached. Then the next bin is opened for packing. Thus each color set has at most one unfilled (i.e., non-full) bin. Note that a full bin may contain fewer than c colors. When a new item from a specific color set arrives, it is packed into the corresponding unfilled bin. If all bins of that color set are full, then a new bin is opened to accommodate the item.

Our basic idea is to fill up the unfilled sets (except the last one) while minimizing its impact on the existing color assignment. We first check if there are any pending requests to add new colors into the system. If there are, we allocate the new colors to the unfilled sets first using the following add_new_colors procedure.

Procedure add_new_colors:

Sort the list of unfilled color sets in descending order of their cardinality. Use a greedy algorithm to add the new colors into those sets according to their positions in the list. If we run out of the new colors before filling up all but the last unfilled sets, use the consolidate_unfilled_sets procedure below to consolidate the remaining unfilled sets until there is only one left.

If there are still new colors left after filling up all unfilled sets in the system, we partition the remaining new colors into additional color sets using a greedy algorithm. The consolidate_unfilled_sets procedure below consolidates unfilled sets in the system until there is only one left.

Procedure consolidate_unfilled_sets:

Sort the list of unfilled color sets in descending order of their cardinality Use the last set in the list (with the fewest colors) to fill the first set in the list (with the most colors) through the fill procedure below.

Remove the resulting full set or empty set from the list.

Repeat the previous step until there is only one unfilled set left in the list. The fill( , ) procedure below uses the colors in set to fill the set . Procedure fill( , ):

Sort the list of colors in in ascending order of their numbers of items.

Addthe first color in the list (with the fewest items) into. Use “item departure” operation in and “item arrival” operation in to move all items of that color from to . Then remove that color from the list. Repeat the above step until either becomes empty or becomes full.

4.2 MODULES:

USER MODULES:

LOCAL NODE MANAGER (LNM):

APPLICATION LOAD INCREASE:

APPLICATION LOAD DECREASE:

AUTOMATIC SCALING RESOURCES:

APPROXIMATION RATIO:

4.3 MODULE DESCRIPTION:

CHAPTER 5

5.0 SYSTEM STUDY:

5.1 FEASIBILITY STUDY:

The feasibility of the project is analyzed in this phase and business proposal is put forth with a very general plan for the project and some cost estimates. During system analysis the feasibility study of the proposed system is to be carried out. This is to ensure that the proposed system is not a burden to the company.  For feasibility analysis, some understanding of the major requirements for the system is essential.

Three key considerations involved in the feasibility analysis are 

• ECONOMICAL FEASIBILITY
• TECHNICAL FEASIBILITY
• SOCIAL FEASIBILITY

5.1.1 ECONOMICAL FEASIBILITY:     

This study is carried out to check the economic impact that the system will have on the organization. The amount of fund that the company can pour into the research and development of the system is limited. The expenditures must be justified. Thus the developed system as well within the budget and this was achieved because most of the technologies used are freely available. Only the customized products had to be purchased.

 

5.1.2 TECHNICAL FEASIBILITY   

This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.  

5.1.3 SOCIAL FEASIBILITY:  

The aspect of study is to check the level of acceptance of the system by the user. This includes the process of training the user to use the system efficiently. The user must not feel threatened by the system, instead must accept it as a necessity. The level of acceptance by the users solely depends on the methods that are employed to educate the user about the system and to make him familiar with it. His level of confidence must be raised so that he is also able to make some constructive criticism, which is welcomed, as he is the final user of the system.

5.2 SYSTEM TESTING:

Testing is a process of checking whether the developed system is working according to the original objectives and requirements. It is a set of activities that can be planned in advance and conducted systematically. Testing is vital to the success of the system. System testing makes a logical assumption that if all the parts of the system are correct, the global will be successfully achieved. In adequate testing if not testing leads to errors that may not appear even many months.

This creates two problems, the time lag between the cause and the appearance of the problem and the effect of the system errors on the files and records within the system. A small system error can conceivably explode into a much larger Problem. Effective testing early in the purpose translates directly into long term cost savings from a reduced number of errors. Another reason for system testing is its utility, as a user-oriented vehicle before implementation. The best programs are worthless if it produces the correct outputs.

5.2.1 UNIT TESTING:

Description	Expected result
Test for application window properties.	All the properties of the windows are to be properly aligned and displayed.
Test for mouse operations.	All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions.

A program represents the logical elements of a system. For a program to run satisfactorily, it must compile and test data correctly and tie in properly with other programs. Achieving an error free program is the responsibility of the programmer. Program  testing  checks  for  two  types  of  errors:  syntax  and  logical. Syntax error is a program statement that violates one or more rules of the language in which it is written. An improperly defined field dimension or omitted keywords are common syntax errors. These errors are shown through error message generated by the computer. For Logic errors the programmer must examine the output carefully.

5.1.2 FUNCTIONAL TESTING:

Functional testing of an application is used to prove the application delivers correct results, using enough inputs to give an adequate level of confidence that will work correctly for all sets of inputs. The functional testing will need to prove that the application works for each client type and that personalization function work correctly.When a program is tested, the actual output is compared with the expected output. When there is a discrepancy the sequence of instructions must be traced to determine the problem.  The process is facilitated by breaking the program into self-contained portions, each of which can be checked at certain key points. The idea is to compare program values against desk-calculated values to isolate the problems.

Description	Expected result
Test for all modules.	All peers should communicate in the group.
Test for various peer in a distributed network framework as it display all users available in the group.	The result after execution should give the accurate result.

5.1. 3 NON-FUNCTIONAL TESTING:

 The Non Functional software testing encompasses a rich spectrum of testing strategies, describing the expected results for every test case. It uses symbolic analysis techniques. This testing used to check that an application will work in the operational environment. Non-functional testing includes:

• Load testing
• Performance testing
• Usability testing
• Reliability testing
• Security testing

5.1.4 LOAD TESTING:

An important tool for implementing system tests is a Load generator. A Load generator is essential for testing quality requirements such as performance and stress. A load can be a real load, that is, the system can be put under test to real usage by having actual telephone users connected to it. They will generate test input data for system test.

Description	Expected result
It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received.	Should designate another active node as a Server.

5.1.5 PERFORMANCE TESTING:

Performance tests are utilized in order to determine the widely defined performance of the software system such as execution time associated with various parts of the code, response time and device utilization. The intent of this testing is to identify weak points of the software system and quantify its shortcomings.

Description	Expected result
This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management.	Should handle large input values, and produce accurate result in a  expected time.

5.1.6 RELIABILITY TESTING:

The software reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time and it is being ensured in this testing. Reliability can be expressed as the ability of the software to reveal defects under testing conditions, according to the specified requirements. It the portability that a software system will operate without failure under given conditions for a given time interval and it focuses on the behavior of the software element. It forms a part of the software quality control team.

Description	Expected result
This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application.	In case of failure of  the server an alternate server should take over the job.

5.1.7 SECURITY TESTING:

Security testing evaluates system characteristics that relate to the availability, integrity and confidentiality of the system data and services. Users/Clients should be encouraged to make sure their security needs are very clearly known at requirements time, so that the security issues can be addressed by the designers and testers.

Description	Expected result
Checking that the user identification is authenticated.	In case failure it should not be connected in the framework.
Check whether group keys in a tree are shared by all peers.	The peers should know group key in the same group.

5.1.8 WHITE BOX TESTING:

White  box  testing,  sometimes called  glass-box  testing is  a test  case  design method  that  uses  the  control  structure  of the procedural  design  to  derive  test  cases. Using  white  box  testing  method,  the software  engineer  can  derive  test  cases. The White box testing focuses on the inner structure of the software structure to be tested.

Description	Expected result
Exercise all logical decisions on their true and false sides.	All the logical decisions must be valid.
Execute all loops at their boundaries and within their operational bounds.	All the loops must be finite.
Exercise internal data structures to ensure their validity.	All the data structures must be valid.

5.1.9 BLACK BOX TESTING:

Black box testing, also called behavioral testing, focuses on the functional requirements of the software.  That  is,  black  testing  enables  the software engineer  to  derive  sets  of  input  conditions  that  will  fully  exercise  all  functional requirements  for  a  program.  Black box testing is not alternative to white box techniques.  Rather  it  is  a  complementary  approach  that  is  likely  to  uncover  a different  class  of  errors  than  white box  methods. Black box testing attempts to find errors which focuses on inputs, outputs, and principle function of a software module. The starting point of the black box testing is either a specification or code. The contents of the box are hidden and the stimulated software should produce the desired results.

Description	Expected result
To check for incorrect or missing functions.	All the functions must be valid.
To check for interface errors.	The entire interface must function normally.
To check for errors in a data structures or external data base access.	The database updation and retrieval must be done.
To check for initialization and termination errors.	All the functions and data structures must be initialized properly and terminated normally.

All the above system testing strategies are carried out in as the development, documentation and institutionalization of the proposed goals and related policies is essential.

CHAPTER 6

6.0 SOFTWARE DESCRIPTION:

 

6.1 JAVA TECHNOLOGY:

Java technology is both a programming language and a platform.

 

The Java Programming Language

 

The Java programming language is a high-level language that can be characterized by all of the following buzzwords:

• Simple
  • Architecture neutral
  • Object oriented
  • Portable
  • Distributed     
  • High performance
  • Interpreted     
  • Multithreaded
  • Robust
  • Dynamic
  • Secure     

With most programming languages, you either compile or interpret a program so that you can run it on your computer. The Java programming language is unusual in that a program is both compiled and interpreted. With the compiler, first you translate a program into an intermediate language called Java byte codes —the platform-independent codes interpreted by the interpreter on the Java platform. The interpreter parses and runs each Java byte code instruction on the computer. Compilation happens just once; interpretation occurs each time the program is executed. The following figure illustrates how this works.

You can think of Java byte codes as the machine code instructions for the Java Virtual Machine (Java VM). Every Java interpreter, whether it’s a development tool or a Web browser that can run applets, is an implementation of the Java VM. Java byte codes help make “write once, run anywhere” possible. You can compile your program into byte codes on any platform that has a Java compiler. The byte codes can then be run on any implementation of the Java VM. That means that as long as a computer has a Java VM, the same program written in the Java programming language can run on Windows 2000, a Solaris workstation, or on an iMac.

6.2 THE JAVA PLATFORM:

A platform is the hardware or software environment in which a program runs. We’ve already mentioned some of the most popular platforms like Windows 2000, Linux, Solaris, and MacOS. Most platforms can be described as a combination of the operating system and hardware. The Java platform differs from most other platforms in that it’s a software-only platform that runs on top of other hardware-based platforms.

The Java platform has two components:

• The Java Virtual Machine (Java VM)
• The Java Application Programming Interface (Java API)

You’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported onto various hardware-based platforms.

The Java API is a large collection of ready-made software components that provide many useful capabilities, such as graphical user interface (GUI) widgets. The Java API is grouped into libraries of related classes and interfaces; these libraries are known as packages. The next section, What Can Java Technology Do? Highlights what functionality some of the packages in the Java API provide.

The following figure depicts a program that’s running on the Java platform. As the figure shows, the Java API and the virtual machine insulate the program from the hardware.

Native code is code that after you compile it, the compiled code runs on a specific hardware platform. As a platform-independent environment, the Java platform can be a bit slower than native code. However, smart compilers, well-tuned interpreters, and just-in-time byte code compilers can bring performance close to that of native code without threatening portability.

6.3 WHAT CAN JAVA TECHNOLOGY DO?

The most common types of programs written in the Java programming language are applets and applications. If you’ve surfed the Web, you’re probably already familiar with applets. An applet is a program that adheres to certain conventions that allow it to run within a Java-enabled browser.

However, the Java programming language is not just for writing cute, entertaining applets for the Web. The general-purpose, high-level Java programming language is also a powerful software platform. Using the generous API, you can write many types of programs.

An application is a standalone program that runs directly on the Java platform. A special kind of application known as a server serves and supports clients on a network. Examples of servers are Web servers, proxy servers, mail servers, and print servers. Another specialized program is a servlet.

A servlet can almost be thought of as an applet that runs on the server side. Java Servlets are a popular choice for building interactive web applications, replacing the use of CGI scripts. Servlets are similar to applets in that they are runtime extensions of applications. Instead of working in browsers, though, servlets run within Java Web servers, configuring or tailoring the server.

How does the API support all these kinds of programs? It does so with packages of software components that provides a wide range of functionality. Every full implementation of the Java platform gives you the following features:

• The essentials: Objects, strings, threads, numbers, input and output, data structures, system properties, date and time, and so on.
• Applets: The set of conventions used by applets.
• Networking: URLs, TCP (Transmission Control Protocol), UDP (User Data gram Protocol) sockets, and IP (Internet Protocol) addresses.
• Internationalization: Help for writing programs that can be localized for users worldwide. Programs can automatically adapt to specific locales and be displayed in the appropriate language.
• Security: Both low level and high level, including electronic signatures, public and private key management, access control, and certificates.
• Software components: Known as JavaBeans^TM, can plug into existing component architectures.
• Object serialization: Allows lightweight persistence and communication via Remote Method Invocation (RMI).
• Java Database Connectivity (JDBC^TM): Provides uniform access to a wide range of relational databases.

The Java platform also has APIs for 2D and 3D graphics, accessibility, servers, collaboration, telephony, speech, animation, and more. The following figure depicts what is included in the Java 2 SDK.

 

6.4 HOW WILL JAVA TECHNOLOGY CHANGE MY LIFE?

We can’t promise you fame, fortune, or even a job if you learn the Java programming language. Still, it is likely to make your programs better and requires less effort than other languages. We believe that Java technology will help you do the following:

• Get started quickly: Although the Java programming language is a powerful object-oriented language, it’s easy to learn, especially for programmers already familiar with C or C++.
• Write less code: Comparisons of program metrics (class counts, method counts, and so on) suggest that a program written in the Java programming language can be four times smaller than the same program in C++.
• Write better code: The Java programming language encourages good coding practices, and its garbage collection helps you avoid memory leaks. Its object orientation, its JavaBeans component architecture, and its wide-ranging, easily extendible API let you reuse other people’s tested code and introduce fewer bugs.
• Develop programs more quickly: Your development time may be as much as twice as fast versus writing the same program in C++. Why? You write fewer lines of code and it is a simpler programming language than C++.
• Avoid platform dependencies with 100% Pure Java: You can keep your program portable by avoiding the use of libraries written in other languages. The 100% Pure Java^TM Product Certification Program has a repository of historical process manuals, white papers, brochures, and similar materials online.
• Write once, run anywhere: Because 100% Pure Java programs are compiled into machine-independent byte codes, they run consistently on any Java platform.
• Distribute software more easily: You can upgrade applets easily from a central server. Applets take advantage of the feature of allowing new classes to be loaded “on the fly,” without recompiling the entire program.

 

6.5 ODBC:

 

Microsoft Open Database Connectivity (ODBC) is a standard programming interface for application developers and database systems providers. Before ODBC became a de facto standard for Windows programs to interface with database systems, programmers had to use proprietary languages for each database they wanted to connect to. Now, ODBC has made the choice of the database system almost irrelevant from a coding perspective, which is as it should be. Application developers have much more important things to worry about than the syntax that is needed to port their program from one database to another when business needs suddenly change.

Through the ODBC Administrator in Control Panel, you can specify the particular database that is associated with a data source that an ODBC application program is written to use. Think of an ODBC data source as a door with a name on it. Each door will lead you to a particular database. For example, the data source named Sales Figures might be a SQL Server database, whereas the Accounts Payable data source could refer to an Access database. The physical database referred to by a data source can reside anywhere on the LAN.

The ODBC system files are not installed on your system by Windows 95. Rather, they are installed when you setup a separate database application, such as SQL Server Client or Visual Basic 4.0. When the ODBC icon is installed in Control Panel, it uses a file called ODBCINST.DLL. It is also possible to administer your ODBC data sources through a stand-alone program called ODBCADM.EXE. There is a 16-bit and a 32-bit version of this program and each maintains a separate list of ODBC data sources.

From a programming perspective, the beauty of ODBC is that the application can be written to use the same set of function calls to interface with any data source, regardless of the database vendor. The source code of the application doesn’t change whether it talks to Oracle or SQL Server. We only mention these two as an example. There are ODBC drivers available for several dozen popular database systems. Even Excel spreadsheets and plain text files can be turned into data sources. The operating system uses the Registry information written by ODBC Administrator to determine which low-level ODBC drivers are needed to talk to the data source (such as the interface to Oracle or SQL Server). The loading of the ODBC drivers is transparent to the ODBC application program. In a client/server environment, the ODBC API even handles many of the network issues for the application programmer.

The advantages of this scheme are so numerous that you are probably thinking there must be some catch. The only disadvantage of ODBC is that it isn’t as efficient as talking directly to the native database interface. ODBC has had many detractors make the charge that it is too slow. Microsoft has always claimed that the critical factor in performance is the quality of the driver software that is used. In our humble opinion, this is true. The availability of good ODBC drivers has improved a great deal recently. And anyway, the criticism about performance is somewhat analogous to those who said that compilers would never match the speed of pure assembly language. Maybe not, but the compiler (or ODBC) gives you the opportunity to write cleaner programs, which means you finish sooner. Meanwhile, computers get faster every year.

6.6 JDBC:

In an effort to set an independent database standard API for Java; Sun Microsystems developed Java Database Connectivity, or JDBC. JDBC offers a generic SQL database access mechanism that provides a consistent interface to a variety of RDBMSs. This consistent interface is achieved through the use of “plug-in” database connectivity modules, or drivers. If a database vendor wishes to have JDBC support, he or she must provide the driver for each platform that the database and Java run on.

To gain a wider acceptance of JDBC, Sun based JDBC’s framework on ODBC. As you discovered earlier in this chapter, ODBC has widespread support on a variety of platforms. Basing JDBC on ODBC will allow vendors to bring JDBC drivers to market much faster than developing a completely new connectivity solution.

JDBC was announced in March of 1996. It was released for a 90 day public review that ended June 8, 1996. Because of user input, the final JDBC v1.0 specification was released soon after.

The remainder of this section will cover enough information about JDBC for you to know what it is about and how to use it effectively. This is by no means a complete overview of JDBC. That would fill an entire book.

 

6.7 JDBC Goals:

Few software packages are designed without goals in mind. JDBC is one that, because of its many goals, drove the development of the API. These goals, in conjunction with early reviewer feedback, have finalized the JDBC class library into a solid framework for building database applications in Java.

The goals that were set for JDBC are important. They will give you some insight as to why certain classes and functionalities behave the way they do. The eight design goals for JDBC are as follows:

SQL Level API

The designers felt that their main goal was to define a SQL interface for Java. Although not the lowest database interface level possible, it is at a low enough level for higher-level tools and APIs to be created. Conversely, it is at a high enough level for application programmers to use it confidently. Attaining this goal allows for future tool vendors to “generate” JDBC code and to hide many of JDBC’s complexities from the end user.

SQL Conformance

SQL syntax varies as you move from database vendor to database vendor. In an effort to support a wide variety of vendors, JDBC will allow any query statement to be passed through it to the underlying database driver. This allows the connectivity module to handle non-standard functionality in a manner that is suitable for its users.

JDBC must be implemental on top of common database interfaces

The JDBC SQL API must “sit” on top of other common SQL level APIs. This goal allows JDBC to use existing ODBC level drivers by the use of a software interface. This interface would translate JDBC calls to ODBC and vice versa.

1. Provide a Java interface that is consistent with the rest of the Java system

Because of Java’s acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system.

• Keep it simple

This goal probably appears in all software design goal listings. JDBC is no exception. Sun felt that the design of JDBC should be very simple, allowing for only one method of completing a task per mechanism. Allowing duplicate functionality only serves to confuse the users of the API.

• Use strong, static typing wherever possible

Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.

• Keep the common cases simple

Because more often than not, the usual SQL calls used by the programmer are simple SELECT’s, INSERT’s, DELETE’s and UPDATE’s, these queries should be simple to perform with JDBC. However, more complex SQL statements should also be possible.

Finally we decided to precede the implementation using Java Networking.

And for dynamically updating the cache table we go for MS Access database.

Java ha two things: a programming language and a platform.

Java is a high-level programming language that is all of the following

Simple                                     Architecture-neutral

Object-oriented                       Portable

Distributed                              High-performance

Interpreted                              Multithreaded

Robust                                     Dynamic Secure

Java is also unusual in that each Java program is both compiled and interpreted. With a compile you translate a Java program into an intermediate language called Java byte codes the platform-independent code instruction is passed and run on the computer.

Compilation happens just once; interpretation occurs each time the program is executed. The figure illustrates how this works.

6.7 NETWORKING TCP/IP STACK:

The TCP/IP stack is shorter than the OSI one:

TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.

IP datagram’s:

The IP layer provides a connectionless and unreliable delivery system. It considers each datagram independently of the others. Any association between datagram must be supplied by the higher layers. The IP layer supplies a checksum that includes its own header. The header includes the source and destination addresses. The IP layer handles routing through an Internet. It is also responsible for breaking up large datagram into smaller ones for transmission and reassembling them at the other end.

UDP:

UDP is also connectionless and unreliable. What it adds to IP is a checksum for the contents of the datagram and port numbers. These are used to give a client/server model – see later.

TCP:

TCP supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.

Internet addresses

In order to use a service, you must be able to find it. The Internet uses an address scheme for machines so that they can be located. The address is a 32 bit integer which gives the IP address.

Network address:

Class A uses 8 bits for the network address with 24 bits left over for other addressing. Class B uses 16 bit network addressing. Class C uses 24 bit network addressing and class D uses all 32.

Subnet address:

Internally, the UNIX network is divided into sub networks. Building 11 is currently on one sub network and uses 10-bit addressing, allowing 1024 different hosts.

Host address:

8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.

Total address:

The 32 bit address is usually written as 4 integers separated by dots.

Port addresses

A service exists on a host, and is identified by its port. This is a 16 bit number. To send a message to a server, you send it to the port for that service of the host that it is running on. This is not location transparency! Certain of these ports are “well known”.

Sockets:

A socket is a data structure maintained by the system to handle network connections. A socket is created using the call socket. It returns an integer that is like a file descriptor. In fact, under Windows, this handle can be used with Read File and Write File functions.

#include <sys/types.h>

#include <sys/socket.h>

int socket(int family, int type, int protocol);

Here “family” will be AF_INET for IP communications, protocol will be zero, and type will depend on whether TCP or UDP is used. Two processes wishing to communicate over a network create a socket each. These are similar to two ends of a pipe – but the actual pipe does not yet exist.

6.8 JFREE CHART:

JFreeChart is a free 100% Java chart library that makes it easy for developers to display professional quality charts in their applications. JFreeChart’s extensive feature set includes:

A consistent and well-documented API, supporting a wide range of chart types;

A flexible design that is easy to extend, and targets both server-side and client-side applications;

Support for many output types, including Swing components, image files (including PNG and JPEG), and vector graphics file formats (including PDF, EPS and SVG);

JFreeChart is “open source” or, more specifically, free software. It is distributed under the terms of the GNU Lesser General Public Licence (LGPL), which permits use in proprietary applications.

 

6.8.1. Map Visualizations:

Charts showing values that relate to geographical areas. Some examples include: (a) population density in each state of the United States, (b) income per capita for each country in Europe, (c) life expectancy in each country of the world. The tasks in this project include: Sourcing freely redistributable vector outlines for the countries of the world, states/provinces in particular countries (USA in particular, but also other areas);

Creating an appropriate dataset interface (plus default implementation), a rendered, and integrating this with the existing XYPlot class in JFreeChart; Testing, documenting, testing some more, documenting some more.

6.8.2. Time Series Chart Interactivity

Implement a new (to JFreeChart) feature for interactive time series charts — to display a separate control that shows a small version of ALL the time series data, with a sliding “view” rectangle that allows you to select the subset of the time series data to display in the main chart.

6.8.3. Dashboards

There is currently a lot of interest in dashboard displays. Create a flexible dashboard mechanism that supports a subset of JFreeChart chart types (dials, pies, thermometers, bars, and lines/time series) that can be delivered easily via both Java Web Start and an applet.

 

6.8.4. Property Editors

The property editor mechanism in JFreeChart only handles a small subset of the properties that can be set for charts. Extend (or reimplement) this mechanism to provide greater end-user control over the appearance of the charts.

CHAPTER 7

APPENDIX

7.1 SAMPLE SOURCE CODE

7.2 SAMPLE OUTPUT

CHAPTER 8

8.1 CONCLUSION

We presented the design and implementation of a system that can scale up and down the number of application instances automatically based on demand. We developed a color set algorithm to decide the application placement and the load distribution. Our system achieves high satisfaction ratio of application demand even when the load is very high. It saves energy by reducing the number of running instances when the load is low.

There are several directions for future work. Some cloud service providers may provide multiple levels of services to their customers. When the resources become tight, they may want to give their premium customers a higher demand satisfaction ratio than other customers. In the future, we plan to extend our system to support differentiated services but also consider fairness when allocating the resources across the applications. We mentioned in the paper that we can divide multiple generations of hardware in a data center into “equivalence classes” and run our algorithm within each class.

Our future work is to develop an efficient algorithm to distribute incoming requests among the set of equivalence classes and to balance the load across those server clusters adaptively. As analyzed in the paper, CCBP works well when the aggregate load of applications in a color set is high. Another direction for future work is to extend the algorithm to pack applications with complementary bottleneck resources together, e.g., to co-locate a CPU intensive application with a memory intensive one so that different dimensions of server resources can be adequately utilized.

CHAPTER 9

9.1 REFERENCES

1. C. Tang, M. Steinder, M. Spreitzer, and G. Pacifici, “A scalable application placement controller for enterprise data centers,” in Proc. Int. World Wide Web Conf. (WWW’07), May 2007, pp. 331–340.

• C. Adam and R. Stadler, “Service middleware for self-managing large-scale systems,” IEEE Trans. Netw. Serv. Manage., vol. 4, no. 3, pp. 50–64, Dec. 2007.

• J. Famaey, W. D. Cock, T. Wauters, F. D. Turck, B. Dhoedt, and P. Demeester, “A latency-aware algorithm for dynamic service placement in large-scale overlays,” in Proc. IFIP/IEEE Int. Conf. Symp. Integrat. Netw. Manage. (IM’09), 2009, pp. 414–421.

• E. Caron, L. Rodero-Merino, F. Desprez, and A.Muresan, “Autoscaling, load balancing and monitoring in commercial and opensource clouds,” INRIA, Rapport de recherche RR-7857, Feb. 2012.

• A. Karve, T. Kimbrel, G. Pacifici, M. Spreitzer, M. Steinder, M. Sviridenko, and A. Tantawi, “Dynamic placement for clustered web applications,” in Proc. Int. World Wide Web Conf. (WWW’06), May 2006, pp. 595–604.

• D. Magenheimer, “Transcendent memory: A new approach to managingRAMin a virtualized environment,” in Proc. Linux Symp., 2009, pp. 191–200.

• E. C. Xavier and F. K. Miyazawa, “The class constrained bin packing problem with applications to video-on-demand,” Theor. Comput.Sci., vol. 393, no. 1–3, pp. 240–259, 2008.

A STUDY ON FALSE CHANNEL CONDITION REPORTING ATTACKS IN WIRELESS NETWORKS

By

A

PROJECT REPORT

Submitted to the Department of Computer Science & Engineering in the                                                  FACULTY OF ENGINEERING & TECHNOLOGY

In partial fulfillment of the requirements for the award of the degree

Of

MASTER OF TECHNOLOGY

IN

COMPUTER SCIENCE & ENGINEERING

APRIL 2015

BONAFIDE CERTIFICATE

Certified that this project report titled “A STUDY ON FALSE CHANNEL CONDITION REPORTING ATTACKS IN WIRELESS NETWORKS” is the bonafide work of Mr. _____________Who carried out the research under my supervision Certified further, that to the best of my knowledge the work reported herein does not form part of any other project report or dissertation on the basis of which a degree or award was conferred on an earlier occasion on this or any other candidate.

Signature of the Guide                                                                             Signature of the H.O.D

Name                                                                                                           Name

CHAPTER 1

1. ABSTRACT:

Wireless networking protocols are increasingly being designed to exploit a user’s measured channel condition; we call such protocols channel-aware. Each user reports the measured channel condition to a manager of wireless resources and a channel-aware protocol uses these reports to determine how resources are allocated to users. In a channel-aware protocol, each user’s reported channel condition affects the performance of every other user. The deployment of channel-aware protocols increases the risks posed by false channel-condition feedback.

We study what happens in the presence of an attacker that falsely reports its channel condition. We perform case studies on channel-aware network protocols to understand how an attack can use false feedback and how much the attack can affect network performance. The results of the case studies show that we need a secure channel condition estimation algorithm to fundamentally defend against the channel-condition misreporting attack. We design such an algorithm and evaluate our algorithm through analysis and simulation. Our evaluation quantifies the effect of our algorithm on system performance as well as the security and the performance of our algorithm.

1. INTRODUCTION

Many protocols in modern wireless networks treat a link’s channel condition information as a protocol input parameter; we call such protocols channel-aware. Examples include cooperative relaying network architectures, efficient ad hoc network routing metrics, and opportunistic schedulers. While work on channel-aware protocols has mainly focused on how channel condition information can be used to more efficiently utilize wireless resources, security aspects of channel-aware protocols have only recently been studied. These works on security of channel-aware protocols revealed new threats in specific network environments by simulation or measurement.

However, understanding the effect of possible attacks across varied network environments is still an open area for study. In particular, we consider the effect of a user equipment’s reporting false channel condition. This issue is partially addressed in the work of Racic et al.  in a limited network setting. They consider a particular scheduler in a cellular network with handover process and propose a secure handover algorithm. In contrast, we reveal the possible effects of false channel condition reporting in various channel-aware network protocols and propose a primitive defense mechanism that provides secure channel condition estimation.

Our contributions are:

 • We analyze specific attack mechanisms and evaluate the effects of misreporting channel condition on various channel-aware wireless network protocols including cooperative relaying protocols, routing metrics in wireless ad-hoc network and opportunistic schedulers.

• We propose a secure channel condition estimation algorithm that can be used to construct a secure channel-aware protocol in single-hop settings.

• We analyze our algorithm in the respects of performance and security, and we perform a simulation study to understand the impact of our algorithm on system performance.

The false channel condition reporting attack that we introduce in this paper is difficult to identify by existing mechanisms, since our attack is mostly protocol compliant; only the channel-condition measurement mechanism need to be modified. Our attack can thus be performed using modified user equipment legitimately registered to a network.

1.3 LITRATURE SURVEY

EXPLOITING AND DEFENDING OPPORTUNISTIC SCHEDULING IN CELLULAR DATA NETWORKS

PUBLICATION: R. Racic, D. Ma, H. Chen, and X. Liu, IEEE Trans. Mobile Comput., vol. 9, no. 5, pp. 609–620, May 2010.

Third Generation (3G) cellular networks take advantage of time-varying and location-dependent channel conditions of mobile users to provide broadband services. Under fairness and QoS constraints, they use opportunistic scheduling to efficiently utilize the available spectrum. Opportunistic scheduling algorithms rely on the collaboration among all mobile users to achieve their design objectives. However, we demonstrate that rogue cellular devices can exploit vulnerabilities in popular opportunistic scheduling algorithms, such as Proportional Fair (PF) and Temporal Fair (TF), to usurp the majority of time slots in 3G networks. Our simulations show that under realistic conditions, only five rogue device per 50-user cell can capture up to 95 percent of the time slots, and can cause 2-second end-to-end interpacket transmission delay on VoIP applications for every user in the same cell, rendering VoIP applications useless. To defend against this attack, we propose strengthening the PF and TF schedulers and a robust handoff scheme.

ON THE VULNERABILITY OF THE PROPORTIONAL FAIRNESS SCHEDULER TO RETRANSMISSION ATTACKS

PUBLICATION: U. Ben-Porat, A. Bremler-Barr, H. Levy, and B. Plattner, in Proc. IEEE INFOCOM, Shanghai, China, Apr. 2011, pp. 1431–1439.

Channel aware schedulers of modern wireless networks – such as the popular Proportional Fairness Scheduler (PFS) – improve throughput performance by exploiting channel fluctuations while maintaining fairness among the users. In order to simplify the analysis, PFS was introduced and vastly investigated in a model where frame losses do not occur, which is of course not the case in practical wireless networks. Recent studies focused on the efficiency of various implementations of PFS in a realistic model where frame losses can occur. In this work we show that the common straight forward adaptation of PFS to frame losses exposes the system to a malicious attack (which can alternatively be caused by malfunctioning user equipment) that can drastically degrade the performance of innocent users. We analyze the factors behind the vulnerability of the system and propose a modification of PFS designed for the frame loss model which is resilient to such malicious attack while maintaining the fairness properties of original PFS.

A MEASUREMENT STUDY OF SCHEDULER-BASED ATTACKS IN 3G WIRELESS NETWORKS

PUBLICATION: S. Bali, S. Machiraju, H. Zang, and V. Frost, in Proc. PAM, Berlin, Germany, 2007.

Though high-speed (3G) wide-area wireless networks have been rapidly proliferating, little is known about the robustness and security properties of these networks. In this paper, we make initial steps towards understanding these properties by studying Proportional Fair (PF), the scheduling algorithm used on the downlinks of these networks. We find that the fairness-ensuring mechanism of PF can be easily corrupted by a malicious user to monopolize the wireless channel thereby starving other users. Using extensive experiments on commercial and laboratory-based CDMA networks, we demonstrate this vulnerability and quantify the resulting performance impact. We find that delay jitter can be increased by up to 1 second and TCP throughput can be reduced by as much as 25−30 % by a single malicious user. Based on our results, we argue for the need to use a more robust scheduling algorithm and outline one such algorithm. 1

CHAPTER 2

2.0 SYSTEM ANALYSIS

2.1 EXISTING SYSTEM:

Many protocols in modern wireless networks treat a link’s channel condition information as a protocol input parameter; we call such protocols channel-aware. Examples include cooperative relaying network architectures, efficient ad hoc network routing metrics, and opportunistic schedulers. While work on channel-aware protocols has mainly focused on how channel condition information can be used to more efficiently utilize wireless resources, security aspects of channel-aware protocols have only recently been studied. These works on security of channel-aware protocols revealed new threats in specific network environments by simulation or measurement. However, under-standing the effect of possible attacks across varied network environments is still an open area for study.

2.1.1 DISADVANTAGES:

• Difficult to guarantee QoS in MANETs due to their unique features including user mobility, channel variance errors, and limited bandwidth.
• Although these protocols can increase the QoS of the MANETs to a certain extent, they suffer from invalid reservation and race condition problems.

2.2 PROPOSED SYSTEM:

We introduce our attack concept and perform case studies to quantize the attack effects on specific channel-aware network protocols. Depending on deployed PHY-layer technologies (e.g. OFDM), a system can utilize conditions for subchannels to perform more efficient frequency-selective scheduling. Our work can apply for this case by handling each subchannel condition information separately. However, for clarity of presentation, we consider a single channel between network participants in this paper.

We can easily implement false channel condition reporting attack by modifying only a subcomponent to report channel condition. This subcomponent of user equipment can be implemented in hardware or software. One recent trend of user equipment implementation is to increasingly move hardware part to software part for adaptable configuration of a general hardware. The increasing software control of user equipment makes false channel condition reporting attack an increasingly practical attack.

2.2.1 ADVANTAGES:

• We analyze specific attack mechanisms and evaluate the effects of misreporting channel condition on various channel-aware wireless network protocols including cooperative relaying protocols, routing metrics in wireless ad-hoc network and opportunistic schedulers.

• We propose a secure channel condition estimation algorithm that can be used to construct a secure channel-aware protocol in single-hop settings.

• We analyze our algorithm in the respects of performance and security, and we perform a simulation study to understand the impact of our algorithm on system performance.

2.3 HARDWARE & SOFTWARE REQUIREMENTS:

2.3.1 HARDWARE REQUIREMENT:

v    Processor                                 –    Pentium –IV

• Speed                                      –    1.1 GHz
  • RAM                                       –    256 MB (min)
  • Hard Disk                               –   20 GB
  • Floppy Drive                           –    1.44 MB
  • Key Board                              –    Standard Windows Keyboard
  • Mouse                                     –    Two or Three Button Mouse
  • Monitor                                   –    SVGA

 

2.3.2 SOFTWARE REQUIREMENTS:

• Operating System                   :           Windows XP or Win 7
• Front End                                :           Java JDK 1.7
• Back End                                :           MS-ACCESS
• Tools                                       :           Netbeans IDE 7
• Document                               :           MS-Office 2007

CHAPTER 3

3.0 SYSTEM DESIGN:

Data Flow Diagram / Use Case Diagram / Flow Diagram:

• The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of the input data to the system, various processing carried out on these data, and the output data is generated by the system

• The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. These components are the system process, the data used by the process, an external entity that interacts with the system and the information flows in the system.

• DFD shows how the information moves through the system and how it is modified by a series of transformations. It is a graphical technique that depicts information flow and the transformations that are applied as data moves from input to output.

• DFD is also known as bubble chart. A DFD may be used to represent a system at any level of abstraction. DFD may be partitioned into levels that represent increasing information flow and functional detail.

NOTATION:

SOURCE OR DESTINATION OF DATA:

External sources or destinations, which may be people or organizations or other entities

 

DATA SOURCE:

Here the data referenced by a process is stored and retrieved.

 

PROCESS:

People, procedures or devices that produce data. The physical component is not identified.

DATA FLOW:

Data moves in a specific direction from an origin to a destination. The data flow is a “packet” of data.

MODELING RULES:

There are several common modeling rules when creating DFDs:

1. All processes must have at least one data flow in and one data flow out.
2. All processes should modify the incoming data, producing new forms of outgoing data.
3. Each data store must be involved with at least one data flow.
4. Each external entity must be involved with at least one data flow.
5. A data flow must be attached to at least one process.

3.1 ARCHITECTURE DIAGRAM:

3.2 DATAFLOW DIAGRAM:

UML DIAGRAMS:

3.2 USE CASE DIAGRAM:

3.3 CLASS DIAGRAM:

3.4 SEQUENCE DIAGRAM:

3.5 ACTIVITY DIAGRAM:

 

CHAPTER 4

4.0 IMPLEMENTATION:

We performed a simulation study to evaluate the overclaiming attack’s effect on the normal users’ performance in a cooperative relaying environment. We quantifyWe use the ns-2 simulator patched with EURANE UMTS system simulator. Our simulated network consists of one base station serving four users. The base station sends 11Mbps of Constant Bit Rate (CBR) traffic to each user. There is one attacker who may falsely report its channel condition to the base station. We represent channel condition using the Channel Quality Indicator (CQI) defined in the 3GPP standard.

The same equation and block size are used for a case study of opportunistic scheduler presented in Section 2.2.3. We assume that one victim is close to the attacker so that when the victim experiences poor channel condition, the victim can use the attacker as a relaying node. The other two normal users get packets directly from the base station and do not participate in the cooperative relaying protocol. The victim and the two normal users honestly report their channel condition. The channel for the attacker and two normal users uses a shadowing plus Rayleigh model of a moving node 100m away from the base station with velocity of 3km/h. We vary the victim’s distance to the base station from 100m to 500m to see the effect of the victim’s channel condition on the performance degradation.

The attacker’s goal in this simulation is to reduce the victim’s throughput. The attacker can adopt two approaches. In the conservative approach, the attacker does not forward packets for the victim without falsely reporting its channel condition. In the aggressive approach, the attacker overclaims its channel condition so that the attacker can increase its probability of relaying packets for the victim.

Our simulations do not consider the overhead that an actual relaying protocol might incur in finding a new relaying node due to channel condition variation since such overhead is not related to the effect of attack. We assume that each transmission uses an orthogonal carrier so that transmissions do not interfere with each other.

Our simulations do not implement a relay discovery protocol; rather, we compare the attacker and victim CQI, and use the link with better CQI value to transmit to the victim. This relaying scheme is an example; a system operator may choose a different scheme. However, the scheme that we chose is good for exploiting increased diversity to optimize throughput. We ran each of our simulations for 100 simulated seconds.

4.1 ALGORITHM

In this section, we evaluate the performance and the security of our algorithm. Firstly, we analyze the performance of our algorithm according to algorithm parameters. This analysis can be used for parameter design guidelines. This analysis result is compared to simulation results.   Secondly, we analyze the security of our algorithm. In this analysis, we show how much a brute-force attacker can be successful in guessing the value included in a challenge according to algorithm parameters. This analysis can be used for understanding tradeoff between security and system overhead. Thirdly, we integrate our algorithm into a network simulator and evaluate the effect of our algorithm on the system performance. We show that our algorithm securely and effectively estimates channel condition through most of its parameter space.

We used identical parameters, except we replaced the static channel with various variable channel models. We measure the throughput of normal users under scheduling policies of MAX-SINR, PF and MAX-SINR with our algorithm. In MAX-SINR with our algorithm, a base station does not use reported CQI-level to determine a user with the best channel condition in a give time slot. Instead, the base station uses CQI-level estimated by our algorithm. In the case study of opportunistic scheduler, our observation was that PF scheduler prevented attackers from stealing throughput. Hence, we concluded that PF was a good candidate for defending against false channel condition reporting attack. However, our simulation results for the system performance show that MAX-SINR with our algorithm can achieve higher throughput than PF scheduler in most cases. The fact that the performance of our algorithm depends on channel characteristic affects the throughput of normal users in case of MAX-SINR with our algorithm.

4.2 MODULE DESCRIPTION:

SERVER CLIENT MODULE:

NETWORK SECURITY:

• ATTACK MODEL:

COOPERATIVE RELAYING:

EFFICIENT ROUTING METRICS:

PERFORMANCE ANALYSIS

4.3 MODULES DESCRIPTION:

SERVER CLIENT MODULE:

Client-server computing or networking is a distributed application architecture that partitions tasks or workloads between service providers (servers) and service requesters, called clients. Often clients and servers operate over a computer network on separate hardware. A server machine is a high-performance host that is running one or more server programs which share its resources with clients. A client also shares any of its resources; Clients therefore initiate communication sessions with servers which await (listen to) incoming requests.

NETWORK SECURITY:

Network-accessible resources may be deployed in a network as surveillance and early-warning tools, as the detection of attackers are not normally accessed for legitimate purposes. Techniques used by the attackers that attempt to compromise these decoy resources are studied during and after an attack to keep an eye on new exploitation techniques. Such analysis may be used to further tighten security of the actual network being protected by the data’s. Data forwarding can also direct an attacker’s attention away from legitimate servers. A user encourages attackers to spend their time and energy on the decoy server while distracting their attention from the data on the real server. Similar to a server, a user is a network set up with intentional vulnerabilities. Its purpose is also to invite attacks so that the attacker’s methods can be studied and that information can be used to increase network security.

ATTACK MODEL:

We introduce our attack concept and perform case studies to quantize the attack effects on specific channel-aware network protocols. We evaluate the effect of falsely reported channel condition under three types of channel-aware protocols: cooperative relaying protocols in hybrid networks, efficient routing metrics in wireless ad hoc networks, and opportunistic schedulers in high-speed wireless networks. For each protocol, we suggest possible attack mechanisms and quantify the effectiveness of the attack. We show that we can defend against some attacks using existing algorithms, and that other attacks require new security mechanisms. Each following case study has the same presentation format. First, we briefly explain the protocols. Then, we discuss effective attack scenarios for each protocol. Finally, we use simulations to evaluate the effect of each attack scenario.

COOPERATIVE RELAYING:

In a mobile wireless network, mobile nodes can experience different channel conditions depending on their different locations. When a node experiences a channel condition that is too poor to receive packets from a source node, a third node may have a good channel condition to both the source and the intended destination. Cooperative relaying network architectures help a node that has poor channel condition to route its packet through a node with a good channel condition, thus improving system throughput.

A cooperative relaying protocol must distribute channel condition information for each candidate path, find the most appropriate relay path, and provide incentives to motivate nodes to forward packets for other nodes. Specifically, in UCAN user equipment has two wireless adaptors, one High Data Rate (HDR) cellular interface and one IEEE 802.11 interface. The HDR interface is used for communication with a base station and the IEEE 802.11 interface is used for peer-to-peer communication with other user equipment in a network.

EFFICIENT ROUTING METRICS:

Routing Protocols in Ad Hoc Networks a wireless ad hoc network supports communication between nodes without need for centralized infrastructure such as base stations or access points. To deliver packets to destinations out of a source node’s transmission range, the source employs the help of intermediate nodes to forward each packet to its destination. Routing protocols in wireless ad hoc network discover routes between nodes. When there are multiple valid routes from a source to a destination, a routing protocol needs to choose among valid routes. A routing metric is a value associated to a route and represents the desirability of a route. A typical metric in the seminal routing protocols is minimum hop count. The rationale behind the metric of minimum hop count is that a route with fewer hops allows a packet to be delivered with the smaller number of transmissions.

PERFORMANCE ANALYSIS:

Our analysis assumes that the channel condition does not change. Though this assumption does not hold in a mobile environment, the purpose of our analysis is not to capture every detail of real world but to verify our simulator. For the evaluation in a realistic environment, we perform simulations with channel models considering variable channel conditions, as described in that the challenge size and Psref (i) are the same for different challenges for easy comparison of performance. The equations in our analysis do not assume the same values of challenge size and Psref (i). However, with different values of challenge size and Psref (i), it is not easy to understand the parameters’ effect on the performance. In this analysis, we assume that the challenges are authenticated.

CHAPTER 5

5.0 SYSTEM STUDY:

5.1 FEASIBILITY STUDY:

The feasibility of the project is analyzed in this phase and business proposal is put forth with a very general plan for the project and some cost estimates. During system analysis the feasibility study of the proposed system is to be carried out. This is to ensure that the proposed system is not a burden to the company.  For feasibility analysis, some understanding of the major requirements for the system is essential.

Three key considerations involved in the feasibility analysis are 

• ECONOMICAL FEASIBILITY
• TECHNICAL FEASIBILITY
• SOCIAL FEASIBILITY

5.1.1 ECONOMICAL FEASIBILITY:     

This study is carried out to check the economic impact that the system will have on the organization. The amount of fund that the company can pour into the research and development of the system is limited. The expenditures must be justified. Thus the developed system as well within the budget and this was achieved because most of the technologies used are freely available. Only the customized products had to be purchased.

 

5.1.2 TECHNICAL FEASIBILITY   

This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.  

5.1.3 SOCIAL FEASIBILITY:  

The aspect of study is to check the level of acceptance of the system by the user. This includes the process of training the user to use the system efficiently. The user must not feel threatened by the system, instead must accept it as a necessity. The level of acceptance by the users solely depends on the methods that are employed to educate the user about the system and to make him familiar with it. His level of confidence must be raised so that he is also able to make some constructive criticism, which is welcomed, as he is the final user of the system.

5.2 SYSTEM TESTING:

Testing is a process of checking whether the developed system is working according to the original objectives and requirements. It is a set of activities that can be planned in advance and conducted systematically. Testing is vital to the success of the system. System testing makes a logical assumption that if all the parts of the system are correct, the global will be successfully achieved. In adequate testing if not testing leads to errors that may not appear even many months.

This creates two problems, the time lag between the cause and the appearance of the problem and the effect of the system errors on the files and records within the system. A small system error can conceivably explode into a much larger Problem. Effective testing early in the purpose translates directly into long term cost savings from a reduced number of errors. Another reason for system testing is its utility, as a user-oriented vehicle before implementation. The best programs are worthless if it produces the correct outputs.

5.2.1 UNIT TESTING:

Description	Expected result
Test for application window properties.	All the properties of the windows are to be properly aligned and displayed.
Test for mouse operations.	All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions.

A program represents the logical elements of a system. For a program to run satisfactorily, it must compile and test data correctly and tie in properly with other programs. Achieving an error free program is the responsibility of the programmer. Program  testing  checks  for  two  types  of  errors:  syntax  and  logical. Syntax error is a program statement that violates one or more rules of the language in which it is written. An improperly defined field dimension or omitted keywords are common syntax errors. These errors are shown through error message generated by the computer. For Logic errors the programmer must examine the output carefully.

5.1.2 FUNCTIONAL TESTING:

Functional testing of an application is used to prove the application delivers correct results, using enough inputs to give an adequate level of confidence that will work correctly for all sets of inputs. The functional testing will need to prove that the application works for each client type and that personalization function work correctly.When a program is tested, the actual output is compared with the expected output. When there is a discrepancy the sequence of instructions must be traced to determine the problem.  The process is facilitated by breaking the program into self-contained portions, each of which can be checked at certain key points. The idea is to compare program values against desk-calculated values to isolate the problems.

Description	Expected result
Test for all modules.	All peers should communicate in the group.
Test for various peer in a distributed network framework as it display all users available in the group.	The result after execution should give the accurate result.

5.1. 3 NON-FUNCTIONAL TESTING:

 The Non Functional software testing encompasses a rich spectrum of testing strategies, describing the expected results for every test case. It uses symbolic analysis techniques. This testing used to check that an application will work in the operational environment. Non-functional testing includes:

• Load testing
• Performance testing
• Usability testing
• Reliability testing
• Security testing

5.1.4 LOAD TESTING:

An important tool for implementing system tests is a Load generator. A Load generator is essential for testing quality requirements such as performance and stress. A load can be a real load, that is, the system can be put under test to real usage by having actual telephone users connected to it. They will generate test input data for system test.

Description	Expected result
It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received.	Should designate another active node as a Server.

5.1.5 PERFORMANCE TESTING:

Performance tests are utilized in order to determine the widely defined performance of the software system such as execution time associated with various parts of the code, response time and device utilization. The intent of this testing is to identify weak points of the software system and quantify its shortcomings.

Description	Expected result
This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management.	Should handle large input values, and produce accurate result in a  expected time.

5.1.6 RELIABILITY TESTING:

The software reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time and it is being ensured in this testing. Reliability can be expressed as the ability of the software to reveal defects under testing conditions, according to the specified requirements. It the portability that a software system will operate without failure under given conditions for a given time interval and it focuses on the behavior of the software element. It forms a part of the software quality control team.

Description	Expected result
This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application.	In case of failure of  the server an alternate server should take over the job.

5.1.7 SECURITY TESTING:

Security testing evaluates system characteristics that relate to the availability, integrity and confidentiality of the system data and services. Users/Clients should be encouraged to make sure their security needs are very clearly known at requirements time, so that the security issues can be addressed by the designers and testers.

Description	Expected result
Checking that the user identification is authenticated.	In case failure it should not be connected in the framework.
Check whether group keys in a tree are shared by all peers.	The peers should know group key in the same group.

5.1.8 WHITE BOX TESTING:

White  box  testing,  sometimes called  glass-box  testing is  a test  case  design method  that  uses  the  control  structure  of the procedural  design  to  derive  test  cases. Using  white  box  testing  method,  the software  engineer  can  derive  test  cases. The White box testing focuses on the inner structure of the software structure to be tested.

Description	Expected result
Exercise all logical decisions on their true and false sides.	All the logical decisions must be valid.
Execute all loops at their boundaries and within their operational bounds.	All the loops must be finite.
Exercise internal data structures to ensure their validity.	All the data structures must be valid.

5.1.9 BLACK BOX TESTING:

Black box testing, also called behavioral testing, focuses on the functional requirements of the software.  That  is,  black  testing  enables  the software engineer  to  derive  sets  of  input  conditions  that  will  fully  exercise  all  functional requirements  for  a  program.  Black box testing is not alternative to white box techniques.  Rather  it  is  a  complementary  approach  that  is  likely  to  uncover  a different  class  of  errors  than  white box  methods. Black box testing attempts to find errors which focuses on inputs, outputs, and principle function of a software module. The starting point of the black box testing is either a specification or code. The contents of the box are hidden and the stimulated software should produce the desired results.

Description	Expected result
To check for incorrect or missing functions.	All the functions must be valid.
To check for interface errors.	The entire interface must function normally.
To check for errors in a data structures or external data base access.	The database updation and retrieval must be done.
To check for initialization and termination errors.	All the functions and data structures must be initialized properly and terminated normally.

All the above system testing strategies are carried out in as the development, documentation and institutionalization of the proposed goals and related policies is essential.

CHAPTER 6

6.0 SOFTWARE DESCRIPTION:

 

6.1 JAVA TECHNOLOGY:

Java technology is both a programming language and a platform.

 

The Java Programming Language

 

The Java programming language is a high-level language that can be characterized by all of the following buzzwords:

• Simple
  • Architecture neutral
  • Object oriented
  • Portable
  • Distributed     
  • High performance
  • Interpreted     
  • Multithreaded
  • Robust
  • Dynamic
  • Secure     

With most programming languages, you either compile or interpret a program so that you can run it on your computer. The Java programming language is unusual in that a program is both compiled and interpreted. With the compiler, first you translate a program into an intermediate language called Java byte codes —the platform-independent codes interpreted by the interpreter on the Java platform. The interpreter parses and runs each Java byte code instruction on the computer. Compilation happens just once; interpretation occurs each time the program is executed. The following figure illustrates how this works.

You can think of Java byte codes as the machine code instructions for the Java Virtual Machine (Java VM). Every Java interpreter, whether it’s a development tool or a Web browser that can run applets, is an implementation of the Java VM. Java byte codes help make “write once, run anywhere” possible. You can compile your program into byte codes on any platform that has a Java compiler. The byte codes can then be run on any implementation of the Java VM. That means that as long as a computer has a Java VM, the same program written in the Java programming language can run on Windows 2000, a Solaris workstation, or on an iMac.

6.2 THE JAVA PLATFORM:

A platform is the hardware or software environment in which a program runs. We’ve already mentioned some of the most popular platforms like Windows 2000, Linux, Solaris, and MacOS. Most platforms can be described as a combination of the operating system and hardware. The Java platform differs from most other platforms in that it’s a software-only platform that runs on top of other hardware-based platforms.

The Java platform has two components:

• The Java Virtual Machine (Java VM)
• The Java Application Programming Interface (Java API)

You’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported onto various hardware-based platforms.

The Java API is a large collection of ready-made software components that provide many useful capabilities, such as graphical user interface (GUI) widgets. The Java API is grouped into libraries of related classes and interfaces; these libraries are known as packages. The next section, What Can Java Technology Do? Highlights what functionality some of the packages in the Java API provide.

The following figure depicts a program that’s running on the Java platform. As the figure shows, the Java API and the virtual machine insulate the program from the hardware.

Native code is code that after you compile it, the compiled code runs on a specific hardware platform. As a platform-independent environment, the Java platform can be a bit slower than native code. However, smart compilers, well-tuned interpreters, and just-in-time byte code compilers can bring performance close to that of native code without threatening portability.

6.3 WHAT CAN JAVA TECHNOLOGY DO?

The most common types of programs written in the Java programming language are applets and applications. If you’ve surfed the Web, you’re probably already familiar with applets. An applet is a program that adheres to certain conventions that allow it to run within a Java-enabled browser.

However, the Java programming language is not just for writing cute, entertaining applets for the Web. The general-purpose, high-level Java programming language is also a powerful software platform. Using the generous API, you can write many types of programs.

An application is a standalone program that runs directly on the Java platform. A special kind of application known as a server serves and supports clients on a network. Examples of servers are Web servers, proxy servers, mail servers, and print servers. Another specialized program is a servlet.

A servlet can almost be thought of as an applet that runs on the server side. Java Servlets are a popular choice for building interactive web applications, replacing the use of CGI scripts. Servlets are similar to applets in that they are runtime extensions of applications. Instead of working in browsers, though, servlets run within Java Web servers, configuring or tailoring the server.

How does the API support all these kinds of programs? It does so with packages of software components that provides a wide range of functionality. Every full implementation of the Java platform gives you the following features:

• The essentials: Objects, strings, threads, numbers, input and output, data structures, system properties, date and time, and so on.
• Applets: The set of conventions used by applets.
• Networking: URLs, TCP (Transmission Control Protocol), UDP (User Data gram Protocol) sockets, and IP (Internet Protocol) addresses.
• Internationalization: Help for writing programs that can be localized for users worldwide. Programs can automatically adapt to specific locales and be displayed in the appropriate language.
• Security: Both low level and high level, including electronic signatures, public and private key management, access control, and certificates.
• Software components: Known as JavaBeans^TM, can plug into existing component architectures.
• Object serialization: Allows lightweight persistence and communication via Remote Method Invocation (RMI).
• Java Database Connectivity (JDBC^TM): Provides uniform access to a wide range of relational databases.

The Java platform also has APIs for 2D and 3D graphics, accessibility, servers, collaboration, telephony, speech, animation, and more. The following figure depicts what is included in the Java 2 SDK.

 

6.4 HOW WILL JAVA TECHNOLOGY CHANGE MY LIFE?

We can’t promise you fame, fortune, or even a job if you learn the Java programming language. Still, it is likely to make your programs better and requires less effort than other languages. We believe that Java technology will help you do the following:

• Get started quickly: Although the Java programming language is a powerful object-oriented language, it’s easy to learn, especially for programmers already familiar with C or C++.
• Write less code: Comparisons of program metrics (class counts, method counts, and so on) suggest that a program written in the Java programming language can be four times smaller than the same program in C++.
• Write better code: The Java programming language encourages good coding practices, and its garbage collection helps you avoid memory leaks. Its object orientation, its JavaBeans component architecture, and its wide-ranging, easily extendible API let you reuse other people’s tested code and introduce fewer bugs.
• Develop programs more quickly: Your development time may be as much as twice as fast versus writing the same program in C++. Why? You write fewer lines of code and it is a simpler programming language than C++.
• Avoid platform dependencies with 100% Pure Java: You can keep your program portable by avoiding the use of libraries written in other languages. The 100% Pure Java^TM Product Certification Program has a repository of historical process manuals, white papers, brochures, and similar materials online.
• Write once, run anywhere: Because 100% Pure Java programs are compiled into machine-independent byte codes, they run consistently on any Java platform.
• Distribute software more easily: You can upgrade applets easily from a central server. Applets take advantage of the feature of allowing new classes to be loaded “on the fly,” without recompiling the entire program.

 

6.5 ODBC:

 

Microsoft Open Database Connectivity (ODBC) is a standard programming interface for application developers and database systems providers. Before ODBC became a de facto standard for Windows programs to interface with database systems, programmers had to use proprietary languages for each database they wanted to connect to. Now, ODBC has made the choice of the database system almost irrelevant from a coding perspective, which is as it should be. Application developers have much more important things to worry about than the syntax that is needed to port their program from one database to another when business needs suddenly change.

Through the ODBC Administrator in Control Panel, you can specify the particular database that is associated with a data source that an ODBC application program is written to use. Think of an ODBC data source as a door with a name on it. Each door will lead you to a particular database. For example, the data source named Sales Figures might be a SQL Server database, whereas the Accounts Payable data source could refer to an Access database. The physical database referred to by a data source can reside anywhere on the LAN.

The ODBC system files are not installed on your system by Windows 95. Rather, they are installed when you setup a separate database application, such as SQL Server Client or Visual Basic 4.0. When the ODBC icon is installed in Control Panel, it uses a file called ODBCINST.DLL. It is also possible to administer your ODBC data sources through a stand-alone program called ODBCADM.EXE. There is a 16-bit and a 32-bit version of this program and each maintains a separate list of ODBC data sources.

From a programming perspective, the beauty of ODBC is that the application can be written to use the same set of function calls to interface with any data source, regardless of the database vendor. The source code of the application doesn’t change whether it talks to Oracle or SQL Server. We only mention these two as an example. There are ODBC drivers available for several dozen popular database systems. Even Excel spreadsheets and plain text files can be turned into data sources. The operating system uses the Registry information written by ODBC Administrator to determine which low-level ODBC drivers are needed to talk to the data source (such as the interface to Oracle or SQL Server). The loading of the ODBC drivers is transparent to the ODBC application program. In a client/server environment, the ODBC API even handles many of the network issues for the application programmer.

The advantages of this scheme are so numerous that you are probably thinking there must be some catch. The only disadvantage of ODBC is that it isn’t as efficient as talking directly to the native database interface. ODBC has had many detractors make the charge that it is too slow. Microsoft has always claimed that the critical factor in performance is the quality of the driver software that is used. In our humble opinion, this is true. The availability of good ODBC drivers has improved a great deal recently. And anyway, the criticism about performance is somewhat analogous to those who said that compilers would never match the speed of pure assembly language. Maybe not, but the compiler (or ODBC) gives you the opportunity to write cleaner programs, which means you finish sooner. Meanwhile, computers get faster every year.

6.6 JDBC:

In an effort to set an independent database standard API for Java; Sun Microsystems developed Java Database Connectivity, or JDBC. JDBC offers a generic SQL database access mechanism that provides a consistent interface to a variety of RDBMSs. This consistent interface is achieved through the use of “plug-in” database connectivity modules, or drivers. If a database vendor wishes to have JDBC support, he or she must provide the driver for each platform that the database and Java run on.

To gain a wider acceptance of JDBC, Sun based JDBC’s framework on ODBC. As you discovered earlier in this chapter, ODBC has widespread support on a variety of platforms. Basing JDBC on ODBC will allow vendors to bring JDBC drivers to market much faster than developing a completely new connectivity solution.

JDBC was announced in March of 1996. It was released for a 90 day public review that ended June 8, 1996. Because of user input, the final JDBC v1.0 specification was released soon after.

The remainder of this section will cover enough information about JDBC for you to know what it is about and how to use it effectively. This is by no means a complete overview of JDBC. That would fill an entire book.

 

6.7 JDBC Goals:

Few software packages are designed without goals in mind. JDBC is one that, because of its many goals, drove the development of the API. These goals, in conjunction with early reviewer feedback, have finalized the JDBC class library into a solid framework for building database applications in Java.

The goals that were set for JDBC are important. They will give you some insight as to why certain classes and functionalities behave the way they do. The eight design goals for JDBC are as follows:

SQL Level API

The designers felt that their main goal was to define a SQL interface for Java. Although not the lowest database interface level possible, it is at a low enough level for higher-level tools and APIs to be created. Conversely, it is at a high enough level for application programmers to use it confidently. Attaining this goal allows for future tool vendors to “generate” JDBC code and to hide many of JDBC’s complexities from the end user.

SQL Conformance

SQL syntax varies as you move from database vendor to database vendor. In an effort to support a wide variety of vendors, JDBC will allow any query statement to be passed through it to the underlying database driver. This allows the connectivity module to handle non-standard functionality in a manner that is suitable for its users.

JDBC must be implemental on top of common database interfaces

The JDBC SQL API must “sit” on top of other common SQL level APIs. This goal allows JDBC to use existing ODBC level drivers by the use of a software interface. This interface would translate JDBC calls to ODBC and vice versa.

1. Provide a Java interface that is consistent with the rest of the Java system

Because of Java’s acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system.

• Keep it simple

This goal probably appears in all software design goal listings. JDBC is no exception. Sun felt that the design of JDBC should be very simple, allowing for only one method of completing a task per mechanism. Allowing duplicate functionality only serves to confuse the users of the API.

• Use strong, static typing wherever possible

Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.

• Keep the common cases simple

Because more often than not, the usual SQL calls used by the programmer are simple SELECT’s, INSERT’s, DELETE’s and UPDATE’s, these queries should be simple to perform with JDBC. However, more complex SQL statements should also be possible.

Finally we decided to precede the implementation using Java Networking.

And for dynamically updating the cache table we go for MS Access database.

Java ha two things: a programming language and a platform.

Java is a high-level programming language that is all of the following

Simple                                     Architecture-neutral

Object-oriented                       Portable

Distributed                              High-performance

Interpreted                              Multithreaded

Robust                                     Dynamic Secure

Java is also unusual in that each Java program is both compiled and interpreted. With a compile you translate a Java program into an intermediate language called Java byte codes the platform-independent code instruction is passed and run on the computer.

Compilation happens just once; interpretation occurs each time the program is executed. The figure illustrates how this works.

6.7 NETWORKING TCP/IP STACK:

The TCP/IP stack is shorter than the OSI one:

TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.

IP datagram’s:

The IP layer provides a connectionless and unreliable delivery system. It considers each datagram independently of the others. Any association between datagram must be supplied by the higher layers. The IP layer supplies a checksum that includes its own header. The header includes the source and destination addresses. The IP layer handles routing through an Internet. It is also responsible for breaking up large datagram into smaller ones for transmission and reassembling them at the other end.

UDP:

UDP is also connectionless and unreliable. What it adds to IP is a checksum for the contents of the datagram and port numbers. These are used to give a client/server model – see later.

TCP:

TCP supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.

Internet addresses

In order to use a service, you must be able to find it. The Internet uses an address scheme for machines so that they can be located. The address is a 32 bit integer which gives the IP address.

Network address:

Class A uses 8 bits for the network address with 24 bits left over for other addressing. Class B uses 16 bit network addressing. Class C uses 24 bit network addressing and class D uses all 32.

Subnet address:

Internally, the UNIX network is divided into sub networks. Building 11 is currently on one sub network and uses 10-bit addressing, allowing 1024 different hosts.

Host address:

8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.

Total address:

The 32 bit address is usually written as 4 integers separated by dots.

Port addresses

A service exists on a host, and is identified by its port. This is a 16 bit number. To send a message to a server, you send it to the port for that service of the host that it is running on. This is not location transparency! Certain of these ports are “well known”.

Sockets:

A socket is a data structure maintained by the system to handle network connections. A socket is created using the call socket. It returns an integer that is like a file descriptor. In fact, under Windows, this handle can be used with Read File and Write File functions.

#include <sys/types.h>

#include <sys/socket.h>

int socket(int family, int type, int protocol);

Here “family” will be AF_INET for IP communications, protocol will be zero, and type will depend on whether TCP or UDP is used. Two processes wishing to communicate over a network create a socket each. These are similar to two ends of a pipe – but the actual pipe does not yet exist.

6.8 JFREE CHART:

JFreeChart is a free 100% Java chart library that makes it easy for developers to display professional quality charts in their applications. JFreeChart’s extensive feature set includes:

A consistent and well-documented API, supporting a wide range of chart types;

A flexible design that is easy to extend, and targets both server-side and client-side applications;

Support for many output types, including Swing components, image files (including PNG and JPEG), and vector graphics file formats (including PDF, EPS and SVG);

JFreeChart is “open source” or, more specifically, free software. It is distributed under the terms of the GNU Lesser General Public Licence (LGPL), which permits use in proprietary applications.

 

6.8.1. Map Visualizations:

Charts showing values that relate to geographical areas. Some examples include: (a) population density in each state of the United States, (b) income per capita for each country in Europe, (c) life expectancy in each country of the world. The tasks in this project include: Sourcing freely redistributable vector outlines for the countries of the world, states/provinces in particular countries (USA in particular, but also other areas);

Creating an appropriate dataset interface (plus default implementation), a rendered, and integrating this with the existing XYPlot class in JFreeChart; Testing, documenting, testing some more, documenting some more.

6.8.2. Time Series Chart Interactivity

Implement a new (to JFreeChart) feature for interactive time series charts — to display a separate control that shows a small version of ALL the time series data, with a sliding “view” rectangle that allows you to select the subset of the time series data to display in the main chart.

6.8.3. Dashboards

There is currently a lot of interest in dashboard displays. Create a flexible dashboard mechanism that supports a subset of JFreeChart chart types (dials, pies, thermometers, bars, and lines/time series) that can be delivered easily via both Java Web Start and an applet.

 

6.8.4. Property Editors

The property editor mechanism in JFreeChart only handles a small subset of the properties that can be set for charts. Extend (or reimplement) this mechanism to provide greater end-user control over the appearance of the charts.

CHAPTER 7

APPENDIX

7.1 SAMPLE SOURCE CODE

7.2 SAMPLE OUTPUT

CHAPTER 8

8.1 CONCLUSION

In this paper, we have studied the threat imposed by falsely reporting users’ channel condition. Through case studies for three different types of wireless network protocols, we show that in a cooperative relaying network and a network using ETX, a false reporting attack can significantly reduce the performance of other users. Our false channel-feedback attack can arise in any channel-aware protocol where a user reports its own channel condition. To counter such attacks, we propose a secure channel condition estimation algorithm to prevent the overclaiming attack. Through analysis and simulations, we show that with proper parameters, we can prevent the over claiming attack.

CHAPTER 9

9.1 REFERENCES

1. Dongho Kim and Yih-Chun Hu, “A Study on False Channel Condition Reporting Attacks in Wireless Networks”, IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 13, NO. 5, MAY 2014.

• H. Luo, R. Ramjee, P. Sinha, L. E. Li, and S. Lu, “Ucan: A unified cellular and ad-hoc network architecture,” in Proc. ACM MobiCom, San Diego, CA, USA, 2003, pp. 353–367.

• D. S. J. De Couto, D. Aguayo, J. Bicket, and R. Morris, “A highthroughput path metric for multi-hop wireless routing,” in Proc. ACM MobiCom, San Diego, CA, USA, 2003, pp. 134–146.

• R. Draves, J. Padhye, and B. Zill, “Comparison of routing metrics for static multi-hop wireless networks,” in Proc. ACM SIGCOMM, Portland, OR, USA, 2004, pp. 133–144.

• A. Jalali, R. Padovani, and R. Pankaj, “Data throughput of CDMA-HDR a high efficiency-high data rate personal communication wireless system,” in Proc. IEEE VTC, Tokyo, Japan, 2000, pp. 1854–1858.

• P. Viswanath, D. N. C. Tse, and R. Laroia, “Opportunistic beamforming using dumb antennas,” IEEE Trans. Inf. Theory, vol. 48, no. 6, pp. 1277–1294, Jun. 2002.

• R. Racic, D. Ma, H. Chen, and X. Liu, “Exploiting and defending opportunistic scheduling in cellular data networks,” IEEE Trans. Mobile Comput., vol. 9, no. 5, pp. 609–620, May 2010.

• U. Ben-Porat, A. Bremler-Barr, H. Levy, and B. Plattner, “On the vulnerability of the proportional fairness scheduler to retransmission attacks,” in Proc. IEEE INFOCOM, Shanghai, China, Apr. 2011, pp. 1431–1439.

• S. Bali, S. Machiraju, H. Zang, and V. Frost, “A measurement study of scheduler-based attacks in 3G wireless networks,” in Proc. PAM, Berlin, Germany, 2007.

A QOS-ORIENTED DISTRIBUTED ROUTING PROTOCOL FOR HYBRID

WIRELESS NETWORKS

By

A

PROJECT REPORT

Submitted to the Department of Computer Science & Engineering in the                                                  FACULTY OF ENGINEERING & TECHNOLOGY

In partial fulfillment of the requirements for the award of the degree

Of

MASTER OF TECHNOLOGY

IN

COMPUTER SCIENCE & ENGINEERING

APRIL 2015

BONAFIDE CERTIFICATE

Certified that this project report titled “A QOS-ORIENTED DISTRIBUTED ROUTING PROTOCOL FOR HYBRID WIRELESS NETWORKS” is the bonafide work of                         Mr. _____________Who carried out the research under my supervision Certified further, that to the best of my knowledge the work reported herein does not form part of any other project report or dissertation on the basis of which a degree or award was conferred on an earlier occasion on this or any other candidate.

Signature of the Guide                                                                             Signature of the H.O.D

Name                                                                                                           Name

CHAPTER 1

ABSTRACT:

As wireless communication gains popularity, significant research has been devoted to supporting real-time transmission with stringent Quality of Service (QoS) requirements for wireless applications. At the same time, a wireless hybrid network that integrates a mobile wireless ad hoc network (MANET) and a wireless infrastructure network has been proven to be a better alternative for the next generation wireless networks. By directly adopting resource reservation-based QoS routing for MANETs, hybrids networks inherit invalid reservation and race condition problems in MANETs. How to guarantee the QoS in hybrid networks remains an open problem.

In this paper, we propose a QoS-Oriented Distributed routing protocol (QOD) to enhance the QoS support capability of hybrid networks. Taking advantage of fewer transmission hops and anycast transmission features of the hybrid networks, QOD transforms the packet routing problem to a resource scheduling problem.

QOD incorporates five algorithms:

1) A QoS-guaranteed neighbor selection algorithm to meet the transmission delay requirement,

2) A distributed packet scheduling algorithm to further reduce transmission delay,

3) A mobility-based segment resizing algorithm that adaptively adjusts segment size according to node mobility in order to reduce transmission time,

4) A traffic redundant elimination algorithm to increase the transmission throughput, and

5) A data redundancy elimination-based transmission algorithm to eliminate the redundant data to further improve the transmission QoS.

Analytical and simulation results based on the random way-point model and the real human mobility model show that QOD can provide high QoS performance in terms of overhead, transmission delay, mobility-resilience, and scalability.

1.2 INTRODUCTION

The rapid development of wireless networks has stimulated numerous wireless applications that have been used in wide areas such as commerce, emergency services, military, education, and entertainment. The number of WiFi capable mobile devices including laptops and handheld devices (e.g., smartphone and tablet PC) has been increasing rapidly. For example, the number of wireless Internet users has tripled world-wide in the last three years, and the number of smartphone users in US has increased from 92.8 million in 2011 to 121.4 million in 2012, and will reach around 207 million by 2017. Nowadays, people wish to watch videos, play games, watch TV, and make longdistanceconferencing via wireless mobile devices “on the go.” Therefore, video streaming applications such as Qik, Flixwagon, and FaceTime on the infrastructure wireless networks have received increasing attention recently. These applications use an infrastructure to directly connect mobile users for video watching or interaction in real time. The widespread use of wireless and mobile devices and the increasing demand for mobile multimedia streaming services are leading to a promising near future where wireless multimedia services (e.g., mobile gaming, online TV, and online conferences) are widely deployed.

The emergence and the envisioned future of real time andmultimedia applications have stimulated the need of high Quality of Service (QoS) support in wireless and mobile networking environments. The QoS support reduces endto- end transmission delay and enhances throughput to guarantee the seamless communication between mobile devices and wireless infrastructures.

At the same time, hybrid wireless networks (i.e., multihop cellular networks) have been proven to be a better network structure for the next generation wireless networks and can help to tackle the stringent end-to-end QoS requirements of different applications. Hybrid networks synergistically combine infrastructure networks and MANETs to leverage each other. Specifically, infrastructure networks improve the scalability of MANETs, while MANETs automatically establish self-organizing networks, extending the coverage of the infrastructure networks. In a vehicle opportunistic access network (an instance of hybrid networks), people in vehicles need to upload or download videos from remote Internet servers through access points (APs) (i.e., base stations) spreading out in a city.

 Since it is unlikely that the base stations cover the entire city to maintain sufficiently strong signal everywhere to support an application requiring high link rates, the vehicles themselves can form a MANET to extend the coverage of the base stations, providing continuous network connections. How to guarantee the QoS in hybrid wireless networks with high mobility and fluctuating bandwidth still remains an open question. In the infrastructure wireless networks, QoS provision (e.g., Intserv, RSVP ) has been proposed for QoS routing, which often requires node negotiation, admission control, resource reservation, and, priority scheduling of packets.

However, it is more difficult to guarantee QoS in MANETs due to their unique features including user mobility, channel variance errors, and limited bandwidth. Thus, attempts to directly adapt the QoS solutions for infrastructure networks to MANETs generally do not have great succes. Numerous reservation-based QoS routing protocols have been proposed for MANETs that create routes formed by nodes and links that reserve their resources to fulfill QoS requirements. Although these protocols can increase the QoS of the MANETs to a certain extent, they suffer from invalid reservation and race condition problems. Invalid reservation problem means that the reserved resources become useless if the data transmission path between a source node and a destination node breaks. Race condition problem means a double allocation of the same resource to two different QoS paths. However, little effort has been devoted to support QoS routing in hybrid networks. Most of the current works in hybrid networks focus on increasing network capacity or routing reliability but cannot provide QoS-guaranteed services. Direct adoption of the reservation-based QoS routing protocols of MANETs into hybrid networks inherits the invalid reservation and race condition problems.

In order to enhance the QoS support capability of hybrid networks, in this paper, we propose a QoS-Oriented Distributed routing protocol (QOD). Usually, a hybrid network has widespread base stations. The data transmission in hybrid networks has two features. First, an AP can be a source or a destination to any mobile node. Second, the number of transmission hops between a mobile node and an AP is small. The first feature allows a stream to have anycast transmission along multiple transmission paths to its destination through base stations, and the second feature enables a source node to connect to an AP through an intermediate node. Taking full advantage of the two features, QOD transforms the packet routing problem into a dynamic resource scheduling problem. Specifically, in QOD, if a source node is not within the transmission range of the AP, a source node selects nearby neighbors that can provide QoS services to forward its packets to base stations in a distributed manner.  The source node schedules the packet streams to neighbors based on their queuing condition, channel condition, and mobility, aiming toreduce transmission time and increase network capacity. The neighbors then forward packets to base stations, which further forward packets to the destination. In this paper, we focus on the neighbor node selection for QoS-guaranteed transmission. QOD is the first work for QoS routing in hybrid networks.

1. LITRATURE SURVEY

QOS MULTICAST ROUTING BY USING MULTIPLE PATHS/TREES IN WIRELESSAD HOC NETWORKS

AUTHOR:  H. Wu and X. Jia

PUBLISH:  Ad Hoc Networks, vol. 5, pp. 600-612, 2009.

In this paper, we investigate the issues of QoS multicast routing in wireless ad hoc networks. Due to limited bandwidth of a wireless node, a QoS multicast call could often be blocked if there does not exist a single multicast tree that has the requested bandwidth, even though there is enough bandwidth in the system to support the call. In this paper, we propose a new multicast routing scheme by using multiple paths or multiple trees to meet the bandwidth requirement of a call. Three multicast routing strategies are studied, SPT (shortest path tree) based multiple-paths (SPTM), least cost tree based multiple-paths (LCTM) and multiple least cost trees (MLCT). The final routing tree(s) can meet the user’s QoS requirements such that the delay from the source to any destination node shall not exceed the required bound and the aggregate bandwidth of the paths or trees shall meet the bandwidth requirement of the call. Extensive simulations have been conducted to evaluate the performance of our three multicast routing strategies. The simulation results show that the new scheme improves the call success ratio and makes a better use of network resources.

QUALITY OF SERVICE PROVISIONING IN AD HOC WIRELESS NETWORKS: A SURVEY OF ISSUES AND SOLUTIONS

AUTHOR:  T. Reddy, I. Karthigeyan, B. Manoj, and C. Murthy

PULISH:  Ad Hoc Networks, vol. 4, no. 1, pp. 83-124, 2006.

An ad hoc wireless network (AWN) is a collection of mobile hosts forming a temporary network on the fly, without using any fixed infrastructure. Characteristics of AWNs such as lack of central coordination, mobility of hosts, dynamically varying network topology, and limited availability of resources make QoS provisioning very challenging in such networks. In this paper, we describe the issues and challenges in providing QoS for AWNs and review some of the QoS solutions proposed. We first provide a layer-wise classification of the existing QoS solutions, and then discuss each of these solutions.

QOS ROUTING BASED ON MULTI-CLASS NODES FOR MOBILE AD HOC NETWORKS

AUTHOR:  X. Du, Ad Hoc Networks

PUBLISH:  vol. 2, pp. 241-254, 2004.

Efficient routing is very important for Mobile Ad hoc Networks (MANETs). Most existing routing protocols  consider homogeneous ad hoc networks, in which all nodes are  identical, i.e., they have the same communication capabilities and  characteristics. Although a homogeneous network model is simple and easy to analyze, it misses important characteristics of  many realistic MANETs such as military battlefield networks. In  addition, a homogeneous ad hoc network suffers from poor  performance limits and scalability. In many ad hoc networks,  multiple types of nodes do co-exist; and some nodes have larger transmission power, higher transmission data rate, better  processing capability, and are more robust against bit errors and  congestion than other nodes. Hence, a heterogeneous network model is more realistic and provides many advantages (e.g., leading to more efficient routing protocol design). In this paper,

We present a new routing protocol called Multi-Class (MC) routing, which is specifically designed for heterogeneous MANETs. Moreover, we also design a new Medium Access Control (MAC) protocol for heterogeneous MANETs, which is  more efficient than IEEE 802.11b. Extensive simulation results  demonstrate that the MC routing has very good performance,  and outperforms a popular routing protocol — Zone Routing Protocol, in terms of reliability, scalability, route discovery latency, overhead, as well as packet delay and throughput.

PROVISIONING OF ADAPTABILITY TO VARIABLE TOPOLOGIES FOR ROUTING SCHEMES IN MANETS

AUTHOR:  S. Jiang, Y. Liu, Y. Jiang, and Q. Yin,

PUBLISH:   IEEE J. Selected Areas in Comm., vol. 22, no. 7, pp. 1347-1356, Sept. 2004.

Frequent changes in network topologies caused by mobility in mobile ad hoc networks (MANETs) impose great challenges to designing routing schemes for such networks. Various routing schemes each aiming at particular type of MANET (e.g., flat or clustered MANETs) with different mobility degrees (e.g., low, medium, and high mobility) have been proposed in the literature. However, since a mobile node should not be limited to operate in a particular MANET assumed by a routing scheme, an important issue is how to enable a mobile node to achieve routing performance as high as possible when it roams across different types of MANETs. To handle this issue, a quantity that can predict the link status for a time period in the future with the consideration of mobility is required. In this paper, we discuss such a quantity and investigate how well this quantity can be used by the link caching scheme in the dynamic source routing protocol to provide the adaptability to variable topologies caused by mobility through computer simulation in NS-2.

                                                                                                                                 CHAPTER 2

2.0 SYSTEM ANALYSIS

2.1EXISTING SYSTEM:

Existing approaches for providing guaranteed services in the infrastructure networks are based on two models: integrated services (IntServ) and differentiated service (DiffServ) [42]. IntServ is a stateful model that uses resource reservation for individual flow, and uses admission control and a scheduler to maintain the QoS of traffic flows. In contrast, DiffServ is a stateless model which uses coarsegrained class-based mechanism for traffic management a number of queuing scheduling algorithms. Reservation-based QoS routing protocols have been proposed for MANETs that create routes formed by nodes and links that reserve their resources to fulfill QoS requirements although these protocols can increase the QoS of the MANETs to a certain extent.

2.2 DISADVANTAGES:

• Cannot provide QoS-guaranteed services.
• Suffer from invalid reservation and race condition problems .
• Invalid reservation problem means that the reserved resources become useless and Race condition problem means a double allocation of the same resource to two different QoS paths.

PROPOSED SYSTEM:

We propose a QoS-Oriented Distributed routing protocol (QOD). Usually, a hybrid network has widespread base stations.

The data transmission in hybrid networks has two features.

First, an AP can be a source or a destination to any mobile node. Second, the number of transmission hops between a mobile node and an AP is small.  The first feature allows a stream to have anycast transmission along multiple transmission paths to its destination through base stations, and the second feature enables a source node to connect to an AP through an intermediate node. Taking full advantage of the two features, QOD transforms the packet routing problem into a dynamic resource scheduling problem. Specifically, in QOD, if a source node is not within the transmission range of the AP, a source node selects nearby neighbors that can provide QoS services to forward its packets to base stations in a distributed manner. The source node schedules the packet streams to neighbors based on their queuing condition, channel condition, and mobility, aiming to reduce transmission time and increase network capacity. The neighbors then forward packets to base stations, which further forward packets to the destination.

ADVANTAGES:

• QoS-guaranteed neighbor selection algorithm. The algorithm selects qualified neighbors and employs deadline-driven scheduling mechanism to guarantee QoS routing.

• Distributed packet scheduling algorithm. After qualified neighbors are identified, this algorithm schedules packet routing. It assigns earlier generated packets to forwarders with higher queuing delays, while assigns more recently generated packets to forwarders with lower queuing delays to reduce total transmission delay.

• Mobility-based segment resizing algorithm. The source node adaptively resizes each packet in its packet stream for each neighbor node according to the neighbor’s mobility in order to increase the scheduling feasibility of the packets from the source node.

• Soft-deadline based forwarding scheduling algorithm. In this algorithm, an intermediate node first forwards the packet with the least time allowed to wait before being forwarded out to achieve fairness in packet forwarding.

• Data redundancy elimination based transmission. Due to the broadcasting feature of the wireless networks, the APs and mobile nodes can overhear and cache packets. This algorithm eliminates the redundant data to improve the QoS of the packet transmission.

HARDWARE & SOFTWARE REQUIREMENTS:

HARDWARE REQUIREMENT:

v    Processor                                 –    Pentium –IV

• Speed                                      –    1.1 GHz
  • RAM                                       –    256 MB (min)
  • Hard Disk                               –   20 GB
  • Floppy Drive                           –    1.44 MB
  • Key Board                              –    Standard Windows Keyboard
  • Mouse                                     –    Two or Three Button Mouse
  • Monitor                                   –    SVGA

 

SOFTWARE REQUIREMENTS:

• Operating System                   :           Windows XP
• Front End                                :           Java JDK 1.7
• Document                               :           MS-Office 2007

CHAPTER 3

SYSTEM DESIGN:

Data Flow Diagram / Use Case Diagram / Flow Diagram:

• The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of the input data to the system, various processing carried out on these data, and the output data is generated by the system

• The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. These components are the system process, the data used by the process, an external entity that interacts with the system and the information flows in the system.

• DFD shows how the information moves through the system and how it is modified by a series of transformations. It is a graphical technique that depicts information flow and the transformations that are applied as data moves from input to output.

• DFD is also known as bubble chart. A DFD may be used to represent a system at any level of abstraction. DFD may be partitioned into levels that represent increasing information flow and functional detail.

NOTATION:

SOURCE OR DESTINATION OF DATA:

External sources or destinations, which may be people or organizations or other entities

DATA SOURCE:

Here the data referenced by a process is stored and retrieved.

PROCESS:

People, procedures or devices that produce data. The physical component is not identified.

DATA FLOW:

Data moves in a specific direction from an origin to a destination. The data flow is a “packet” of data.

MODELING RULES:

There are several common modeling rules when creating DFDs:

1. All processes must have at least one data flow in and one data flow out.
2. All processes should modify the incoming data, producing new forms of outgoing data.
3. Each data store must be involved with at least one data flow.
4. Each external entity must be involved with at least one data flow.
5. A data flow must be attached to at least one process.

3.1 SYSTEM ARCHITECTURE:

3.2 DATAFLOW DIAGRAM

SENSOR NODE:

MOBILE RELAY NODE:

SINK:

UML DIAGRAMS:

3.2 USE CASE DIAGRAM:

3.3 CLASS DIAGRAM:

3.4 SEQUENCE DIAGRAM:

3.5 ACTIVITY DIAGRAM:

3.5 ACTIVITY DIAGRAM:

CHAPTER 4

4.0 IMPLEMENTATION:

QOD ROUTING PROTOCOL:

Scheduling feasibility is the ability of a node to guarantee a packet to arrive at its destination within QoS requirements. As mentioned, when the QoS of the direct transmission between a source node and an AP cannot be guaranteed, the source node sends a request message to its neighbor nodes. After receiving a forward request from a source node, a neighbor node ni with space utility less than a threshold replies the source node. The reply message contains information about available resources for checking packet scheduling feasibility (Section 2.4), packet arrival interval Ta, transmission delay TI!D, and packet deadline Dp of the packets in each flow being forwarded by the neighbor for queuing delay estimation and distributed packet scheduling and the node’s mobility speed for determining packet size. Based on this information, the source node chooses the replied neighbors that can guarantee the delay QoS of packet transmission to APs.

The selected neighbor nodes periodically report their statuses to the source node, which ensures their scheduling feasibility and locally schedules the packet stream to them. The individual packets are forwarded to the neighbor nodes that are scheduling feasible in a round-robin fashion from a longer delayed node to a shorter delayed node, aiming to reduce the entire packet transmission delay. Algorithm 1 shows the pseudocode for the QOD routing protocol executed by each node. The QOD distributed routing algorithm is developed based on the assumption that the neighboring nodes in the network have different channel utilities and workloads using IEEE 802.11 protocol. Otherwise, there is no need for packet scheduling in routing, since all neighbors produce comparative delay for packet forwarding. Therefore, we analyze the difference in node channel utilities and workloads in a network with IEEE 802.11 protocol in order to see whether the assumption holds true in practice.

4.1 ALGORITHM

The packets travel from different APs, which may lead to different packet transmission delay, resulting in a jitter at the receiver side. The jitter problem can be solved by using token buckets mechanism at the destination APs to shape the traffic flows. This technique is orthogonal to our study in this paper and its details are beyond the scope of this paper.

4.2 MODULES:

HYBRID WIRELESS NETWORKS:

DISTRIBUTED PACKET SCHEDULING:

NEIGHBOR SELECTION ALGORITHM:

MOBILITY-BASED PACKET RESIZING:

4.3 MODULE DESCRIPTION:

HYBRID WIRELESS NETWORKS:

Hybrid wireless networks (i.e., multihop cellular networks) have been proven to be a better network structure for the next generation wireless networks and can help to tackle the stringent end-to end QoS requirements of different applications. Hybrid networks synergistically combine infrastructure networks and MANETs to leverage each other. Specifically, infrastructure networks improve the scalability of MANETs, while MANETs automatically establish self-organizing networks, extending the coverage of the infrastructure networks.

In a vehicle opportunistic access network (an instance of hybrid networks), people in vehicles need to upload or download videos from remote Internet servers through access points (APs) (i.e., base stations) spreading out in a city. Since it is unlikely that the base stations cover the entire city to maintain sufficiently strong signal everywhere to support an application requiring high link rates, the vehicles themselves can form a MANET to extend the coverage of the base stations, providing continuous network connections.

The QoS requirements mainly include end-to-end delay bound, which is essential for many applications with stringent real-time requirement. While throughput guarantee is also important, it is automatically guaranteed by bounding the transmission delay for a certain amount of packets. The source node conducts admission control to check whether there are enough resources to satisfy the requirements of QoS of the packet stream in the network model of a hybrid network. For example, when a source node n1 wants to upload files to an Internet server through APs, it can choose to send packets to the APs directly by itself or require its neighbor nodes n2, n3, or n4 to assist the packet transmission.

DISTRIBUTED PACKET SCHEDULING:

QoS of the packet transmission and how a source node assigns traffic to the intermediate nodes to ensure their scheduling feasibility in order to further reduce the stream transmission time, a distributed packet scheduling algorithm is proposed for packet routing. This algorithm assigns earlier generated packets to forwarders with higher queuing delays and scheduling feasibility, while assigns more recently generated packets to forwarders with lower queuing delays and scheduling feasibility, so that the transmission delay of an entire packet stream can be reduced.

NEIGHBOR SELECTION ALGORITHM:

Since short delay is the major real-time QoS requirement for traffic transmission, QOD incorporates the Earliest Deadline First scheduling algorithm (EDF), which is a deadline driven scheduling algorithm for data traffic scheduling in intermediate nodes. In this algorithm, an intermediate node assigns the highest priority to the packet with the closest deadline and forwards the packet with the highest priority first. Let us use SpðiÞ to denote the size of the packet steam from node ni, use Wi to denote the bandwidth of node i, and TaðiÞ to denote the packet arrival interval from node ni.

MOBILITY-BASED PACKET RESIZING:

In a highly dynamic mobile wireless network, the transmission link between two nodes is frequently broken down. The delay generated in the packet retransmission degrades the QoS of the transmission of a packet flow. On the other hand, a node in a highly dynamic network has higher probability to meet different mobile nodes and APs, which is beneficial to resource scheduling. As (2) shows, the space utility of an intermediate node that is used for forwarding a packet p is reducing packet size can increase the scheduling feasibility of an intermediate node and reduces packet dropping probability. However, we cannot make the size of the packet too small because it generates more packets to be transmitted, producing higher packet overhead. Based on this rationale and taking advantage of the benefits of node mobility, we propose a mobility-based packet resizing algorithm for QOD in this section. The basic idea is that the larger size packets are assigned to lower mobility intermediate nodes and smaller size packets are assigned to higher mobility intermediate nodes, which increases the QoS-guaranteed packet transmissions. Specifically, in QOD, as the mobility of a node increases, the size of a packet Sp sent from a node to its neighbor nodes i decreases as following:

CHAPTER 5

5.0 SYSTEM STUDY:

5.1 FEASIBILITY STUDY:

The feasibility of the project is analyzed in this phase and business proposal is put forth with a very general plan for the project and some cost estimates. During system analysis the feasibility study of the proposed system is to be carried out. This is to ensure that the proposed system is not a burden to the company.  For feasibility analysis, some understanding of the major requirements for the system is essential.

Three key considerations involved in the feasibility analysis are      

• ECONOMICAL FEASIBILITY
• TECHNICAL FEASIBILITY
• SOCIAL FEASIBILITY

5.1.1 ECONOMICAL FEASIBILITY:                  

This study is carried out to check the economic impact that the system will have on the organization. The amount of fund that the company can pour into the research and development of the system is limited. The expenditures must be justified. Thus the developed system as well within the budget and this was achieved because most of the technologies used are freely available. Only the customized products had to be purchased.

 

5.1.2 TECHNICAL FEASIBILITY:

 This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.  

5.1.3 SOCIAL FEASIBILITY:  

The aspect of study is to check the level of acceptance of the system by the user. This includes the process of training the user to use the system efficiently. The user must not feel threatened by the system, instead must accept it as a necessity. The level of acceptance by the users solely depends on the methods that are employed to educate the user about the system and to make him familiar with it. His level of confidence must be raised so that he is also able to make some constructive criticism, which is welcomed, as he is the final user of the system.

5.2 SYSTEM TESTING:

Testing is a process of checking whether the developed system is working according to the original objectives and requirements. It is a set of activities that can be planned in advance and conducted systematically. Testing is vital to the success of the system. System testing makes a logical assumption that if all the parts of the system are correct, the global will be successfully achieved. In adequate testing if not testing leads to errors that may not appear even many months. This creates two problems, the time lag between the cause and the appearance of the problem and the effect of the system errors on the files and records within the system. A small system error can conceivably explode into a much larger Problem. Effective testing early in the purpose translates directly into long term cost savings from a reduced number of errors. Another reason for system testing is its utility, as a user-oriented vehicle before implementation. The best programs are worthless if it produces the correct outputs.

5.2.1 UNIT TESTING:

A program represents the logical elements of a system. For a program to run satisfactorily, it must compile and test data correctly and tie in properly with other programs. Achieving an error free program is the responsibility of the programmer. Program  testing  checks  for  two  types  of  errors:  syntax  and  logical. Syntax error is a program statement that violates one or more rules of the language in which it is written. An improperly defined field dimension or omitted keywords are common syntax errors. These errors are shown through error message generated by the computer. For Logic errors the programmer must examine the output carefully.

UNIT TESTING:

Description	Expected result
Test for application window properties.	All the properties of the windows are to be properly aligned and displayed.
Test for mouse operations.	All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions.

5.1.3 FUNCTIONAL TESTING:

Functional testing of an application is used to prove the application delivers correct results, using enough inputs to give an adequate level of confidence that will work correctly for all sets of inputs. The functional testing will need to prove that the application works for each client type and that personalization function work correctly.When a program is tested, the actual output is compared with the expected output. When there is a discrepancy the sequence of instructions must be traced to determine the problem.  The process is facilitated by breaking the program into self-contained portions, each of which can be checked at certain key points. The idea is to compare program values against desk-calculated values to isolate the problems.

FUNCTIONAL TESTING:

Description	Expected result
Test for all modules.	All peers should communicate in the group.
Test for various peer in a distributed network framework as it display all users available in the group.	The result after execution should give the accurate result.

5.1. 4 NON-FUNCTIONAL TESTING:

 The Non Functional software testing encompasses a rich spectrum of testing strategies, describing the expected results for every test case. It uses symbolic analysis techniques. This testing used to check that an application will work in the operational environment. Non-functional testing includes:

• Load testing
• Performance testing
• Usability testing
• Reliability testing
• Security testing

5.1.5 LOAD TESTING:

An important tool for implementing system tests is a Load generator. A Load generator is essential for testing quality requirements such as performance and stress. A load can be a real load, that is, the system can be put under test to real usage by having actual telephone users connected to it. They will generate test input data for system test.

Load Testing

Description	Expected result
It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received.	Should designate another active node as a Server.

5.1.5 PERFORMANCE TESTING:

Performance tests are utilized in order to determine the widely defined performance of the software system such as execution time associated with various parts of the code, response time and device utilization. The intent of this testing is to identify weak points of the software system and quantify its shortcomings.

PERFORMANCE TESTING:

Description	Expected result
This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management.	Should handle large input values, and produce accurate result in a  expected time.

5.1.6 RELIABILITY TESTING:

The software reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time and it is being ensured in this testing. Reliability can be expressed as the ability of the software to reveal defects under testing conditions, according to the specified requirements. It the portability that a software system will operate without failure under given conditions for a given time interval and it focuses on the behavior of the software element. It forms a part of the software quality control team.

RELIABILITY TESTING:

Description	Expected result
This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application.	In case of failure of  the server an alternate server should take over the job.

5.1.7 SECURITY TESTING:

Security testing evaluates system characteristics that relate to the availability, integrity and confidentiality of the system data and services. Users/Clients should be encouraged to make sure their security needs are very clearly known at requirements time, so that the security issues can be addressed by the designers and testers.

SECURITY TESTING:

Description	Expected result
Checking that the user identification is authenticated.	In case failure it should not be connected in the framework.
Check whether group keys in a tree are shared by all peers.	The peers should know group key in the same group.

5.1.7 WHITE BOX TESTING:

White  box  testing,  sometimes called  glass-box  testing is  a test  case  design method  that  uses  the  control  structure  of the procedural  design  to  derive  test  cases. Using  white  box  testing  method,  the software  engineer  can  derive  test  cases. The White box testing focuses on the inner structure of the software structure to be tested.

5.1.8 WHITE BOX TESTING:

Description	Expected result
Exercise all logical decisions on their true and false sides.	All the logical decisions must be valid.
Execute all loops at their boundaries and within their operational bounds.	All the loops must be finite.
Exercise internal data structures to ensure their validity.	All the data structures must be valid.

5.1.9 BLACK BOX TESTING:

Black box testing, also called behavioral testing, focuses on the functional requirements of the software.  That  is,  black  testing  enables  the software engineer  to  derive  sets  of  input  conditions  that  will  fully  exercise  all  functional requirements  for  a  program.  Black box testing is not alternative to white box techniques.  Rather  it  is  a  complementary  approach  that  is  likely  to  uncover  a different  class  of  errors  than  white box  methods. Black box testing attempts to find errors which focuses on inputs, outputs, and principle function of a software module. The starting point of the black box testing is either a specification or code. The contents of the box are hidden and the stimulated software should produce the desired results.

5.1.10 BLACK BOX TESTING:

Description	Expected result
To check for incorrect or missing functions.	All the functions must be valid.
To check for interface errors.	The entire interface must function normally.
To check for errors in a data structures or external data base access.	The database updation and retrieval must be done.
To check for initialization and termination errors.	All the functions and data structures must be initialized properly and terminated normally.

All the above system testing strategies are carried out in as the development, documentation and institutionalization of the proposed goals and related policies is essential.

                                                                                                                                     CHAPTER 7

APPENDIX

7.1 SAMPLE SOURCE CODE

7.2 SAMPLE OUTPUT

CHAPTER 8

CONCLUSION:

We propose a QoS oriented distributed routing protocol (QOD) for hybrid networks to provide QoS services in a highly dynamic scenario. Taking advantage of the unique features of hybrid networks, i.e., anycast transmission and short transmission hops, QOD transforms the packet routing problem to a packet scheduling problem. In QOD, a source node directly transmits packets to an AP if the direct transmission can guarantee the QoS of the traffic. Otherwise, the source node schedules the packets to a number of qualified neighbor nodes.

Specifically, QOD incorporates five algorithms. The QoS-guaranteed neighbor selection algorithm chooses qualified neighbors for packet forwarding. The distributed packet scheduling algorithm schedules the packet transmission to further reduce the packet transmission time. The mobility-based packet resizing algorithm resizes packets and assigns smaller packets to nodes with faster mobility to guarantee the routing QoS in a highly mobile environment.

The traffic redundant elimination-based transmission algorithm can further increase the transmission throughput. The soft-deadline-based forwarding scheduling achieves fairness in packet forwarding scheduling when some packets are not scheduling feasible. Experimental results show that QOD can achieve high mobility-resilience, scalability, and contention reduction. In the future, we plan to evaluate the performance of QOD based on the real testbed.

CHAPTER 9

REFERENCES:

[1] H. Wu and X. Jia, “QoS Multicast Routing by Using Multiple Paths/Trees in Wireless Ad Hoc Networks,” Ad Hoc Networks, vol. 5, pp. 600-612, 2009.

[2] T. Reddy, I. Karthigeyan, B. Manoj, and C. Murthy, “Quality of Service Provisioning in Ad Hoc Wireless Networks: A Survey of Issues and Solutions,” Ad Hoc Networks, vol. 4, no. 1, pp. 83-124, 2006.

[3] X. Du, “QoS Routing Based on Multi-Class Nodes for Mobile Ad Hoc Networks,” Ad Hoc Networks, vol. 2, pp. 241-254, 2004.

[4] S. Jiang, Y. Liu, Y. Jiang, and Q. Yin, “Provisioning of Adaptability to Variable Topologies for Routing Schemes in MANETs,” IEEE J. Selected Areas in Comm., vol. 22, no. 7, pp. 1347-1356, Sept. 2004.

[5] M. Conti, E. Gregori, and G. Maselli, “Reliable and Efficient Forwarding in Ad Hoc Networks,” Ad Hoc Networks, vol. 4, pp. 398-415, 2006.

[6] G. Chakrabarti and S. Kulkarni, “Load Balancing and Resource Reservation in Mobile Ad Hoc Networks,” Ad Hoc Networks, vol. 4, pp. 186-203, 2006.

[7] Z. Shen and J.P. Thomas, “Security and QoS Self-Optimization in Mobile Ad Hoc Networks,” IEEE Trans. Mobile Computing, vol. 7, pp. 1138-1151, Sept. 2008.

[8] S. Ibrahim, K. Sadek, W. Su, and R. Liu, “Cooperative Communications with Relay-Selection: When to Cooperate and Whom to Cooperate With?” IEEE Trans. Wireless Comm., vol. 7, no. 7, pp. 2814-2827, July 2008.

In this paper, propose a cocktail approach to generate the lists for personalized travel package recommendation. Furthermore, we extend the TAST model to the tourist-relation-area-season topic (TRAST) model for capturing the latent relationships among the tourists in each travel group. Finally, we evaluate the TAST model, the TRAST model, and the cocktail recommendation approach on the real-world travel package data.

TRAST model can be easily extended for computing relationships among many more tourists. However, the computation cost will also go up. To simplify the problem, in this paper, each time we only consider two tourists in a travel group as a tourist pair for mining their relationships. By this TRAST model, all the tourists’ travel preferences are represented by relationship distributions.

We can use their relationship distributions as features to cluster them, so as to put them into different travel groups. Thus, in this scenario, many clustering methods can be adopted. Since choosing clustering algorithm is beyond the scope of this paper, in the experiments, we refer to K-means one of the most popular clustering algorithms.

INTRODUCTION:

AS an emerging trend, more and more travel companies provide online services. However, the rapid growth of online travel information imposes an increasing challenge for tourists who have to choose from a large number of available travel packages for satisfying their personalized needs. Moreover, to increase the profit, the travel companies have to understand the preferences from different tourists and serve more attractive packages. Therefore, the demand for intelligent travel services is expected to increase dramatically. Since recommender systems have been successfully applied to enhance the quality of service in a number of fields, it is natural choice to provide travel package recommendations. Actually, recommendations for tourists have been studied before and to the best of our knowledge, the first operative tourism recommender system was introduced by Delgado and DavidsonDespite of the increasing interests in this field, the problem of leveraging unique features to distinguish personalized travel package recommendations from traditional recommender systems remains pretty open.

 Indeed, there are many technical and domain challenges inherent in designing and implementing an effective recommender system for personalized travel package recommendation. First, travel data are much fewer and sparser than traditional items, such as movies for recommendation, because the costs for a travel are much more expensive than for watching a movie. Second, every travel package consists of many landscapes (places of interest and attractions), and, thus, has intrinsic complex spatio-temporal relationships. For example, a travel package only includes the landscapes which are geographically colocated together. Also, different travel packages areusually developed for different travel seasons.

Therefore, the landscapes in a travel package usually have spatialtemporal autocorrelations. Third, traditional recommender systems usually rely on user explicit ratings.  However, for travel data, the user ratings are usually not conveniently available. Finally, the traditional items for recommendation usually have a long period of stable value, while the values of travel packages can easily depreciate over time and a package usually only lasts for a certain period of time. The travel companies need to actively create new tour packages to replace the old ones based on the interests of the tourists. To address these challenges, in our preliminary work we proposed a cocktail approach on personalized travel package recommendation. Specifically, we first analyze the key characteristics of the existing travel packages. Along this line, travel time and travel destinations are divided into different seasons and areas. Then, wedevelop a tourist-area-season topic (TAST) model, which can represent travel packages and tourists by different topic distributions. In the TAST model, the extraction of topics is conditioned on both the tourists and the intrinsic features (i.e., locations, travel seasons) of the landscapes. As a result, the TAST model can well represent the content of the travel packages and the interests of the tourists. Based on this TAST model, a cocktail approach is developed for personalized travel package recommendation by considering some additional factors including the seasonal behaviors of tourists, the prices of travel packages, and the cold start problem of new packages.

Finally, the experimental results on real-world travel data show that the TAST model can effectively capture the unique characteristics of travel data and the cocktail recommendation approach performs much better than traditional techniques. In this paper, we further study some related topic models of the TAST model, and explain the corresponding travel package recommendation strategies based on them. Also, we propose the tourist-relation-area-season topic (TRAST) model, which helps understand the reasons why tourists form a travel group. This goes beyond personalized package recommendations and is helpful for capturing the latent relationships among the tourists in each travel group.

In addition, we conduct systematic experiments on the realworld data. These experiments not only demonstrate that the TRAST model can be used as an assessment for travel group automatic formation but also provide more insights into the TAST model and the cocktail recommendation

approach. In summary, the contributions of the TAST model, the cocktail approaches, and the TRAST model for travel package recommendations are shown in Fig. 1, whereeach dashed rectangular box in the dashed circle identifies atravel group and the tourists in the same travel group are represented by the same icons.

LITRATURE SURVEY

COST-AWARE TRAVEL TOUR RECOMMENDATION

AUTHOR: Y. Ge et al.,

PUBLISH: Proc. 17th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (SIGKDD ’11), pp. 983-991, 2011.

Recent years have witnessed an increased interest in recommender systems. Despite significant progress in this field, there still remain numerous avenues to explore. Indeed, this paper provides a study of exploiting online travel information for personalized travel package recommendation. A critical challenge along this line is to address the unique characteristics of travel data, which distinguish travel packages from traditional items for recommendation. To that end, in this paper, we first analyze the characteristics of the existing travel packages and develop a tourist-area-season topic (TAST) model. This TAST model can represent travel packages and tourists by different topic distributions, where the topic extraction is conditioned on both the tourists and the intrinsic features (i.e., locations, travel seasons) of the landscapes. Then, based on this topic model representation, we propose a cocktail approach to generate the lists for personalized travel package recommendation. Furthermore, we extend the TAST model to the tourist-relation-area-season topic (TRAST) model for capturing the latent relationships among the tourists in each travel group. Finally, we evaluate the TAST model, the TRAST model, and the cocktail recommendation approach on the real-world travel package data. Experimental results show that the TAST model can effectively capture the unique characteristics of the travel data and the cocktail approach is, thus, much more effective than traditional recommendation techniques for travel package recommendation. Also, by considering tourist relationships, the TRAST model can be used as an effective assessment for travel group formation.

FLDA: MATRIX FACTORIZATION THROUGH LATENT DIRICHLET ALLOCATION

AUTHOR: D. Agarwal and B. Chen

PUBLISH: Proc. Third ACM Int’l Conf. Web Search and Data Mining (WSDM ’10), pp. 91-100, 2010.

We propose fLDA, a novel matrix factorization method to predict ratings in recommender system applications where a “bag-of-words” representation for item meta-data is natural. Such scenarios are commonplace in web applications like content recommendation, ad targeting and web search where items are articles, ads and web pages respectively. Because of data sparseness, regularization is key to good predictive accuracy. Our method works by regularizing both user and item factors simultaneously through user features and the bag of words associated with each item. Specifically, each word in an item is associated with a discrete latent factor often referred to as the topic of the word; item topics are obtained by averaging topics across all words in an item. Then, user rating on an item is modeled as user’s affinity to the item’s topics where user affinity to topics (user factors) and topic assignments to words in items (item factors) are learned jointly in a supervised fashion. To avoid overfitting, user and item factors are regularized through Gaussian linear regression and Latent Dirichlet Allocation (LDA) priors respectively. We show our model is accurate, interpretable and handles both cold-start and warm-start scenarios seamlessly through a single model. The efficacy of our method is illustrated on benchmark datasets and a new dataset from Yahoo! Buzz where fLDA provides superior predictive accuracy in cold-start scenarios and is comparable to state-of-the-art methods in warm-start scenarios. As a by-product, fLDA also identifies interesting topics that explains user-item interactions. Our method also generalizes a recently proposed technique called supervised LDA (sLDA) to collaborative filtering applications. While sLDA estimates item topic vectors in a supervised fashion for a single regression, fLDA incorporates multiple regressions (one for each user) in estimating the item factors.

MAP-BASED INTERACTION WITH A CONVERSATIONAL MOBILE RECOMMENDER SYSTEM,

AUTHOR: D. Agarwal and B. Chen

PUBLISH: Proc. Second Int’l Conf. Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM ’08), pp. 212-218, 2008.

Recommender systems are information search and decision support tools used when there is an overwhelming set of options to consider or when the user lacks the domain-specific knowledge necessary to take autonomous decisions. They provide users with personalized recommendations adapted to their needs and preferences in a particular usage context. In this paper, we present an approach for integrating recommendation and electronic map technologies to build a map-based conversational mobile recommender system that can effectively and intuitively support users in finding their desired products and services. The results of our real-user study show that integrating map-based visualization and interaction in mobile recommender systems improves the system recommendation effectiveness and increases the user satisfaction.

GENERATING COMPARATIVE DESCRIPTIONS OF PLACES OF INTEREST IN THE TOURISM DOMAIN

AUTHOR: B.D. Carolis, N. Novielli, V.L. Plantamura, and E. Gentile

PUBLISH: Proc. Third ACM Conf. Recommender Systems (RecSys ’09), pp. 277-280, 2009.

When visiting cities as tourists, most of the times people do not make very detailed plans and, when choosing where to go and what to seem they tend to select the area with the major number of interesting facilities. Therefore, it would be useful to support the user choice with contextual information presentation, information clustering and comparative explanations of places of potential interest in a given area. In this paper we illustrate how My Map, a mobile recommender system in the Tourism domain, generates comparative descriptions to support users in making decisions about what to see, among relevant objects of interest.

CHAPTER 2

EXISTING SYSTEM:

We first analyze the characteristics of the existing travel packages and develop a tourist-area-season topic (TAST) model. This TAST model can represent travel packages and tourists by different topic distributions, where the topic extraction is conditioned on both the tourists and the intrinsic features (i.e., locations, travel seasons) of the landscapes a tourist-area-season topic (TAST) model which can represent travel packages and tourists by different topic distributions. In the TAST model, the extraction of topics is conditioned on both the tourists and the intrinsic features (i.e., locations, travel seasons) of the landscapes.

As a result, the TAST model can well represent the content of the travel packages and the interests of the tourists. Based on this TAST model, a cocktail approach is developed for personalized travel package recommendation by considering some additional factors including the seasonal behaviors of tourists, the prices of travel packages, and the cold start problem of new packages. Finally, the experimental results on real-world travel data show that the TAST model can effectively capture the unique characteristics of travel data and the cocktail recommendation approach performs much better than traditional techniques.

DISADAVANTAGES:

• The previous cocktail recommendation approach (Cocktail) is mainly based on the TAST model and the collaborative filtering method.

• Indeed, another possible cocktail approach is the content-based cocktail, and in the following, we call this method TAST Content.

• The main difference between TAST Content and Cocktail is that in TAST Content the content similarity between packages and tourists are used for ranking packages instead of using collaborative filtering interests of the tourists, thus it may also suffer from the overspecialization problem.

PROPOSED SYSTEM:

We propose the tourist-relation-area-season topic (TRAST) model, which helps understand the reasons why tourists form a travel group. This goes beyond personalized package recommendations and is helpful for capturing the latent relationships among the tourists in each travel group. In addition, we conduct systematic experiments on the real world data. These experiments demonstrate that the TRAST model can be used as an assessment for travel group automatic formation but also provide more insights into the TAST model and the cocktail recommendation approach.

Our contributions of the cocktail approaches, and the TRAST model for travel package recommendations are each dashed rectangular box in the dashed circle identifies a travel group and the tourists in the same travel group are represented in this TRAST model, all the tourists’ travel preferences are represented by relationship distributions. For a set of tourists, who want to travel the same package, we can use their relationship distributions as features to cluster them, so as to put them into different travel groups. Thus, in this scenario, many clustering methods can be adopted. Since choosing clustering algorithm is beyond the scope of this paper, in the experiments, we refer to K-means one of the most popular clustering algorithms.

ADVANTAGES:

• The results of the season splitting and price segmentation,
• The understanding of the extracted topics,
• A recommendation performance comparison between Cocktail and benchmark methods,
• The evaluation of the TRAST model, and
• A brief discussion on recommendations for travel groups.

HARDWARE & SOFTWARE REQUIREMENTS:

HARDWARE REQUIREMENT:

v    Processor                                 –    Pentium –IV

• Speed                                      –    1.1 GHz
  • RAM                                       –    256 MB (min)
  • Hard Disk                               –   20 GB
  • Floppy Drive                           –    1.44 MB
  • Key Board                              –    Standard Windows Keyboard
  • Mouse                                     –    Two or Three Button Mouse
  • Monitor                                   –    SVGA

 

2.3.2 SOFTWARE REQUIREMENTS:

• Operating System                   :           Windows XP
• Front End                                :           JAVA JDK 1.7/JSP
• Back End                                :           MYSQL Server

CHAPTER 3

3.0 SYSTEM DESIGN:

SYSTEM DESIGN

SYSTEM ARCHITECTURE:

ARCHITECTURE DIAGRAM:

CHAPTER 4

DATA FLOW DIAGRAM:

1. The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of input data to the system, various processing carried out on this data, and the output data is generated by this system.
2. The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. These components are the system process, the data used by the process, an external entity that interacts with the system and the information flows in the system.
3. DFD shows how the information moves through the system and how it is modified by a series of transformations. It is a graphical technique that depicts information flow and the transformations that are applied as data moves from input to output.
4. DFD is also known as bubble chart. A DFD may be used to represent a system at any level of abstraction. DFD may be partitioned into levels that represent increasing information flow and functional detail.

UML DIAGRAMS

UML stands for Unified Modeling Language. UML is a standardized general-purpose modeling language in the field of object-oriented software engineering. The standard is managed, and was created by, the Object Management Group.

The goal is for UML to become a common language for creating models of object oriented computer software. In its current form UML is comprised of two major components: a Meta-model and a notation. In the future, some form of method or process may also be added to; or associated with, UML.

          The Unified Modeling Language is a standard language for specifying, Visualization, Constructing and documenting the artifacts of software system, as well as for business modeling and other non-software systems.

The UML represents a collection of best engineering practices that have proven successful in the modeling of large and complex systems.

 The UML is a very important part of developing objects oriented software and the software development process. The UML uses mostly graphical notations to express the design of software projects.

GOALS:

          The Primary goals in the design of the UML are as follows:

1. Provide users a ready-to-use, expressive visual modeling Language so that they can develop and exchange meaningful models.
2. Provide extendibility and specialization mechanisms to extend the core concepts.
3. Be independent of particular programming languages and development process.
4. Provide a formal basis for understanding the modeling language.
5. Encourage the growth of OO tools market.
6. Support higher level development concepts such as collaborations, frameworks, patterns and components.
7. Integrate best practices.

USE CASE DIAGRAM:

A use case diagram in the Unified Modeling Language (UML) is a type of behavioral diagram defined by and created from a Use-case analysis. Its purpose is to present a graphical overview of the functionality provided by a system in terms of actors, their goals (represented as use cases), and any dependencies between those use cases. The main purpose of a use case diagram is to show what system functions are performed for which actor. Roles of the actors in the system can be depicted.

CLASS DIAGRAM:

In software engineering, a class diagram in the Unified Modeling Language (UML) is a type of static structure diagram that describes the structure of a system by showing the system’s classes, their attributes, operations (or methods), and the relationships among the classes. It explains which class contains information.

SEQUENCE DIAGRAM:

A sequence diagram in Unified Modeling Language (UML) is a kind of interaction diagram that shows how processes operate with one another and in what order. It is a construct of a Message Sequence Chart. Sequence diagrams are sometimes called event diagrams, event scenarios, and timing diagrams.

ACTIVITY DIAGRAM:

Activity diagrams are graphical representations of workflows of stepwise activities and actions with support for choice, iteration and concurrency. In the Unified Modeling Language, activity diagrams can be used to describe the business and operational step-by-step workflows of components in a system. An activity diagram shows the overall flow of control.

MODULES:

COCKTAIL APPROACH

TRAVEL PACKAGE

TRAST MODEL

RECOMMENDATIONS

MODULES DESCRIPTION:

COCKTAIL APPROACH:

Our cocktail approaches the specific preferences of the tourist while he/she is planning a trip, such as the transportation preference. In other words, we focus on designing the recommendation algorithm to attract the tourists before they make a travel decision rather than providing the travel support in the on-tour stage. Thus, our approach may be only useful in some situations (e.g., email marketing). Also, if we want to deploy this work for real-world services, tourist package and locating in the top of the recommendation list to a lack of interest of those packages, relevant (desirable) travel packages in the test set may be just a small fraction of the entire relevant ones that are actually of interest to each tourist.

TRAVEL PACKAGE:

We aim to make personalized travel package recommendations for the tourists. Thus, the users

are the tourists and the items are the existing packages, and we exploit a real-world travel data set provided by a travel company packages in a way that each tourist has traveled at least two different packages.

First, it is very sparse, and each tourist has only a few travel records. The extreme sparseness of the data leads to difficulties for using traditional recommendation techniques, such as collaborative filtering. For example, it is hard to find the credible nearest neighbors for the tourists because there are very few cotraveling packages.

Second, the travel data has strong time dependence. The travel packages often have a life cycle along with the change to the business demand, i.e., they only last for a certain period. In contrast, most of the landscapes will still be active after the original package has been discarded. These landscapes can be used to form new packages together with some other landscapes. Thus, we can observe that the landscapes are more sustainable and important than the package itself.

Third, landscape has some intrinsic features like the geographic location and the right travel seasons. Only the landscapes with similar spatial-temporal features are suitable for the same packages, i.e., the landscapes in one package have spatial-temporal autocorrelations and follow the first law of geography-everything is related to everything else, but the nearby things are more related than distant things.

Fourth, the tourists will consider both time and financial costs before they accept a package. This is quite different from the traditional recommendations where the cost of an item is usually not a concern. Thus, it is very important to profile the tourists based on their interests as well as the time and the money they can afford. Since the package with a higher price often tends to have more time and vice versa, in this paper we only take the price factor into consideration.

Fifth, people often travel with their friends, family, or colleagues. Even when two tourists in the same travel group are totally strangers, there must be some reasons for the travel company to put them together. For instance, they may be of the same age or have the same travel schedule. Hence, it is also very important to understand the relationships among the tourists in the same travel group. This understanding can help to form the travel group. Last but not least, few tourist ratings are available for travel packages. However, we can see that every choice of a travel package indicates the strong interest of the tourist in the content provided in the package.

TRAST MODEL:

TRAST model, all the tourists’ travel preferences are represented by relationship distributions. For a set of tourists, who want to travel the same package, we can use their relationship distributions as features to cluster them, so as to put them into different travel groups. Thus, in this scenario, many clustering methods can be adopted. Since choosing clustering algorithm is beyond the scope of this paper, in the experiments, we refer to K-means, one of the most popular clustering algorithms. Thus, the TRAST model can be used as an assessment for travel group automatic formation. Indeed, in real applications, when generating a travel group, some more external constraints, such as tourists’ travel date requirements, the travel company’s travel group schedule should also be considered by TRAST1 to represent the latent relationships directly.

TRAST model, the purchases of the tourists in each travel group are summed up as one single expense record and, thus, it has more complex generative process. We can understand this process by a simple example. Assume that two selected tourists in a travel group (U00d) are u1 and u2, who are young and dating with each other. Now, they decide to travel in winter (Sd) and the destination is North America (Ad). To generate a travel landscape (l), we first extract a relationship (r, e.g., lover), and then find a topic (t) for lovers to travel in the winter (e.g., skiing). Finally, based on this skiing topic and the selected travel area (e.g., Northeast America), we draw a landscape (e.g., Stowe, Vermont).

RECOMMENDATIONS:

The evaluations in previous sections are mainly focused on the individual (personalized) recommendations. Since there are tourists who frequently travel together, it is interesting to know whether the latent variables (e.g., the topics of each individual tourist and the relationships of a travel group) as well as the cocktail approaches are useful for making recommendations to a group of tourists. To this end, we performed an experimental study on group recommendations.

We adopt the widely used degree of agreement (DOA) and Top-K [23] as the evaluation metrics. Also, a simple user study was conducted and volunteers were invited to rate the recommendations. For comparison, we recorded the best performance of each algorithm by tuning their parameters, and we also set some general rules for fair comparison. For instance, for collaborative filtering-based methods, we usually consider the contribution of the nearest neighbors with similarity values larger than 0.

CHAPTER 5

5.0 SYSTEM STUDY:

5.1 FEASIBILITY STUDY:

The feasibility of the project is analyzed in this phase and business proposal is put forth with a very general plan for the project and some cost estimates. During system analysis the feasibility study of the proposed system is to be carried out. This is to ensure that the proposed system is not a burden to the company.  For feasibility analysis, some understanding of the major requirements for the system is essential.

Three key considerations involved in the feasibility analysis are      

• ECONOMICAL FEASIBILITY
• TECHNICAL FEASIBILITY
• SOCIAL FEASIBILITY

5.1.1 ECONOMICAL FEASIBILITY:                  

This study is carried out to check the economic impact that the system will have on the organization. The amount of fund that the company can pour into the research and development of the system is limited. The expenditures must be justified. Thus the developed system as well within the budget and this was achieved because most of the technologies used are freely available. Only the customized products had to be purchased.

5.1.2 TECHNICAL FEASIBILITY:

This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.  

5.1.3 SOCIAL FEASIBILITY:  

The aspect of study is to check the level of acceptance of the system by the user. This includes the process of training the user to use the system efficiently. The user must not feel threatened by the system, instead must accept it as a necessity. The level of acceptance by the users solely depends on the methods that are employed to educate the user about the system and to make him familiar with it. His level of confidence must be raised so that he is also able to make some constructive criticism, which is welcomed, as he is the final user of the system.

5.2 SYSTEM TESTING:

Testing is a process of checking whether the developed system is working according to the original objectives and requirements. It is a set of activities that can be planned in advance and conducted systematically. Testing is vital to the success of the system. System testing makes a logical assumption that if all the parts of the system are correct, the global will be successfully achieved. In adequate testing if not testing leads to errors that may not appear even many months. This creates two problems, the time lag between the cause and the appearance of the problem and the effect of the system errors on the files and records within the system. A small system error can conceivably explode into a much larger Problem. Effective testing early in the purpose translates directly into long term cost savings from a reduced number of errors. Another reason for system testing is its utility, as a user-oriented vehicle before implementation. The best programs are worthless if it produces the correct outputs.

5.2.1 UNIT TESTING:

A program represents the logical elements of a system. For a program to run satisfactorily, it must compile and test data correctly and tie in properly with other programs. Achieving an error free program is the responsibility of the programmer. Program  testing  checks  for  two  types  of  errors:  syntax  and  logical. Syntax error is a program statement that violates one or more rules of the language in which it is written. An improperly defined field dimension or omitted keywords are common syntax errors. These errors are shown through error message generated by the computer. For Logic errors the programmer must examine the output carefully.

UNIT TESTING:

Description	Expected result
Test for application window properties.	All the properties of the windows are to be properly aligned and displayed.
Test for mouse operations.	All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions.

5.1.3 FUNCTIONAL TESTING:

Functional testing of an application is used to prove the application delivers correct results, using enough inputs to give an adequate level of confidence that will work correctly for all sets of inputs. The functional testing will need to prove that the application works for each client type and that personalization function work correctly.When a program is tested, the actual output is compared with the expected output. When there is a discrepancy the sequence of instructions must be traced to determine the problem.  The process is facilitated by breaking the program into self-contained portions, each of which can be checked at certain key points. The idea is to compare program values against desk-calculated values to isolate the problems.

FUNCTIONAL TESTING:

Description	Expected result
Test for all modules.	All peers should communicate in the group.
Test for various peer in a distributed network framework as it display all users available in the group.	The result after execution should give the accurate result.

5.1. 4 NON-FUNCTIONAL TESTING:

 The Non Functional software testing encompasses a rich spectrum of testing strategies, describing the expected results for every test case. It uses symbolic analysis techniques. This testing used to check that an application will work in the operational environment. Non-functional testing includes:

• Load testing
• Performance testing
• Usability testing
• Reliability testing
• Security testing

5.1.5 LOAD TESTING:

An important tool for implementing system tests is a Load generator. A Load generator is essential for testing quality requirements such as performance and stress. A load can be a real load, that is, the system can be put under test to real usage by having actual telephone users connected to it. They will generate test input data for system test.

Load Testing

Description	Expected result
It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received.	Should designate another active node as a Server.

5.1.5 PERFORMANCE TESTING:

Performance tests are utilized in order to determine the widely defined performance of the software system such as execution time associated with various parts of the code, response time and device utilization. The intent of this testing is to identify weak points of the software system and quantify its shortcomings.

PERFORMANCE TESTING:

Description	Expected result
This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management.	Should handle large input values, and produce accurate result in a  expected time.

5.1.6 RELIABILITY TESTING:

The software reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time and it is being ensured in this testing. Reliability can be expressed as the ability of the software to reveal defects under testing conditions, according to the specified requirements. It the portability that a software system will operate without failure under given conditions for a given time interval and it focuses on the behavior of the software element. It forms a part of the software quality control team.

RELIABILITY TESTING:

Description	Expected result
This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application.	In case of failure of  the server an alternate server should take over the job.

5.1.7 SECURITY TESTING:

Security testing evaluates system characteristics that relate to the availability, integrity and confidentiality of the system data and services. Users/Clients should be encouraged to make sure their security needs are very clearly known at requirements time, so that the security issues can be addressed by the designers and testers.

SECURITY TESTING:

Description	Expected result
Checking that the user identification is authenticated.	In case failure it should not be connected in the framework.
Check whether group keys in a tree are shared by all peers.	The peers should know group key in the same group.

5.1.7 WHITE BOX TESTING:

White  box  testing,  sometimes called  glass-box  testing is  a test  case  design method  that  uses  the  control  structure  of the procedural  design  to  derive  test  cases. Using  white  box  testing  method,  the software  engineer  can  derive  test  cases. The White box testing focuses on the inner structure of the software structure to be tested.

5.1.8 WHITE BOX TESTING:

Description	Expected result
Exercise all logical decisions on their true and false sides.	All the logical decisions must be valid.
Execute all loops at their boundaries and within their operational bounds.	All the loops must be finite.
Exercise internal data structures to ensure their validity.	All the data structures must be valid.

5.1.9 BLACK BOX TESTING:

Black box testing, also called behavioral testing, focuses on the functional requirements of the software.  That  is,  black  testing  enables  the software engineer  to  derive  sets  of  input  conditions  that  will  fully  exercise  all  functional requirements  for  a  program.  Black box testing is not alternative to white box techniques.  Rather  it  is  a  complementary  approach  that  is  likely  to  uncover  a different  class  of  errors  than  white box  methods. Black box testing attempts to find errors which focuses on inputs, outputs, and principle function of a software module. The starting point of the black box testing is either a specification or code. The contents of the box are hidden and the stimulated software should produce the desired results.

5.1.10 BLACK BOX TESTING:

Description	Expected result
To check for incorrect or missing functions.	All the functions must be valid.
To check for interface errors.	The entire interface must function normally.
To check for errors in a data structures or external data base access.	The database updation and retrieval must be done.
To check for initialization and termination errors.	All the functions and data structures must be initialized properly and terminated normally.

All the above system testing strategies are carried out in as the development, documentation and institutionalization of the proposed goals and related policies is essential.

CHAPTER 6

6.0 SOFTWARE DESCRIPTION:

 

6.1 JAVA TECHNOLOGY:

Java technology is both a programming language and a platform.

 

The Java Programming Language

 

The Java programming language is a high-level language that can be characterized by all of the following buzzwords:

• Simple
  • Architecture neutral
  • Object oriented
  • Portable
  • Distributed     
  • High performance
  • Interpreted     
  • Multithreaded
  • Robust
  • Dynamic
  • Secure     

With most programming languages, you either compile or interpret a program so that you can run it on your computer. The Java programming language is unusual in that a program is both compiled and interpreted. With the compiler, first you translate a program into an intermediate language called Java byte codes —the platform-independent codes interpreted by the interpreter on the Java platform. The interpreter parses and runs each Java byte code instruction on the computer. Compilation happens just once; interpretation occurs each time the program is executed. The following figure illustrates how this works.

You can think of Java byte codes as the machine code instructions for the Java Virtual Machine (Java VM). Every Java interpreter, whether it’s a development tool or a Web browser that can run applets, is an implementation of the Java VM. Java byte codes help make “write once, run anywhere” possible. You can compile your program into byte codes on any platform that has a Java compiler. The byte codes can then be run on any implementation of the Java VM. That means that as long as a computer has a Java VM, the same program written in the Java programming language can run on Windows 2000, a Solaris workstation, or on an iMac.

6.2 THE JAVA PLATFORM:

A platform is the hardware or software environment in which a program runs. We’ve already mentioned some of the most popular platforms like Windows 2000, Linux, Solaris, and MacOS. Most platforms can be described as a combination of the operating system and hardware. The Java platform differs from most other platforms in that it’s a software-only platform that runs on top of other hardware-based platforms.

The Java platform has two components:

• The Java Virtual Machine (Java VM)
• The Java Application Programming Interface (Java API)

You’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported onto various hardware-based platforms.

The Java API is a large collection of ready-made software components that provide many useful capabilities, such as graphical user interface (GUI) widgets. The Java API is grouped into libraries of related classes and interfaces; these libraries are known as packages. The next section, What Can Java Technology Do? Highlights what functionality some of the packages in the Java API provide.

The following figure depicts a program that’s running on the Java platform. As the figure shows, the Java API and the virtual machine insulate the program from the hardware.

Native code is code that after you compile it, the compiled code runs on a specific hardware platform. As a platform-independent environment, the Java platform can be a bit slower than native code. However, smart compilers, well-tuned interpreters, and just-in-time byte code compilers can bring performance close to that of native code without threatening portability.

 

6.3 WHAT CAN JAVA TECHNOLOGY DO?

The most common types of programs written in the Java programming language are applets and applications. If you’ve surfed the Web, you’re probably already familiar with applets. An applet is a program that adheres to certain conventions that allow it to run within a Java-enabled browser.

However, the Java programming language is not just for writing cute, entertaining applets for the Web. The general-purpose, high-level Java programming language is also a powerful software platform. Using the generous API, you can write many types of programs.

An application is a standalone program that runs directly on the Java platform. A special kind of application known as a server serves and supports clients on a network. Examples of servers are Web servers, proxy servers, mail servers, and print servers. Another specialized program is a servlet.

A servlet can almost be thought of as an applet that runs on the server side. Java Servlets are a popular choice for building interactive web applications, replacing the use of CGI scripts. Servlets are similar to applets in that they are runtime extensions of applications. Instead of working in browsers, though, servlets run within Java Web servers, configuring or tailoring the server.

How does the API support all these kinds of programs? It does so with packages of software components that provides a wide range of functionality. Every full implementation of the Java platform gives you the following features:

• The essentials: Objects, strings, threads, numbers, input and output, data structures, system properties, date and time, and so on.
• Applets: The set of conventions used by applets.
• Networking: URLs, TCP (Transmission Control Protocol), UDP (User Data gram Protocol) sockets, and IP (Internet Protocol) addresses.
• Internationalization: Help for writing programs that can be localized for users worldwide. Programs can automatically adapt to specific locales and be displayed in the appropriate language.
• Security: Both low level and high level, including electronic signatures, public and private key management, access control, and certificates.
• Software components: Known as JavaBeans^TM, can plug into existing component architectures.
• Object serialization: Allows lightweight persistence and communication via Remote Method Invocation (RMI).
• Java Database Connectivity (JDBC^TM): Provides uniform access to a wide range of relational databases.

The Java platform also has APIs for 2D and 3D graphics, accessibility, servers, collaboration, telephony, speech, animation, and more. The following figure depicts what is included in the Java 2 SDK.

7.4 HOW WILL JAVA TECHNOLOGY CHANGE MY LIFE?

We can’t promise you fame, fortune, or even a job if you learn the Java programming language. Still, it is likely to make your programs better and requires less effort than other languages. We believe that Java technology will help you do the following:

• Get started quickly: Although the Java programming language is a powerful object-oriented language, it’s easy to learn, especially for programmers already familiar with C or C++.
• Write less code: Comparisons of program metrics (class counts, method counts, and so on) suggest that a program written in the Java programming language can be four times smaller than the same program in C++.
• Write better code: The Java programming language encourages good coding practices, and its garbage collection helps you avoid memory leaks. Its object orientation, its JavaBeans component architecture, and its wide-ranging, easily extendible API let you reuse other people’s tested code and introduce fewer bugs.
• Develop programs more quickly: Your development time may be as much as twice as fast versus writing the same program in C++. Why? You write fewer lines of code and it is a simpler programming language than C++.
• Avoid platform dependencies with 100% Pure Java: You can keep your program portable by avoiding the use of libraries written in other languages. The 100% Pure Java^TM Product Certification Program has a repository of historical process manuals, white papers, brochures, and similar materials online.
• Write once, run anywhere: Because 100% Pure Java programs are compiled into machine-independent byte codes, they run consistently on any Java platform.
• Distribute software more easily: You can upgrade applets easily from a central server. Applets take advantage of the feature of allowing new classes to be loaded “on the fly,” without recompiling the entire program.

6.5 ODBC:

 Microsoft Open Database Connectivity (ODBC) is a standard programming interface for application developers and database systems providers. Before ODBC became a de facto standard for Windows programs to interface with database systems, programmers had to use proprietary languages for each database they wanted to connect to. Now, ODBC has made the choice of the database system almost irrelevant from a coding perspective, which is as it should be. Application developers have much more important things to worry about than the syntax that is needed to port their program from one database to another when business needs suddenly change.

Through the ODBC Administrator in Control Panel, you can specify the particular database that is associated with a data source that an ODBC application program is written to use. Think of an ODBC data source as a door with a name on it. Each door will lead you to a particular database. For example, the data source named Sales Figures might be a SQL Server database, whereas the Accounts Payable data source could refer to an Access database. The physical database referred to by a data source can reside anywhere on the LAN.

The ODBC system files are not installed on your system by Windows 95. Rather, they are installed when you setup a separate database application, such as SQL Server Client or Visual Basic 4.0. When the ODBC icon is installed in Control Panel, it uses a file called ODBCINST.DLL. It is also possible to administer your ODBC data sources through a stand-alone program called ODBCADM.EXE. There is a 16-bit and a 32-bit version of this program and each maintains a separate list of ODBC data sources.

From a programming perspective, the beauty of ODBC is that the application can be written to use the same set of function calls to interface with any data source, regardless of the database vendor. The source code of the application doesn’t change whether it talks to Oracle or SQL Server. We only mention these two as an example. There are ODBC drivers available for several dozen popular database systems. Even Excel spreadsheets and plain text files can be turned into data sources. The operating system uses the Registry information written by ODBC Administrator to determine which low-level ODBC drivers are needed to talk to the data source (such as the interface to Oracle or SQL Server). The loading of the ODBC drivers is transparent to the ODBC application program. In a client/server environment, the ODBC API even handles many of the network issues for the application programmer.

The advantages of this scheme are so numerous that you are probably thinking there must be some catch. The only disadvantage of ODBC is that it isn’t as efficient as talking directly to the native database interface. ODBC has had many detractors make the charge that it is too slow. Microsoft has always claimed that the critical factor in performance is the quality of the driver software that is used. In our humble opinion, this is true. The availability of good ODBC drivers has improved a great deal recently. And anyway, the criticism about performance is somewhat analogous to those who said that compilers would never match the speed of pure assembly language. Maybe not, but the compiler (or ODBC) gives you the opportunity to write cleaner programs, which means you finish sooner. Meanwhile, computers get faster every year.

6.6 JDBC:

In an effort to set an independent database standard API for Java; Sun Microsystems developed Java Database Connectivity, or JDBC. JDBC offers a generic SQL database access mechanism that provides a consistent interface to a variety of RDBMSs. This consistent interface is achieved through the use of “plug-in” database connectivity modules, or drivers. If a database vendor wishes to have JDBC support, he or she must provide the driver for each platform that the database and Java run on.

To gain a wider acceptance of JDBC, Sun based JDBC’s framework on ODBC. As you discovered earlier in this chapter, ODBC has widespread support on a variety of platforms. Basing JDBC on ODBC will allow vendors to bring JDBC drivers to market much faster than developing a completely new connectivity solution.

JDBC was announced in March of 1996. It was released for a 90 day public review that ended June 8, 1996. Because of user input, the final JDBC v1.0 specification was released soon after.

The remainder of this section will cover enough information about JDBC for you to know what it is about and how to use it effectively. This is by no means a complete overview of JDBC. That would fill an entire book.

 

6.7 JDBC Goals:

Few software packages are designed without goals in mind. JDBC is one that, because of its many goals, drove the development of the API. These goals, in conjunction with early reviewer feedback, have finalized the JDBC class library into a solid framework for building database applications in Java.

The goals that were set for JDBC are important. They will give you some insight as to why certain classes and functionalities behave the way they do. The eight design goals for JDBC are as follows:

SQL Level API

The designers felt that their main goal was to define a SQL interface for Java. Although not the lowest database interface level possible, it is at a low enough level for higher-level tools and APIs to be created. Conversely, it is at a high enough level for application programmers to use it confidently. Attaining this goal allows for future tool vendors to “generate” JDBC code and to hide many of JDBC’s complexities from the end user.

SQL Conformance

SQL syntax varies as you move from database vendor to database vendor. In an effort to support a wide variety of vendors, JDBC will allow any query statement to be passed through it to the underlying database driver. This allows the connectivity module to handle non-standard functionality in a manner that is suitable for its users.

JDBC must be implemental on top of common database interfaces

The JDBC SQL API must “sit” on top of other common SQL level APIs. This goal allows JDBC to use existing ODBC level drivers by the use of a software interface. This interface would translate JDBC calls to ODBC and vice versa.

1. Provide a Java interface that is consistent with the rest of the Java system

Because of Java’s acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system.

• Keep it simple

This goal probably appears in all software design goal listings. JDBC is no exception. Sun felt that the design of JDBC should be very simple, allowing for only one method of completing a task per mechanism. Allowing duplicate functionality only serves to confuse the users of the API.

• Use strong, static typing wherever possible

Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.

• Keep the common cases simple

Because more often than not, the usual SQL calls used by the programmer are simple SELECT’s, INSERT’s, DELETE’s and UPDATE’s, these queries should be simple to perform with JDBC. However, more complex SQL statements should also be possible.

Finally we decided to precede the implementation using Java Networking.

And for dynamically updating the cache table we go for MS Access database.

Java ha two things: a programming language and a platform.

Java is a high-level programming language that is all of the following

Simple                                     Architecture-neutral

Object-oriented                       Portable

Distributed                              High-performance

Interpreted                              Multithreaded

Robust                                     Dynamic Secure

Java is also unusual in that each Java program is both compiled and interpreted. With a compile you translate a Java program into an intermediate language called Java byte codes the platform-independent code instruction is passed and run on the computer.

Compilation happens just once; interpretation occurs each time the program is executed. The figure illustrates how this works.

7.7 NETWORKING TCP/IP STACK:

The TCP/IP stack is shorter than the OSI one:

TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.

IP datagram’s:

The IP layer provides a connectionless and unreliable delivery system. It considers each datagram independently of the others. Any association between datagram must be supplied by the higher layers. The IP layer supplies a checksum that includes its own header. The header includes the source and destination addresses. The IP layer handles routing through an Internet. It is also responsible for breaking up large datagram into smaller ones for transmission and reassembling them at the other end.

UDP:

UDP is also connectionless and unreliable. What it adds to IP is a checksum for the contents of the datagram and port numbers. These are used to give a client/server model – see later.

TCP:

TCP supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.

Internet addresses

In order to use a service, you must be able to find it. The Internet uses an address scheme for machines so that they can be located. The address is a 32 bit integer which gives the IP address.

Network address:

Class A uses 8 bits for the network address with 24 bits left over for other addressing. Class B uses 16 bit network addressing. Class C uses 24 bit network addressing and class D uses all 32.

Subnet address:

Internally, the UNIX network is divided into sub networks. Building 11 is currently on one sub network and uses 10-bit addressing, allowing 1024 different hosts.

Host address:

8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.

Total address:

The 32 bit address is usually written as 4 integers separated by dots.

Port addresses

A service exists on a host, and is identified by its port. This is a 16 bit number. To send a message to a server, you send it to the port for that service of the host that it is running on. This is not location transparency! Certain of these ports are “well known”.

Sockets:

A socket is a data structure maintained by the system to handle network connections. A socket is created using the call socket. It returns an integer that is like a file descriptor. In fact, under Windows, this handle can be used with Read File and Write File functions.

#include <sys/types.h>

#include <sys/socket.h>

int socket(int family, int type, int protocol);

Here “family” will be AF_INET for IP communications, protocol will be zero, and type will depend on whether TCP or UDP is used. Two processes wishing to communicate over a network create a socket each. These are similar to two ends of a pipe – but the actual pipe does not yet exist.

6.8 JFREE CHART:

JFreeChart is a free 100% Java chart library that makes it easy for developers to display professional quality charts in their applications. JFreeChart’s extensive feature set includes:

A consistent and well-documented API, supporting a wide range of chart types;

A flexible design that is easy to extend, and targets both server-side and client-side applications;

Support for many output types, including Swing components, image files (including PNG and JPEG), and vector graphics file formats (including PDF, EPS and SVG);

JFreeChart is “open source” or, more specifically, free software. It is distributed under the terms of the GNU Lesser General Public Licence (LGPL), which permits use in proprietary applications.

6.8.1. Map Visualizations:

Charts showing values that relate to geographical areas. Some examples include: (a) population density in each state of the United States, (b) income per capita for each country in Europe, (c) life expectancy in each country of the world. The tasks in this project include: Sourcing freely redistributable vector outlines for the countries of the world, states/provinces in particular countries (USA in particular, but also other areas);

Creating an appropriate dataset interface (plus default implementation), a rendered, and integrating this with the existing XYPlot class in JFreeChart; Testing, documenting, testing some more, documenting some more.

6.8.2. Time Series Chart Interactivity

Implement a new (to JFreeChart) feature for interactive time series charts — to display a separate control that shows a small version of ALL the time series data, with a sliding “view” rectangle that allows you to select the subset of the time series data to display in the main chart.

 

6.8.3. Dashboards

There is currently a lot of interest in dashboard displays. Create a flexible dashboard mechanism that supports a subset of JFreeChart chart types (dials, pies, thermometers, bars, and lines/time series) that can be delivered easily via both Java Web Start and an applet.

 

6.8.4. Property Editors

The property editor mechanism in JFreeChart only handles a small subset of the properties that can be set for charts. Extend (or reimplement) this mechanism to provide greater end-user control over the appearance of the charts.

CHAPTER 7

APPENDIX

7.1 SAMPLE SOURCE CODE

7.2 SAMPLE OUTPUT

CHAPTER 8

CONCLUSION:

We present study on personalized travel package recommendation. Specifically, we first analyzed the unique characteristics of travel packages and developed the TAST model, a Bayesian network for travel package and tourist representation. The TAST model can discover the interests of the tourists and extract the spatial-temporal correlations among landscapes. Then, we exploited the TAST model for developing a cocktail approach on personalized travel package recommendation. This cocktail approach follows a hybrid recommendation strategy and has the ability to combine several constraints existing in the real-world scenario.

Furthermore, we extended the TAST model to the TRAST model, which can capture the relationships among tourists in each travel group. Finally, an empirical study was conducted on real-world travel data. Experimental results demonstrate that the TAST model can capture the unique characteristics of the travel packages, the cocktail approach can lead to better performances of travel package recommendation, and the TRAST model can be used as an effective assessment for travel group automatic formation. We hope these encouraging results could lead to many future works.