Automatic Scaling of Internet Applications for Cloud Computing Services

by

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.

g1

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.

helloWorld

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.

g3

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 JavaBeansTM, can plug into existing component architectures.
  • Object serialization: Allows lightweight persistence and communication via Remote Method Invocation (RMI).
  • Java Database Connectivity (JDBCTM): 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 JavaTM 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.