An initiative data prefetching scheme on the storage servers in distributed file systems for cloud computing. In this prefetching technique, the client machines are not substantially involved in the process of data prefetching, but the storage servers can directly prefetch the data after analyzing the history of disk I/O access events, and then send the prefetched data to the relevant client machines proactively. To put this technique to work, the information about client nodes is piggybacked onto the real client I/O requests, and then forwarded to the relevant storage server. Next, two prediction algorithms have been proposed to forecast future block access operations for directing what data should be fetched on storage servers in advance.
Finally, the prefetched data can be
pushed to the relevant client machine from the storage server. Through a series
of evaluation experiments with a collection of application benchmarks, we have
demonstrated that our presented initiative prefetching technique can benefit
distributed file systems for cloud environments to achieve better I/O
performance. In particular, configurationlimited client machines in the cloud
are not responsible for predicting I/O access operations, which can definitely
contribute to preferable system performance on them.
1.2 INTRODUCTION
The assimilation of distributed computing for search engines, multimedia websites, and data-intensive applications has brought about the generation of data at unprecedented speed. For instance, the amount of data created, replicated, and consumed in United States may double every three years through the end of this decade, according to the general, the file system deployed in a distributed computing environment is called a distributed file system, which is always used to be a backend storage system to provide I/O services for various sorts of dataintensive applications in cloud computing environments. In fact, the distributed file system employs multiple distributed I/O devices by striping file data across the I/O nodes, and uses high aggregate bandwidth to meet the growing I/O requirements of distributed and parallel scientific applications.
However, because distributed file systems scale both numerically and geographically, the network delay is becoming the dominant factor in remote file system access [26], [34]. With regard to this issue, numerous data prefetching mechanisms have been proposed to hide the latency in distributed file systems caused by network communication and disk operations. In these conventional prefetching mechanisms, the client file system (which is a part of the file system and runs on theclient machine) is supposed to predict future access by analyzing the history of occurred I/O access without any application intervention. After that, the client file system may send relevant I/O requests to storage servers for reading the relevant data in. Consequently, the applications that have intensive read workloads can automatically yield not only better use of available bandwidth, but also less file operations via batched I/O requests through prefetching.
On the other hand, mobile devices generally have limited processing power, battery life and storage, but cloud computing offers an illusion of infinite computing resources. For combining the mobile devices and cloud computing to create a new infrastructure, the mobile cloud computing research field emerged [45]. Namely, mobile cloud computing provides mobile applications with data storage and processing services in clouds, obviating the requirement to equip a powerful hardware configuration, because all resource-intensive computing can be completed in the cloud. Thus, conventional prefetching schemes are not the best-suited optimization strategies for distributed file systems to boost I/O performance in mobile clouds, since these schemes require the client file systems running on client machines to proactively issue prefetching requests after analyzing the occurred access events recorded by them, which must place negative effects to the client nodes.
Furthermore, considering only disk I/O events can
reveal the disk tracks that can offer critical information to perform I/O
optimization tactics certain prefetching techniques have been proposed in
succession to read the data on the disk in advance after analyzing disk I/O
traces. But, this kind of prefetching only works for local file systems, and
the prefetched data iscached on the local machine to fulfill the application’s
I/O requests passively in brief, although block access history reveals the
behavior of disk tracks, there are no prefetching schemes on storage servers in
a distributed file system for yielding better system performance. And the
reason for this situation is because of the difficulties in modeling the block
access history to generate block access patterns and deciding the destination
client machine for driving the prefetched data from storage servers.
1.3 LITRATURE SURVEY
PARTIAL REPLICATION OF METADATA TO ACHIEVE HIGH METADATA AVAILABILITY IN PARALLEL FILE SYSTEMS
AUTHOR: J. Liao, Y. Ishikawa
PUBLISH: In the Proceedings of 41st International Conference on Parallel Processing (ICPP ’12), pp. 168–177, 2012.
EXPLANATION:
This paper presents PARTE, a
prototype parallel file system with active/standby configured metadata servers
(MDSs). PARTE replicates and distributes a part of files’ metadata to the
corresponding metadata stripes on the storage servers (OSTs) with a per-file
granularity, meanwhile the client file system (client) keeps certain sent
metadata requests. If the active MDS has crashed for some reason, these client
backup requests will be replayed by the standby MDS to restore the lost
metadata. In case one or more backup requests are lost due to network problems
or dead clients, the latest metadata saved in the associated metadata stripes
will be used to construct consistent and up-to-date metadata on the standby
MDS. Moreover, the clients and OSTs can work in both normal mode and recovery
mode in the PARTE file system. This differs from conventional active/standby
configured MDSs parallel file systems, which hang all I/O requests and metadata
requests during restoration of the lost metadata. In the PARTE file system,
previously connected clients can continue to perform I/O operations and
relevant metadata operations, because OSTs work as temporary MDSs during that
period by using the replicated metadata in the relevant metadata stripes.
Through examination of experimental results, we show the feasibility of the
main ideas presented in this paper for providing high availability metadata
service with only a slight overhead effect on I/O performance. Furthermore,
since previously connected clients are never hanged during metadata recovery,
in contrast to conventional systems, a better overall I/O data throughput can
be achieved with PARTE.
EVALUATING PERFORMANCE AND ENERGY IN FILE SYSTEM SERVER WORKLOADS
AUTHOR: P. Sehgal, V. Tarasov, E. Zadok
PUBLISH: the 8th USENIX Conference on File and Storage Technologies (FAST ’10), pp.253-266, 2010.
EXPLANATION:
Recently, power has emerged as a critical factor in designing components of storage systems, especially for power-hungry data centers. While there is some research into power-aware storage stack components, there are no systematic studies evaluating each component’s impact separately. This paper evaluates the file system’s impact on energy consumption and performance. We studied several popular Linux file systems, with various mount and format options, using the FileBench workload generator to emulate four server workloads: Web, database, mail, and file server. In case of a server node consisting of a single disk, CPU power generally exceeds disk-power consumption. However, file system design, implementation, and available features have a signifi- cant effect on CPU/disk utilization, and hence on performance and power. We discovered that default file system options are often suboptimal, and even poor. We show that a careful matching of expected workloads to file system types and options can improve power-performance efficiency by a factor ranging from 1.05 to 9.4 times.
FLEXIBLE, WIDEAREA STORAGE FOR DISTRIBUTED SYSTEMS WITH WHEELFS
AUTHOR: J. Stribling, Y. Sovran, I. Zhang and R. Morris et al
PUBLISH: In Proceedings of the 6th USENIX symposium on Networked systems design and implementation (NSDI’09), USENIX Association, pp. 43–58, 2009.
EXPLANATION:
WheelFS is a wide-area distributed storage system intended to help multi-site applications share data and gain fault tolerance. WheelFS takes the form of a distributed file system with a familiar POSIX interface. Its design allows applications to adjust the tradeoff between prompt visibility of updates from other sites and the ability for sites to operate independently despite failures and long delays. WheelFS allows these adjustments via semantic cues, which provide application control over consistency, failure handling, and file and replica placement. WheelFS is implemented as a user-level file system and is deployed on PlanetLab and Emulab. Three applications (a distributed Web cache, an email service and large file distribution) demonstrate that WheelFS’s file system interface simplifies construction of distributed applications by allowing reuse of existing software. These applications would perform poorly with the strict semantics implied by a traditional file system interface, but by providing cues to WheelFS they are able to achieve good performance. Measurements show that applications built on WheelFS deliver comparable performance to services such as CoralCDN and BitTorrent that use specialized wide-area storage systems.
CHAPTER 2
2.0 SYSTEM ANALYSIS
2.1 EXISTING SYSTEM:
The file system deployed in a
distributed computing environment is called a distributed file system, which is
always used to be a backend storage system to provide I/O services for various
sorts of data intensive applications in cloud computing environments. In fact,
the distributed file system employs multiple distributed I/O devices by
striping file data across the I/O nodes, and uses high aggregate bandwidth to
meet the growing I/O requirements of distributed and parallel scientific
applications benchmark to create OLTP workloads, since it is able to create
similar OLTP workloads that exist in real systems. All the configured client
file systems executed the same script, and each of them run several threads
that issue OLTP requests. Because Sysbench requires MySQL installed as a
backend for OLTP workloads, we configured mysqld process to 16 cores of storage
servers. As a consequence, it is possible to measure the response time to the
client request while handling the generated workloads.
2.1.1 DISADVANTAGES:
2.2 PROPOSED SYSTEM:
Proposed in succession to read the data on the disk in advance after analyzing disk I/O traces of prefetching only works for local file systems, and the prefetched data is cached on the local machine to fulfill the application’s I/O requests passively. In brief, although block access history reveals the behavior of disk tracks, there are no prefetching schemes on storage servers in a distributed file system for yielding better system performance. And the reason for this situation is because of the difficulties in modeling the block access history to generate block access patterns and deciding the destination client machine for driving the prefetched data from storage servers. To yield attractive I/O performance in the distributed file system deployed in a mobile cloud environment or a cloud environment that has many resource-limited client machines, this paper presents an initiative data prefetching mechanism. The proposed mechanism first analyzes disk I/O tracks to predict the future disk I/O access so that the storage servers can fetch data in advance, and then forward the prefetched data to relevant client file systems for future potential usages.
This paper makes the following two contributions:
1) Chaotic time series prediction and linear
regression prediction to forecast disk I/O access. We have modeled the disk I/O
access operations, and classified them into two kinds of access patterns, i.e.
the random access pattern and the sequential access pattern. Therefore, in
order to predict the future I/O access that belongs to the different access
patterns as accurately as possible (note that the future I/O access indicates
what data will be requested in the near future), two prediction algorithms
including the chaotic time series prediction algorithm and the linear
regression prediction algorithm have been proposed respectively. 2) Initiative
data prefetching on storage servers. Without any intervention from client file
systems except for piggybacking their information onto relevant I/O requests to
the storage servers. The storage servers are supposed to log disk I/O access
and classify access patterns after modeling disk I/O events. Next, by properly
using two proposed prediction algorithms, the storage servers can predict the
future disk I/O access to guide prefetching data. Finally, the storage servers
proactively forward the prefetched data to the relevant client file systems for
satisfying future application’s requests.
2.2.1 ADVANTAGES:
2.3.1 HARDWARE REQUIREMENT:
JAVA
CHAPTER 3
3.0 SYSTEM DESIGN:
Data Flow Diagram / Use Case Diagram / Flow Diagram:
External sources or destinations, which may be people or organizations or other entities
Here the data referenced by a process is stored and retrieved.
People, procedures or devices that produce data’s in the physical component is not identified.
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:
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:
I/O
ACCESS PREDICTION
4.1 ALGORITHM
MARKOV MODEL PREDICTION ALGORITHM
LINEAR
PREDICTION ALGORITHM
4.2 MODULES:
SERVER CLIENT MODULE:
DISTRIBUTED FILE SYSTEMS:
INITIATIVE DATA PREFETCHING:
ANALYSIS
OF PREDICTIONS:
4.3 MODULE DESCRIPTION:
SERVER CLIENT MODULE:
DISTRIBUTED FILE SYSTEMS:
INITIATIVE DATA PREFETCHING:
ANALYSIS
OF PREDICTIONS:
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
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.
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:
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:
Java technology is both a programming language and a platform.
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.
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:
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.
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 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.
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:
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.
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.
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.
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.
Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.
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.
The TCP/IP stack is shorter than the OSI one:
TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.
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 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 supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.
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.
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.
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.
8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.
The 32 bit address is usually written as 4 integers separated by dots.
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”.
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.
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.
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.
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.
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
7.0 APPENDIX
7.1 SAMPLE SCREEN SHOTS:
7.2
SAMPLE SOURCE CODE:
CHAPTER 8
8.1 CONCLUSION AND FUTURE WORK:
We have proposed, implemented and evaluated an initiative data prefetching approach on the storage servers for distributed file systems, which can be employed as a backend storage system in a cloud environment that may have certain resource-limited client machines. To be specific, the storage servers are capable of predicting future disk I/O access to guide fetching data in advance after analyzing the existing logs, and then they proactively push the prefetched data to relevant client file systems for satisfying future applications’ requests.
Purpose of effectively modeling disk I/O access patterns and accurately forwarding the prefetched data, the information about client file systems is piggybacked onto relevant I/O requests, and then transferred from client nodes to corresponding storage server nodes. Therefore, the client file systems running on the client nodes neither log I/O events nor conduct I/O access prediction; consequently, the thin client nodes can focus on performing necessary tasks with limited computing capacity and energy endurance.
Initiative prefetching scheme can be
applied in the distributed file system for a mobile cloud computing
environment, in which there are many tablet computers and smart terminals. The
current implementation of our proposed initiative prefetching scheme can
classify only two access patterns and support two corresponding prediction
algorithms for predicting future disk I/O access. We are planning to work on
classifying patterns for a wider range of application benchmarks in the future
by utilizing the horizontal visibility graph technique applying network delay
aware replica selection techniques for reducing network transfer time when
prefetching data among several replicas is another task in our future work.