AbstractIn the current network environment it is difficult to resolve the problems in network performance. By increasing the performance of energy oriented networking applications, a new network approach is introduced called, Green Computing. Some streaming analysis and measurements are created for preventing the energy streams in architecture. Traditional packet processing engine method is used to represent the most energy saving components of network devices and maintain the traffic overload. A data center resource management framework to support green computing is proposed. This framework is composed of the power and workload management, and capacity planning schemes. While an action of power and workload management is performed in a short-term basis (e.g., fraction of minute), a decision of capacity planning is made in a long-term basis (e.g., few months). This paper mainly addresses a capacity planning problem. With the power and workload management, we formulate a capacity planning optimization as a stochastic programming model. The solution is the number of servers to be installed and deployed in a data center over multiple periods. The objective of this optimization model is to minimize the long-term cost under workload demand uncertainty. From the performance evaluation, with the proposed optimization model for the capacity planning scheme, the total cost to operate the data center in the long-term basis can be minimized while the job waiting time and job blocking probability are maintained below the target thresholds.

TABLE OF CONTENTSCHAPTER NO. TITLE PAGE NO.Abstractlist of figureslist of abbreviations1introduction1.1. Energy-Aware Network Performance1.2. Capacity Planning1.3. The Proposed Queuing Model1.4. Resource Management Framework2LITERATURE SURVEY2.1 Green Computing Technologies2.2 Green Support for PC-based Software Router2.3 Energy-Aware Load Balancing for Parallel Packet Processing2.4 Load Balancing for Parallel Forwarding2.6 Existing System2.7 Proposed System3SYSTEM ORGANIZATION3.1 System Architecture Design3.2 Feature Extraction3.3 Data Flow Diagram

4PROPOSED SYSTEM4.1 Modules4.1.1. Network Module4.1.2. Delay Tolerant Network4.1.3. Dynamic Routing5REQUIREMENT SPECIFICATION5.1 System Requirements5.1.1 Hardware Required5.1.2 Software Required5.2 Java in Detail6IMPLEMENTATION RESULT6.1 Coding6.2 Screen Shots7conclusion and future work8 REFERENCES

CHAPTER 1INTRODUCTIONIn the last few years power consumption has shown a growing and alarming trend in all industrial sectors, and particularly in Information and Communication Technology (ICT). Public organizations, Internet Service Providers (ISPs) and Telecom operators reported alarming statistics of network energy requirements and of the related carbon footprint. The study of power-saving network devices has been based in recent years on the possibility of adapting network energy requirements to the actual traffic loads. Indeed, it is well known that network links and devices are generally provisioned or busy or rush hour load, which typically exceeds their average utilization by a wide margin. Although this margin is seldom reached, network devices are designed on its basis and, consequently, their power consumption remains more or less constant even in the presence of fluctuating traffic loads. Thus, the key of any advanced power saving criteria resides in dynamically adapting resources, provided at the network, link or equipment level, to current traffic requirements and loads. In this respect, current green computing approaches have been based on numerous energy-related criteria, to be applied in particular to network equipment and component interfaces. Despite the great progresses of optics in transmission and switching, it is well known that todays networks still rely very strongly on electronics. With the constant increase of power cost and awareness of global warming, the green computing concept has been introduced to improve an efficiency of computing resource usage. Power management is one of the major approaches in green computing to minimize the power consumption. In a distributed system, power management is generally implemented in the network level in which the operation mode of the computing resource (e.g., server) can be controlled remotely by the centralized controller (e.g., batch scheduler). This power management has to operate jointly with the workload management to achieve an optimal performance. In this case, the workload (i.e., jobs) could be processed by the local data center (i.e., in-house computing resource) or sent to a third party processing service (e.g., cloud computing) when the computing resource of the data center is heavily occupied(e.g., due to the instantaneous peak workload). With the power and workload management, not only the power consumption can be reduced but also met the performance requirements of workload processing. For the owner of a data center, the capacity planning is an important issue. Given the power and workload management, an optimal size of the data center (e.g., the optimal number of servers) has to be determined so that the long-term cost is minimized, while the processing capacity is sufficient to the workload submitted from users of the data center. One of the most challenging issues in designing the power and workload management as well as the capacity planning schemes is due to the uncertainty of the workload. That is, processing jobs can be generated by the users randomly and unknown a priori. Therefore, to achieve the objective and to meet all constraints of the distributed system, a stochastic optimization model would be required.

Fig1. System Model of a Data Center1.1. ENERGY-AWARE NETWORK PERFORMANCEThe current generation of network devices does not support power scaling functionalities. However, power management is a key feature in todays processors across all market segments, and it is rapidly evolving also in other HW technologies. In this process we first introduce the ACPI (Advanced Configuration and Power Interface) specification and how it makes AR and LPI capabilities accessible at the software (SW) layer. Then, in the second part we discuss the impact of AR and LPI on the forwarding performance of a network device, and how these two capabilities may interact between themselves.

1.2 Capacity PlanningCapacity planning or system planning is an important issue in distributed system management. This capacity planning is required to ensure that the computing resource meets users demand while the cost is minimized. Most of the works related to the capacity planning issue are based on the estimation of workload. In the resource management framework was proposed for high-performance computing system which consists of in-house computing resource, and utility computing service provider. In this system, the workload can be processed by in-house resource in which the extra capacity required to meet the performance requirement can be purchased from the utility computing service provider. The heuristic planning algorithms to assign workload to the available resource were developed. In the capacity planning scheme for grid computing based on advance resource reservation to support quality of service (QoS) was proposed. The resource allocation based on heuristic algorithm was proposed to improve the system utilization and meet QoS requirement. The negotiation protocol was also introduced to support resource reservation. In the capacity management tool for computing resource pools was introduced. This tool uses the traces of application workload to analyze and to simulate the workload assignment so that the performance requirement can be met. The optimization search-based algorithm was developed to assign workload to the available computing resource to achieve an objective of the system (e.g., maximum resource utilization). In the profile driven analytical model was proposed to estimate performance characteristics of processing workload. Then, the heuristic method was developed to determine the number of resources for the workload. However, all works in the literature related to capacity planning ignored the optimization with the uncertainty of workload demand. Although some works proposed the optimization algorithms for capacity planning of distributed system, the impact of power and workload management was not jointly considered. In the capacity planning optimization model with the power and workload management scheme was formulated to determine the optimal number of servers required in new data centers, to locate the new data centers to appropriate areas, and to distribute computing jobs to the data centers where energy costs can be minimized. However, this work did not take the uncertainty of workload demand into account.1.3 The Proposed Queuing ModeConsidering the assumption to send every packet composing a batch to a single pipeline, we can outline that the model we propose corresponds to a Mx/D/1/SET queuing system. For each pipeline, packets arrive in batches with exponentially distributed inter-arrival times with average rate, and are served at a fixed rate. In order to take the LPI transition periods into account, the model considers deterministic server setup times. When the system becomes empty, the server is turned off. The system returns operational only when a batch of packets rives. At this point in time service can begin only after an interval has elapsed. The next three sub-sections introduce the analytical model and its specialization to our case. We derive the PGF and the stationary probabilities of the Mx/D/1/SET queuing system; we express the servers idle and busy periods. Then, we propose an approximation for the packet loss probability in the case of a finite buffer of size N and we derive network- and energy aware performance indexes.1.4 Resource Management FrameworkTo achieve the optimal performance and minimum cost, the resource management framework manages servers in a data center. This framework is composed of the power and workload management, and capacity planning schemes. The action of power and workload management scheme is performed by the batch scheduler to control the server mode switching and job outsourcing in the short-term basis for every time slot (e.g., fraction of minute). Alternatively, the decision of capacity planning scheme is made by the data center owner to determine the size of data center (i.e., the number of servers to be in the center) in the long-term basis (e.g., for every few months).

Fig.1.4. Interaction between power and workload management, and capacity planning schemes in the proposed resource management framework.An interaction between these power and workload management, and capacity planning schemes. The power and workload management yields the optimal policy for the short-term plan. This optimal policy provides the total cost information to operate the data center. In particular, the capacity planning scheme utilizes the total cost information to determine the optimal number of servers in the data center.

CHAPTER 2LITERATURE SURVEYLiterature survey is the most important step in software development process. Before developing the tool it is necessary to determine the time factor, economy and company strength. Once these things are satisfied, then the next step is to determine which operating system and language can be used for developing the tool. Once the programmers start building the tool the programmers need lot of external support. This support can be obtained from senior programmers, from book or from websites. Before building the system the above consideration are taken into account for developing the proposed system.The major part of the project development sector considers and fully survey all the required needs for developing the project. For every project Literature survey is the most important sector in software development process. Before developing the tools and the associated designing it is necessary to determine and survey the time factor, resource requirement, man power, economy, and company strength. Once these things are satisfied and fully surveyed, then the next step is to determine about the software specifications in the respective system such as what type of operating system the project would require, and what are all the necessary software are needed to proceed with the next step such as developing the tools, and the associated operations.2.1 Green computing Technologies and the Art of Trading-Off - R. Bolla, R. BruschiIn this contribution, we focus on energy-aware devices able to reduce their energy requirements by adapting their performance. We propose an analytical model to accurately represent the impact of green computing technologies (i.e., low power idle and adaptive rate) on network- and energy-aware performance indexes. The model has been validated with experimental results, performed by using energy-aware software routers and real-world traffic traces. The achieved results demonstrate how the proposed model can effectively represent energy- and network-aware performance indexes. Moreover, also an optimization procedure based on the model has been proposed and experimentally evaluated. The procedure aims at dynamically adapting the energy-aware device configuration to minimize energy consumption, while coping with incoming traffic volumes and meeting network performance constraints.2.2 Green Support for PC-based Software Router: Performance Evaluation and Modeling - R. Bolla, R. Bruschi, and A. RanieriWe consider a new generation of COTS software routers (SRs), able to effectively exploit multi-Core/CPU HW platforms. Our main objective is to evaluate and to model the impact of power saving mechanisms, generally included in today's COTS processors, on the SR networking performance and behavior. To this purpose, we separately characterized the roles of both HW and SW layers through a large set of internal and external experimental measurements, obtained with a heterogeneous set of HW platforms and SR setups. Starting from this detailed measure analysis, we propose a simple model, able to represent the SR performance with a high accuracy level in terms of packet throughput and related power consumption. The proposed model can be effectively applied inside "green optimization" mechanisms in order to minimize power consumption, while maintaining a certain SR performance target.2.3 Energy-Aware Load Balancing for Parallel Packet Processing Engines - R. Bolla and R. BruschiIn this approach, we consider energy-aware network devices (e.g. routers, switches, etc.) able to trade their energy consumption for packet forwarding performance by means of both low power idle and adaptive rate schemes. We focus on state-of-the-art packet processing engines, which generally represent the most energy-starving components of network devices, and which are often composed of a number of parallel pipelines to divide and conquer the incoming traffic load. Our goal is to control both the power configuration of pipelines, and the way to distribute traffic flows among them, in order to optimize the trade-off between energy consumption and network performance indexes. With this aim, we propose and analyze a constrained optimization policy, which try to find the best trade-off between power consumption and packet latency times. In order to deeply understand the impact of such policy, a number of tests have been performed by using experimental data from SW router architectures and real-world traffic traces.2.4 Load Balancing for Parallel Forwarding - W. Shi, M. MacGregor, and P. GburzynskiWorkload distribution is critical to the performance of network processor based parallel forwarding systems. Scheduling schemes that operate at the packet level, e.g., round-robin, cannot preserve packet-ordering within individual TCP connections. Moreover, these schemes create duplicate information in processor caches and therefore are inefficient in resource utilization. Hashing operates at the flow level and is naturally able to maintain per-connection packet ordering; besides, it does not pollute caches. A pure hash-based system, however, cannot balance processor load in the face of highly skewed flow-size distributions in the Internet; usually, adaptive methods are needed. In this paper, based on measurements of Internet traffic, we examine the sources of load imbalance in hash-based scheduling schemes. We prove that under certain Zipf-like flow-size distributions, hashing alone is not able to balance workload. We introduce a new metric to quantify the effects of adaptive load balancing on overall forwarding performance. To achieve both load balancing and efficient system resource utilization, we propose a scheduling scheme that classifies Internet flows into two categories: the aggressive and the normal, and applies different scheduling policies to the two classes of flows. Compared with most state-of-the-art parallel forwarding schemes, our work exploits flow-level Internet traffic characteristics.

2.5 Existing SystemThe main challenge in the implementation of existing schemes lies in how to effectively deal with the interruptions of network connectivity and node failures. Thus, the existing schemes have been reported to seriously suffer from long delivery delays and/or large message loss ratio. Although improved in terms of performance, the previously reported Stream based routing schemes are subject to the following problems and implementation difficulties. Disadvantages: These schemes inevitably take a large number of energy consumption, and a vast amount of transmission bandwidth and nodal memory space, which could easily exhaust the network resource. They suffer from disagreement in case of high traffic loads, when packet drops could result in a significant degradation of performance and scalability. Limited Visibility under the guided measurements. Measurement wastages are high during the detection of latencies.

2.6 Proposed SystemA novel network routing technique is proposed is introduced, which aims to overcome the shortcomings of the previously reported network routing schemes which provides an efficient and cost effective data transfer. The main goal is to achieve a superb applicability to the network scenario with densely distributed hand-held devices. The main feature of this system is the strong capability in adaptation to the fluctuation of network status, traffic patterns/characteristics, user encounter behaviors, and user resource availability, so as to improve network performance in terms of message delivery ratio, message delivery delay, and number of transmissions.The proposed scheme is characterized by the ability of adapting itself to the observed network behaviors, which is made possible by employing an efficient time window based update mechanism for some network status parameters at each node. This approach uses time-window based update strategy because it is simple in implementation and robust against parameter fluctuation. Note that the network conditions could change very fast and make a completely event driven model.advantages: This scheme takes less energy consumption, and small amount of transmission bandwidth and less memory space, which could easily simplify the network resource and provide efficient data transmission. Provide good support in high traffic loads, which results in a significant performance and scalability. Visibility under the scalable measurements, packets can dynamically move under the bandwidth circumstances. Measurement wastages are low during the detection of latencies.

CHAPTER 3system architecture design

Figure 3.1.1 System Architecture Design3.2 Feature ExtractionTo provide a new routing methodology and maintaining the worst case complexities of the router architectures and real world traffic traces while empirically demonstrating a moderate increase in average resource utilization and power consumption procedures. The proposed scheme is characterized by the ability of adapting itself to the observed network behaviors, which is made possible by employing an efficient time window based update mechanism for some network status parameters at each node. This approach uses time-window based update strategy because it is simple in implementation and robust against parameter fluctuation. Note that the network conditions could change very fast and make a completely event driven model.

3.3 data flow diagramLevel 0EFFICIENT data center support AND OPTIMIZATION METHODS IN new ROUTING methods via GREEN computing

ServerClient

Figure 3.3.1. Level-0 - Data Flow Diagram

Level 1Split the data into PacketsSelect the data to Transmit

Client

Select the Router to transmit the packets

Analyze the bandwidth

Figure 3.3.2. Level-1 - Data Flow Diagram

Level - 2 Split the data into Packets

Select the data to Transmit

Client

Select the Router to transmit the packets

RouterTransmit the Packets

Transmit the packets to the sub routerAnalyze the Received Packets size and the capability of the sub routers

Receive the Packets with equal proportion

Sub router transmit the received packets to the server

Figure 3.3.3. Level-2 - Data Flow Diagram

CHAPTER 4proposed system4.1 Modules4.1.1. Network Module4.1.2. Delay Tolerant Network4.1.3. Dynamic Routing4.1.4. Randomization Process4.1.5. Routing Table Maintenance4.1.6. Load on Throughput

4.1.1 Network ModuleClient-server computing or networking is a distributed application architecture that partitions tasks or workloads between service providers (servers) and service requesters, called clients. Often clients and servers operate over a computer network on separate hardware. A server machine is a high-performance host that is running one or more server programs which share its resources with clients. A client also shares any of its resources; Clients therefore initiate communication sessions with servers which await (listen to) incoming requests.4.1.2 Delay Tolerant NetworkDTN is characterized by the lack of end-to-end paths for a given node pair for extended periods, which poses a completely different design scenario from that for conventional mobile adhoc networks (MANETs). Due to the intermittent connections in DTNs, a node is allowed to buffer a message and wait until the next hop node is found to continue storing and carrying the message. Such a process is repeated until the message reaches its destination. This model of routing is significantly different from that employed in the MANETs. DTN routing is usually referred to as encounter-based, store-carry-forward, or mobility-assisted routing, due to the fact that nodal mobility serves as a significant factor for the forwarding decision of each message. 4.1.3 Dynamic RoutingTo propose a distance-vector based algorithm for dynamic routing to improve the security of data transmission. We propose to rely on existing distance information exchanged among neighboring nodes (referred to as routers as well in this paper) for the seeking of routing paths. In many distance-vector-based implementations, e.g., those based on RIP, each node maintains a routing table in which each entry is associated with a tuple, and Next hop denote some unique destination node, an estimated minimal cost to send a packet to t, and the next node along the minimal-cost path to the destination node, respectively.4.1.4. Randomization ProcessIn order to minimize the probability that packets are eavesdropped over a specific link, a randomization process for packet deliveries, in this process, the previous next-hop for the source node s is identified in the first step of the process. Then, the process randomly picks up a neighboring node as the next hop for the current packet transmission. The exclusion for the next hop selection avoids transmitting two consecutive packets in the same link, and the randomized pickup prevents attackers from easily predicting routing paths for the coming transmitted packets.

4.1.5. Routing Table MaintenanceIn the network be given a routing table and a link table. We assume that the link table of each node is constructed by an existing link discovery protocol, such as the Hello protocol in. On the other hand, the construction and maintenance of routing tables are revised based on the well-known Bellman-Ford algorithm.

4.1.6. Load on Throughput Investigate the effect of traffic load on throughput for our proposed DDRA; the traffic is also generated based on variable-bit-rate applications such as file transfers over Transmission Control Protocol (TCP). The average packet size is 1,000 bytes, and source-destination pairs are chosen randomly with uniform probabilities.

CHAPTER 5requirement specification5.1 System Requirements5.1.1 Hardware Requirements System : Pentium IV 2.4 GHz Hard Disk : 40 GB Ram : 512 Mb 5.1.2 Software RequirementsOperating system : Windows XPTechnology Used: JAVACoding Language: Java Swings

5.2 JAVA in DetailJava TechnologyJava technology is both a programming language and a platform.The Java Programming LanguageThe 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 SecureWith 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. You can think of Java byte codes as the machine code instructions for the Java Virtual Machine (Java VM). Every Java interpreter, whether its 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. The Java PlatformA platform is the hardware or software environment in which a program runs. Weve 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 its 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) Youve already been introduced to the Java VM. Its 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 thats 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. What Can Java Technology Do? The most common types of programs written in the Java programming language are applets and applications. If youve surfed the Web, youre 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. How Will Java Technology Change My Life? We cant 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, its 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 peoples 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. 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 doesnt 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 isnt 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.JDBCIn 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 JDBCs 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.

JDBC GoalsFew 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: 1. 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 JDBCs complexities from the end user. 2. SQL ConformanceSQL 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. 3. 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. 4. Provide a Java interface that is consistent with the rest of the Java systemBecause of Javas acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system. 5. Keep it simpleThis 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. 6. 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. 7. Keep the common cases simple Because more often than not, the usual SQL calls used by the programmer are simple SELECTs, INSERTs, DELETEs and UPDATEs, these queries should be simple to perform with JDBC. However, more complex SQL statements should also be possible.

CHAPTER 6implementation and results6.1 CodingClientimport java.awt.BorderLayout;import java.awt.Color;import java.awt.Container;import java.awt.Font;import java.awt.event.ActionEvent;import java.awt.event.ActionListener;import java.awt.event.MouseAdapter;import java.awt.event.MouseEvent;import java.awt.event.WindowAdapter;import java.awt.event.WindowEvent;import java.io.BufferedInputStream;import java.io.BufferedOutputStream;import java.io.BufferedReader;import java.io.DataInputStream;import java.io.File;import java.io.FileInputStream;import java.io.IOException;import java.io.InputStreamReader;import java.net.ServerSocket;import java.net.Socket;import java.net.UnknownHostException;import java.util.Random;

import javax.swing.ImageIcon;import javax.swing.JButton;import javax.swing.JFileChooser;import javax.swing.JFrame;import javax.swing.JLabel;import javax.swing.JOptionPane;import javax.swing.JScrollPane;import javax.swing.JTextArea;import javax.swing.JTextField;import javax.swing.plaf.basic.BasicLookAndFeel;

public class client implements ActionListener {

String event;String rr;String rr1;String rr2;

String firststr;String secondstr;String thirdstr;String firstpckts;String secondpckts;String thirdpckts;String text;String text1;String text2;int i;int j;String ftf;String ftf1;String ftf2;int lentf;int lentf1;int lentf2;

int p1;int p2;int p3;

int q1;int q2;int q3;

int r1;int r2;int r3;

String rrA;String rrB;String rrC;

String len;int t1len;

String t1str;String tft1;String tft2;String tft3;

String stf;String ttf;String one;String ten;String fifty;String hundred;

String twohun;String threehun;String fourhun;String fivehun;String sixhun;String sevenhun;String eighthun;String ninehun;String tennhun;

public JButton b1 = new JButton("Browse");

public JButton b2 = new JButton("Split");public JButton b3 = new JButton("Send");public JLabel la1 = new JLabel("Select the file :");public JLabel la2 = new JLabel("File path :");public JLabel la3 = new JLabel("File Size (Bits) :");public JLabel la4 = new JLabel("CLIENT ");public JLabel c1 = new JLabel();

public JLabel c2 = new JLabel("");public JTextArea t2 = new JTextArea("");public JScrollPane sc = new JScrollPane();public JScrollPane sc1 = new JScrollPane();public JTextArea t1 = new JTextArea("");public Font l = new Font("Times New roman", Font.BOLD, 18);public Font l2 = new Font("Times New roman", Font.BOLD, 13);public Font l1 = new Font("Times New roman", Font.BOLD, 30);public Font l3 = new Font("Times New roman", Font.BOLD, 16);public Font l4 = new Font("tahoma", Font.BOLD, 27);public JFrame jf;public Container c;public JLabel ftr = new JLabel("FILE TRANSFERRING STATUS");public JLabel routerA = new JLabel();public JLabel routerB = new JLabel();public JLabel routerC = new JLabel();

public JLabel fileA = new JLabel();public JLabel fileB = new JLabel();public JLabel fileC = new JLabel();

public JLabel subrA = new JLabel();public JLabel subrB = new JLabel();public JLabel subrC = new JLabel();public JLabel serA = new JLabel();public JLabel serB = new JLabel();public JLabel serC = new JLabel();

public JButton test = new JButton();public JLabel test1 = new JLabel("Browse");public JLabel sen = new JLabel("Send");

public JLabel backgr = new JLabel("Browse");public JLabel splt = new JLabel("Split");public JLabel title = new JLabel("GREEN NETWORKING - EFFICIENT PACKET PROCESSING");

client(){ImageIcon v1=new ImageIcon(this.getClass().getResource("button.png"));b1.setIcon(v1);b2.setIcon(v1);b3.setIcon(v1);ImageIcon back=new ImageIcon(this.getClass().getResource("background-blue.jpg"));backgr.setBounds(0,-80,1035,900);backgr.setIcon(back);jf = new JFrame("Client");c = jf.getContentPane();c.setLayout(null);jf.setSize(1035,900);title.setForeground(Color.MAGENTA);title.setBounds(135,10,900,50);title.setFont(l4);b1.setBounds(380,153,120,35);b1.setForeground(Color.GREEN);b2.setBounds(380,266,120,35);splt.setBounds(420,267,120,35);splt.setForeground(Color.BLACK);splt.setFont(l);b3.setBounds(480,630,120,35);sen.setBounds(520,630,120,35);sen.setForeground(Color.BLACK);sen.setFont(l);la1.setBounds(100,148,150,50);la1.setForeground(Color.GREEN);la2.setBounds(100,205,150,50);la2.setForeground(Color.GREEN);la3.setBounds(100,263,150,50);la3.setForeground(Color.GREEN);la4.setBounds(440,60,150,50);la4.setForeground(Color.GREEN);c1.setBounds(280,216,400,35);c2.setBounds(280,266,100,35);c2.setFont(l2);c2.setForeground(Color.MAGENTA);sc1.setBounds(400,370,100,200);sc.setBounds(100,370,300,200);t1.setColumns(20);t1.setRows(10);t1.setFont(l3);t1.setForeground(Color.BLUE);sc.setViewportView(t1);t2.setBounds(100,300,300,200);t2.setColumns(20);t2.setRows(10);t2.setFont(l3);t2.setForeground(Color.BLUE);sc1.setViewportView(t2);ftr.setBounds(650,150,350,35);ftr.setForeground(Color.CYAN);ftr.setFont(l);fileA.setBounds(550,230,150,35);fileA.setForeground(Color.RED);fileA.setFont(l2);fileB.setBounds(550,300,150,35);fileB.setForeground(Color.RED);fileB.setFont(l2);fileC.setBounds(550,370,150,35);fileC.setForeground(Color.RED);fileC.setFont(l2);routerA.setBounds(600,230,150,35);routerA.setForeground(Color.MAGENTA);routerA.setFont(l2);routerB.setBounds(600,300,150,35);routerB.setForeground(Color.MAGENTA);routerB.setFont(l2);routerC.setBounds(600,370,150,35);routerC.setFont(l2);routerC.setForeground(Color.MAGENTA);subrA.setBounds(680,230,150,35);subrA.setForeground(Color.CYAN);subrA.setFont(l2);subrB.setBounds(680,300,150,35);subrB.setForeground(Color.CYAN);subrB.setFont(l2);subrC.setBounds(680,370,150,35);subrC.setForeground(Color.CYAN);subrC.setFont(l2);serA.setBounds(790,230,250,35);serA.setForeground(Color.GREEN);serA.setFont(l2);serB.setBounds(790,300,250,35);serB.setForeground(Color.GREEN);serB.setFont(l2);serC.setBounds(790,370,250,35);serC.setFont(l2);serC.setForeground(Color.GREEN);c.add(ftr);c.add(title);c.add(fileA);c.add(fileB);c.add(fileC);c.add(routerA);c.add(routerB);c.add(routerC);c.add(subrA);c.add(subrB);c.add(subrC);c.add(serA);c.add(serB);c.add(serC);c.add(la1);c.add(la2);c.add(la3);c.add(la4);c.add(c1);c.add(c2);c.add(sc1,BorderLayout.CENTER); c.add(sc,BorderLayout.CENTER);la1.setFont(l);la2.setFont(l);la3.setFont(l);la4.setFont(l1);test1.setBounds(408,154,100,35);test.setBounds(500,200,110,35);test1.setForeground(Color.BLACK);test1.setFont(l); test.setFocusPainted(true); c.add(splt); b1.setBorderPainted(true); b1.setContentAreaFilled(false); b2.setBorderPainted(true); b2.setContentAreaFilled(false); b3.setBorderPainted(true); b3.setContentAreaFilled(false); c.add(test1); c.add(sen); c.add(b1); c.add(b2); c.add(b3); b1.addMouseListener(new MouseAdapter(){public void mouseEntered(MouseEvent e){ImageIcon v2=new ImageIcon(this.getClass().getResource("button.png"));b1.setIcon(v2);}public void mouseExited(MouseEvent e){ImageIcon v=new ImageIcon(this.getClass().getResource("button3.png"));b1.setIcon(v);}public void mouseClicked(MouseEvent e){}});b2.addMouseListener(new MouseAdapter(){public void mouseEntered(MouseEvent e){ImageIcon v3=new ImageIcon(this.getClass().getResource("button.png"));b2.setIcon(v3);}public void mouseExited(MouseEvent e){ImageIcon v5=new ImageIcon(this.getClass().getResource("button3.png"));b2.setIcon(v5);}public void mouseClicked(MouseEvent e){}});b3.addMouseListener(new MouseAdapter(){public void mouseEntered(MouseEvent e){ImageIcon v2=new ImageIcon(this.getClass().getResource("button.png"));b3.setIcon(v2);}public void mouseExited(MouseEvent e){ImageIcon v=new ImageIcon(this.getClass().getResource("button3.png"));b3.setIcon(v);}public void mouseClicked(MouseEvent e){serC.setText(""); c1b.setText(totalbandwidthA+"(Kbs/s)"); String str=buffer.toString(); int total=str.length(); String substr=str.substring(0,5); String subtf=str.substring(5,total); c1.setText(""); c2.setText(""); t1.setText(""); tflda.setText(""); tfld1a.setText(""); tfld2a.setText(""); t2.setText(subtf.toString()); Random r2 = new Random(); int p1 =(Math.abs(r2.nextInt()) % 20)+10; System.out.println(" P1:"+p1); String p1text=Integer.toString(p1); //tfld1a.setText(p1text.toString()); tfldab.setText("127.0.0.1"); tfld1ab.setText(p1text); tfld2ab.setText("This Link have"+"\n"+"Destination Node"); String lenstr=str.substring(0,str.length()); int len=subtf.length(); String strlen=Integer.toString(len); c2b.setText(strlen); String first=t2.getText(); int lenf=first.length(); String sep=first.substring(0, first.length()); String rutname=p1text; rutname+="SUBROUTER A2"; String rnstr=rutname+str; System.out.println(" Router name and bandwidth ="+rnstr); byte[] byteArray; Socket client = null; try { client = new Socket("127.0.0.1", 6000); bos = new BufferedOutputStream(client.getOutputStream()); byteArray =rnstr.getBytes(); bos.write(byteArray, 0, byteArray.length); bos.flush(); bos.close(); client.close(); } catch (UnknownHostException e1) { e1.printStackTrace(); } catch (IOException e1) {} finally {} String subA2="SUBROUTER A2"; try { client = new Socket("127.0.0.1", 2233); bos = new BufferedOutputStream(client.getOutputStream()); byteArray =subA2.getBytes(); bos.write(byteArray, 0, byteArray.length); bos.flush(); bos.close(); client.close(); } catch (UnknownHostException e1) { e1.printStackTrace();} catch (IOException e1) {} Finally {} } } catch (IOException e) { } finally { } } } }public void actionPerformed(ActionEvent e){ if(e.getSource()==b) {}}}

6.2 Screen Shots6.2.1 Client:

Figure 6.2.1 Client

6.2.2 Router

Figure 6.2.2 Router

6.2.3 Sub Router

Figure 6.2.3 Sub Router

CHAPTER 7conclusion and future workIn this approach the resource management framework is proposed to reduce the power consumption and to minimize the total cost of a data center to support green computing. This framework is composed of power and workload management, and capacity planning schemes. The power and workload management is used by a batch scheduler of the data center to switch the server operation mode (i.e., active or sleep). With the power and workload management, the capacity planning is performed to obtain the optimal number of servers in the data center over multiple periods. To obtain the optimal decision in each period, the deterministic and stochastic optimization models have been proposed. The deterministic optimization model based on multi-stage assignment problem can be applied when the exact information about the job processing demand (i.e., job arrival rate) is known. On the other hand, the stochastic optimization model based on multi-stage stochastic programming is applied when only the probability distribution of the job processing demand is available. While the action of power and workload management is performed in the short term basis (e.g., fraction of minute), the decision of capacity planning (i.e., to expand the size of data center) is made in the long-term basis (i.e., few months). With the proposed joint short-term and long-term optimization models, the performance evaluation has shown that the total cost of the data center can be minimized while the performance requirements are met.For the future work, a stochastic optimization model for the resource management framework with the virtualization technology and the dynamic pricing of electricity in a smart grid will be developed. The computing resource management and capacity planning for a cloud provider owning multiple data centers will be considered.

CHAPTER 8references[1] R. Harmon, H. Demirkan, N. Auseklis, and M. Reinoso, From Green Computing to Sustainable IT: Developing a Sustainable Service Orientation, in Proceedings of the 43rd Hawaii International Conference on System Sciences (HICSS), 2010.[2] D. Niyato, S. Chaisiri, and B. S. Lee, Optimal power management for server farm to support green computing, in Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid), pp. 84-91, 2009.[3] M. L. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, Wiley-Interscience, April 1994.[4] J. R. Birge and F. Louveaux, Introduction to Stochastic Programming, Springer, February 2000.[5] Y. Chen, A. Das, W. Qin, A. Sivasubramaniam, Q. Wang, and N. Gautam, Managing server energy and operational costs in hosting centers, in Proceedings of ACM SIGMETRICS Performance Evaluation Review, vol.33, no. 1, pp. 303-314, June 2005.[6] R. Nathuji and K. Schwan, VirtualPower: coordinated power management in virtualized enterprise systems, in Proceedings of ACM SIGOPS Symposium on Operating Systems Principles (SOSP), pp. 265-278, 2007.[7] A. Verma, P. Ahuja, and A. Neogi, Power-aware dynamic placement of HPC applications, in Proceedings of the 22nd Annual International Conference on Supercomputing (ICS), pp. 175-184, 2008.