Grid resource discovery a survey and comparative analysis 2


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Grid resource discovery a survey and comparative analysis 2

  1. 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME 550 GRID RESOURCE DISCOVERY: A SURVEY AND COMPARATIVE ANALYSIS Arjun Singh Computer Science & Engineering Research Scholar at SGVU Jaipur, Assistant Professor at SPSU Udaipur Dr. Prasun Chakrabarti Computer Science & Engineering Associate Professor, SPSU Udaipur Surbhi Chauhan Computer Science & Engineering Research Scholar ABSTRACT Grid computing is evolving as a sustainable choice for high-performance computing, as the sharing of nodes or resources provides enhanced performance at a low cost compare to individual machines computation dedicated for some task. Resource discovery is very important phase of grid computing deployment. It is very complex and difficult to discover a node because of the geographical resource dispersion and dynamic nature of the grid topology. Discovery process is very critical to manage and allocate the resources. This paper provides a depth insight of the different resource discovery approaches in grid computing. This survey is based on ongoing research on node or resource discovery from 1997 to 2013. At the end paper also present a comparative analysis of various approaches. I. INTRODUCTION A. Computational Grid Many people terms the ‘Grid’ offers a potential means of surmounting these obstacles to progress [1]. The computational grid is a new class of infrastructure which built on the Internet and the World Wide Web and provides high performance, secure and scalable mechanisms for discovering and negotiating access to remote resources, the Grid assurances INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 3, May-June (2013), pp. 550-559 © IAEME: Journal Impact Factor (2013): 6.1302 (Calculated by GISI) IJCET © I A E M E
  2. 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME 551 to make it promising for scientific collaborations to share resources on an unparalleled scale, and for purely distributed groups to work together in ways that were previously impossible. In 1965, Fernando Corbato and other developers of the Multics Operating System had anticipated a computer structure, which is same as of power grid and a water grid. J. C. R. Licklider and Robert W. Taylor in 1968 thought a Grid-like infrastructure in their research paper The Computer as a Communications Device [2]. In 1998, Ian Foster explained details purpose, shape, and architecture of a computational grid. In Ian Foster discussed six main important factors such as why grid computing is essential, for what kind of work it is required, what type of applications it is needed, How the Grid infrastructure will be used, who will the stake holders in grid, how they will be benefited, how to build the grid and what kind of difficulties will be confronted to design a grid. Ian Foster proposed various aspects to design and maintain a heterogeneous large distributed computer network called grid. In Computational environment it is essential to describe the resources. Resources can be computers, online instruments, data, information, storage, routing devices and resource discovery mechanism which returns desired resources after matching based on certain parameters. The main objective of this paper is to get in-depth information of various approaches used in resource discovery. B. Need of Resource Discovery Resource discovery is a challenging task in large heterogeneous and distributed network compare to the traditional computer network [3]. There are several factors such as large number of users with heterogeneous hardware and software platform, large pool of resources and heterogeneity in the user’s request, makes resource discovery difficult. There are more complicated factor which contribute in the difficult node discovery. For example, some organization leaving and joining the grid network and resources (dynamic nature), different administrative domains, availability of resource and CPU load changing. So, an appropriate and effective resource discovery mechanism is very crucial aspect of Grid computing. Success of a grid computer infrastructure lies in finding desired resource for a specific task. C. Components of Resource Discovery Iamnitchi, I. Foster, and Daniel C. Nurmi identified four components of general resource discovery [4]- a. Membership Protocol: Membership Protocol defines that how new nodes join the network and how do they learn about each other. b. Overlay Construction function: It selects the group of active collaborators from local membership list. c. Preprocessing: It refers to the offline preparations for better search performance, independent of request. d. Request processing: Request processing can have local and remote processing components. Apart from this author also identified few environmental parameter factors, which control the performance and design strategies for a resource discovery solution. These factors are a. Resource information distribution and density: It states that different load of information sharing on different nodes.
  3. 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME 552 b. Resource information dynamism: It indicates the different attributes of resources, which are dynamic and fixed in nature. c. Request popularity distribution: It denotes to possible closer uniform distribution of popular requests. d. Peer participation: The peer membership depends on different type of networks. D. Grid Resource Managemtn Model and Algorithms Rajkumar Buyya discussed three different models for grid resource management architecture inspired by three different philosophies such as hierarchical model, abstract owner model and (computational) market model [5]. The hierarchical model captures the approach followed in many contemporary grid systems. Authors shows the potential of an order and delivery approach in job submission and result gathering. The (computational) market model captures the essentials of both hierarchical and abstract owner models and uses the concept of computational economy. Klaus Krauter ,Ra jkumar Buyya, and Muthucumaru Maheswaran acknowledged some important resource management approaches to project a comprehensive resource management system [6]. The authors explained the resource management system as the core component of a network computing system. The authors discussed several dimensions of resource management such as quality of service (QoS) issue, different scheduling approaches, different heterogeneity issues, different resource distribution approaches and resource discovery technique. The authors propose that a resource management system can uphold a replicated network directory, which contain resource information and then resource discovery function queries the “resource dissemination function” for a specific resource. There are many algorithms for resource discovery algorithm used in node discovery. In 1999, Harchol and Balter [7], discussed the flooding algorithm, used in internet routers to find the routes and routers. In this algorithm, a machine is configured to have a fixed set of neighboring machines, and only direct communication is allowed with machines in this set. In terms of the graph, a node only communicates over the edges that were initially in the graph; new edges that are added to the graph are not used for communication. Those edges that constitute the initial neighbors are not necessarily the links in the underlying physical network, but somewhat they are virtual links, every possibly corresponding to a path in the underlying network. The number of rounds of this algorithm is equivalent to the diameter of the graph. Author claimed that this algorithm can be very slow if it is not started with a small diameter graph. Harchol and Blater [7] also discussed the swamping algorithm. This algorithm is same as of flooding algorithm except that t that machines may now open connections with al l their current neighbors, not just their initial neighbors. Also since the neighbor sets change, al l of the current neighbor set is transferred, not just the updates. The advantage of the Swamping algorithm is that the graph always converges to a complete graph in O (log n) steps, irrespective of the initial configuration. In 2012, Mehajabeen Fatima and Roomap Gupta present a new approach for node discovery called Route Discovery by Cross Layer Approach for MANET [8]. In new generation communication, mobile or other network is expected that Mobile adhoc Network (MANET) can be used cautiously anywhere any time. But pervasive computing MANET face lots of methodical challenges. One of them is traditional architecture. The Traditional architecture is hierarchical model of TCPIP which is used for networking. Traditional hierarchical layered design of network protocols is uncompromising to handle the dynamics
  4. 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME 553 of Mobile adhoc Network. Cross-layer design breakdowns from traditional network design. In traditional network design, each layer of the protocol stack operates independently and information is exchanged only between adjacent layers of the protocol stack. Because of the direct dependencies between the physical layer and the upper layers, the traditional protocol stack is not sufficient for mobile adhoc network. The cautious exploitation of protocol interactions that cross the normal layer boundaries can improve the performance of the communication and hence better application-layer performance. The dependent design between the layers blur the boundary between two adjacent layers. Link breakage is of high probability in mobile wireless adhoc networks because of highly dynamic nature of MANET. This happens because of limited battery power, mobility and limited transmission range. It becomes difficult to maintain continuous links in the networks. In AODV, node sends a RERR message for the specified destination when it noticed that a link rupture has taken place. It hampers the stability of the link and results in link rupture, in loss of few packets and escalation in delay. Link breakage, packet loss and delay is not acceptable in real time communication and loss of packets, excessive overhead is not acceptable in non-real time communication. So AODV is improved to manage with these problems. An algorithm with cross layer approach is suggested which helps in maintaining the continuity of the network resulting in less delay, more throughput, less overhead and less battery power consumption which can be used for both real and non-real time communication. Shay Kutten and David Peleg proposed an algorithm for resource discovery [9]. With reference to to the randomized algorithm of Harchol Balter, time complexity is scale down to O (log2 n) to O (log n). This algorithm takes O(n log n) time compare to O (n log2 n) for message complexity. Congfeng Jiang , Xianghua Xu, and Jian Wan proposed replication based job scheduling in Grids with Security Assurance [10] . Security assurance is a perilous prerequisite for QoS or Service level agreement satisfactions in dynamic grid environments. Jobs may be scheduled to numerous machines through dissimilar distributed administrative domains. Instead of traditional methods using fixed-number job replications, author proposed a security-aware parallel and independent job scheduling algorithm based on adaptive job replications. It make sure the job scheduling decision, reliable, secure and fault tolerant. In dynamic and error- prone grids, the replication number is changed as per the current security conditions and the end-user settings. Tung-Shih Su, Chih-Hung Lin and Wen-ShyongHsieh proposed a QoS-aware routing protocol [11] that includes an admission control system into route discovery and route setup. When a node need to find optimal route to a destination, it uses the information that collected from a card with another signal for QUART-DD. For the route discovery and route setup an admission control system is used. Before selecting a path system waits for some time to measure the best possible path. After selecting best path it sends out the packets. II. RESOURCE DISCOVERY APPROACHES There are various approaches specified and proposed by various scientist and organization for resource discovery. Below section discuss these approaches in detail. Resource Discovery is methodical process of defining which grid resource is the best resource to complete a task with following parameters: • It should take shortest amount of time to complete the job.
  5. 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME 554 • It should make optimal and efficient use of resources • Cost of computation should be minimum. Main issue of grid deployment is resource discovery. Dynamic and heterogeneous nature of nodes and network make it complicated task. Many approaches are proposed in various research paper for resource discovery in grid environment. All proposed approaches are based on query and software agent technology. In Query based system, to discover the node a request (query) is forwarded to neighbor node for resource availability. Most of the grid systems use this approach. Query based system are characterized based on whether the query is executed for a centralized database or a distributed database. Resource Discovery can be considered in two forms: Query Based Resource Discovery and Agent Based Resource Discovery. Agent based approach send software code across machines in the Grid that are inferred locally on each machine. Agents are software components with autonomy, intelligence and response. Agents interact with neighbor nodes and on behalf of its users they can accomplished the task. The basic difference between a query based approach and an agent based approach is that agent based systems allow the software code (agent) to control the query procedure and based on its intelligence makes resource discovery decisions. Agent does not depend on fixed function query engine. Agent based resource discovery is essentially distributed. In this subsequent subsections we have discussed resource discovery approaches like decentralized, agent based approach, routing transferring model-based approach, ontology description based system, parameter-based approach, quality of service based approach and request forwarding approach. A. Decentralized Approach Iamnitchi and Rana proposed decentralized approach in 2001[15]. This approach described decentralized resource discovery and management architecture based on software agents. These agents can be a service or an application. This approach provide dynamic registration of nodes and task. According to this research paper, basically this approach is a match making approach, which helps in dynamic resource discovery and their management in grid environment. Proposed system use XML documents for resource capability and resource availability. B. Agent Based Approach Kyungo Jun and L. Boloni proposed agent based resource discovery [16]. This paper present a distributed discovery method called Distributed Awareness Algorithm. Distributed Awareness is a learning system, in which a nodes gets aware about it neighbor in the network. Each node maintain the awareness table and exchange this table with neighbor node. This table have several information such as, node location (IP address), information when the last time awareness table was sent to other nodes and when the last time heard from other nodes. Author also claimed that by using agent‘s independent behavior this agent based resource discovery provides better discovery compare to other system. C. Routing Transffering Model Based Approach Wei Li, Zhiwei Xu, Fangpeng Dong, Jun Zhang proposed a Routing-Transferring resource discovery model [17]. This model have three basic components: resource requester, the resource router and the resource provider. The provider sends its resource information to a router, which store this information in routers’ tables. When requester demand a resource
  6. 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME 555 from router, router checks it table and find appropriate resource. Once router finds the resource, it forward it to the requester. Author formalized this model and analyze the complexity of the SD-RT (Shortest Distance Routing-Transferring) algorithm. Author claimed that the resource discovery time depends on topology and distribution of resources. When topology and distribution are definite, the SD- RT algorithm can find a resource in the shortest time. Experiments shows that when topology is definite, the performance is determined by resource distribution, which includes two important factors: resource frequency and resource location. D. Ontology Discription Based Approach Ontology refers to a description of a resource service, Ludwig proposed a semantic service discovery framework in a grid environment [18]. Author proposed a service matchmaking mechanism based on ontology knowledge and they claimed that this matchmaking framework can provide a better service discovery and also can provide close matches. The main idea behind this approach is the advertisement of the resource. In this approach, service provider registers its service description into the service registry database. When a Grid application sends a request to service directory, matchmaker returns the matches to the service requester. Requester chooses the best resource based on the specific need. E. Parameter Based Approach M. Maheswaran and K. Krauter inspected different approaches for resource discovery in a grid environment. Author proposed a new model, Grid potential, which has the processing capabilities of resources in a large network. The authors also discussed an algorithm called Data Dissemination Algorithm. Algorithm follows swamping approach for message distribution. A message gets validated when it comes to the node. The validation procedure depends on three types of distribution, universal awareness which permits all incoming messages, neighborhood awareness that allows messages from a certain distance, and distinctive awareness, and it drop the messages if it finds out that the less Grid potentiality at the local node in remote node, is less than that of the requestor node. The authors also measured the performance of neighborhood awareness, universal awareness, and distinctive awareness dissemination schemes. The authors claimed that universal approach is more expensive in terms of message complexity than that of neighborhood and distinctive approach. The authors also claimed that this new class of dissemination could reduce the communication overhead during the resource discovery. F. Quality Of Service Based Approach Huang proposed an algorithm to discover the occasionally available resources in a multimedia environment [20]. For a given graph theoretical approach authors define different policies for a QoS based resource discovery service. A generalized version of Discovering Intermittently Available Resources (DIAR) algorithm based on occasionally available resources is presented in the paper. The Author evaluated the performance of QoS policies based on different time-map strategies in a centralized system. There are various QoS parameters include storage capacity, processor runtime, network bandwidth. On these basis, parameters QoS guarantees the best behavior of grid. By experimental study author found that randomized placement strategies and increased server storage can provide better performance for resource discover.
  7. 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME 556 G. Request Forwarding Approach Iamnitchi identified following four-request forwarding approaches [15]. i. Random Walk Approach: In this Approach, A node is chosen randomly to forward the request. ii. Learning-Based Approach: In this approach a request is send to a node who answered same kind of request before. In case, if there is no similar answer is found, the request is forwarded to a randomly selected node. iii. Best-Neighbor Approach: The number of received answer is recorded without recording the type of requests. n. This Approach is identical to learning-based Approach except when no similar answer is found, request is forwarded to the best neighbor. The resource discovery mechanism in an emulated grid, which is a large grid network (for this case up to 5000 peers) based on the assumption that every peer provides at least one resource is analyzed. The measured performance evaluation of a simple resource discovery technique is based on request propagation. iv. Learning-Based + Best-Neighbor Approach This Approach is identical to learning- based Approach except when no similar answer is found. Request is forwarded to the best neighbor. H. Peer-to-Peer Approach Iamnitchi proposed a Peer-to-Peer resource discovery architecture for a grid environment [15]. This resource discovery architecture can reduce administrative overhead and it can also provide effective search-performance result. Author point out various resource discovery problems in a very large distributed resource-sharing environment [4]. This paper identify four different architectural components namely Membership protocol, Overlay construction, Preprocessing, and Request processing. Paper also described four environment parameter factors, which govern the performance and design strategies are Resource information distribution and density, Resource information dynamism, Request popularity distribution and Peer participation. Author gives brief description of different resource discovery Approaches in Peer-to-Peer networking is described [4]. The authors claimed that using four axes framework; it is possible to design any resource discovery architecture in a grid. A general purpose query support enabled “Unified Peer-to-Peer Database Framework (UPDF)” for a large distributed system has been proposed [21]. UPDF can be recognized as a Peer-to-Peer database framework for a general purpose query support. It is unified because it supports arbitrary random node topologies, query languages, different query response modes, different data types, different neighbor selection policies for expressing specific applications. III.COMPARISON Based on different parameters such as reliability scalability, adaptability, and manageability, a comparison between the Resource Discovery Approaches are done in table 1. To find an optimal and best resource on the grid, various parameters required to consider to deal with complexity of resource discovery. Resource discovery becomes complex with the increasing size of grid. Peer-to-Peer is the best approach to be used in the large global grids. It uses the graph theoretic approach to realize scalability and manageability. Routing Transferring based resource discovery approach can also be used for large grids. It is reliable and scalable but create more overheads on the network. Ontology based approach matchmaking algorithm, which limited its scalability.
  8. 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME 557 Table 2.1 Comparison of different Resource Discovery Approaches Approach Ontology Description Routing Transferring Parameter Quality of Service Request Forwarding Peer-to- Peer Type of approach Agent Query Agent Query Agent/Query Agent /Query Scalability Scalability limited due to centralized broker Scalable, use routing protocol Scalable due to grid potential concept used Scalable, Use time Map strategies in centralized system Due to random selection of nodes, it is scalable More scalable as it uses the four axes framework Reliability Failures are detected as soon as they occurs so more reliable Quite reliable as it uses the routing concept Reliable as we can add or delete a node from anywhere Considers parameters like network bandwidth, required CPU, storage capacity, that make it less reliable Random walk Approach make it reliable in case the resources are equally distributed Based on graph theory so reliability increases Adaptability Can be made adaptable by providing manager information about different platforms Routing table is used to make records of different platforms. Adaptable due to universal, network and distractive awareness parameters Depends upon the Service Level Agreement (SLA) sign with user for providing adaptability Using Best neighbor Approach adaptability is easy Multiple platforms environment make it more adaptive Manageability Quite easy to manage as a lot of its working is dependent on single node. Management is easy due to SDRT algorithms as it deals with different topologies Manage the consistency by using the data dissemination algorithms Uses algorithm like DIAR for the resource discovery Better Management can be achieved by combining its two sub approaches. Complex architecture hence difficult to manage. Complexity O(log log n) Θ(n)1/2 (n) (log2n) Θ(n) O(log n) Development Agent OWL, RDF C Java, C Any Description Language C DHT Algorithm Used Matchmaking, Gang matchmaking SDRT, Routing Dissemination DIAR Request Forwarding Algorithm Swamping
  9. 9. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME 558 REFERENCES 1. Foster, I. and Kesselman, C. (eds) (1999) The Grid: Blueprint for a New Computing Infrastructure. San Francisco, CA: Morgan Kaufmann Publishers. 2. In Memoriam: J.C.R. Licklider (1915-1990) 3. Adriana Iamnitchi, 2002, Resource Discovery in Large-Scale Distributed Environments “Thesis Proposal”. 4. Iamnitchi, I. Foster, Daniel C. Nurmi, “A Peer-to-Peer Approach to Resource Discovery in Grid Environments,” Proc. of the 11th Symposium on High Performance Distributed Computing, Edinburgh, UK, 2002 5. Rajkumar Buyya, Steve J.C, David C.C 2002, “Architectural Models for Resource Management in the Grid” ACM International Workshop on Grid computing pages 18- 35. 6. Klaus Krauter ,Ra jkumar Buyya, and Muthucumaru Maheswaran, 2000, “A Taxonomy and Survey of Grid Resource Management Systems for Distributed Computing”, Software Practice and experience. 7. Mor Harchol-Balter, Tom Leighton, and Daniel Lewin. “Resource discovery in distributed networks”, 18th Annual ACMSIGACT/SIGOPS Symposium on Principles of Distributed Computing, May 1999. 8. Mehajabeen Fatima, Roopam Gupta, 2012, “Route Discovery by cross Layer Approach for MANET”, IJCA, vol 37 no-7, pages-14-24. 9. Shay Kutten, David Peleg, “Deterministic Distributed Resource Discovery” Nineteenth Annual ACM SIGACT/SIGOPS Symposium on Principles of Distributed Computing. 10. Congfeng Jiang , Xianghua Xu, and Jian Wan, “Replication based job scheduling in grids with security assurance”, Third International Symposium on Electronic Commerce and Security Workshops (ISECS ’10) Guangzhou, P. R. China, 29-31, July 2010, pp. 156-159. 11. Tung-Shih Su, Chih-Hung Lin and Wen-ShyongHsieh, “A Novel QoS-aware Routing for Adhoc Network Protocol” paper.php? id=117. 12. Karl Czajkowski , Steven Fitzgerald Ian Foster and Carl Kesselman, “Grid Information Services for Distributed Resource Sharing” 10th IEEE International Symposium on HighPerformance Distributed Computing(HPDC-10), IEEE Press, 2001. 13. K.Czajkowski, I. Foster, N. Karonis, C. Kesselm, S. Martin, W.Smith, and S.Tuecke,“A resource management architecture for metacomputing systems,” The 4th Workshop on Job Scheduling Strategies for Parallel Processing, pp: 62–82.Springer-Verlag LNCS 1459, 1998. 14. J. Verbeke, N. Nadgir, G. Ruetsch,I. Sharapov, “Framework for Peerto-Peer Distributed Computing in a Heterogeneous, Decentralized Environment,”Proc of Third International Workshop on Grid Computing – GRID 2002, Baltimore, MD, USA, , LNCS 2536, Springer-Verlag, 2002. 15. A. Iamnitchi and I. Foster, “On Fully Decentralized Resource Discovery in Grid Environments,” IEEE International Workshop on Grid Computing, Denver, CO, 2001. 16. K. Jun, L. Bolon, K. Palacz, D. Marinescu, “Agent-based resource discovery,” Proceeding of IEEE Heterogeneous Computing Workshop, 2000,pp: 43 -52, 2000. 17. W. Li, Z. Xu, F. Dong, J. Zhang, “Grid Resource Discovery Based on a Routing- Transferring Model,” Proc. of Third International Workshop on Grid Computing: GRID 2002,Baltimore, MD. , pp: 145-156, Springer, 2002.
  10. 10. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME 559 18. S. Ludwig, P. Santen, “A Grid Service Discovery Matchmaker based on Ontology Description,” Euroweb 2002 — The Web and the GRID: from e-science to e-business, 2002. 19. M. Maheswaran and K. Krauter, “A Parameter-based approach to resource discovery in Grid computing systems,” 1st IEEE/ACM International Workshop on Grid Computing (Grid 2000), 2000. 20. Yun Huang and Nalini Venkatasubramanian, “QoS-based Resource Discovery in Intermittently Available Environments”, Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing HPDC-11 2002 (HPDC’02) 1082-8907/02, 2002 IEEE. 21. W. Hoschek, “A Unified Peer-to-Peer Database Framework for Scalable Service and Resource Discovery,” Proc. of Third International Workshop on Grid Computing: GRID 2002, Baltimore, MD. , pp: 126-144, Springer, 2002. 22. I. Foster, C. Kesselman, “Globus: A metacomputing infrastructure toolkit,” International Journal of Supercomputer Applications, 11(2), pp: 115-128, 1997. 23. Vimala.S and Sasikala.T, “A Location-Based Least-Cost Scheduling For Data-Intensive Applications in Grid Environment”, International journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 2, 2013, pp. 526 - 534, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 24. Dr. Damanjeet Kaur, “Smart Grids and India”, International Journal of Electrical Engineering & Technology (IJEET), Volume 1, Issue 1, 2010, pp. 157 - 164, ISSN Print: 0976-6545, ISSN Online: 0976-6553. 25. Yogita A. Dalvi, “A Method for Balancing Heterogeneous Request Load in DHT-Based P2P Systems”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 2, 2013, pp. 309 - 314, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.