Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/227198
Title: An efficient dynamic and decentralized load balancing technique for grid
Researcher: Rathore, Neeraj Kumar
Guide(s): Chana, Inderveer
Keywords: Grid Computing
Job Migration
Load Balancing
University: Thapar Institute of Engineering and Technology
Completed Date: 2014
Abstract: Grid computing has recently become one of the most important research topics in the field of computing. The Grid paradigm has gained popularity due to its capability to offer easier access to geographically distributed resources operating across multiple administrative domains. The grid environment is considered as a combination of dynamic, heterogeneous and shared resources in order to provide faster and reliable access to the Grid resources. For efficient resource management in Grid, the resource overloading must be prevented which can be obtained by proper Load Balancing and Job Migration mechanisms. In this scenario, dynamic and decentralized Load Balancing considers all the factors pertaining to the characteristics of the Grid computing environment. Dynamic load-balancing algorithms attempt to use the run-time state information to make more informative decisions in sharing the system load and in decentralization, algorithm is executed by all nodes in the system and the responsibility of Load Balancing is shared among all the nodes in the same pool. For this purpose, in this research work, an extensive survey of the existing Load Balancing and Job Migration techniques has been done. A detailed classification and gap analysis of the existing techniques is presented based on different parameters. A Job Migration and Job Migration approach has been proposed and designed to fulfill all the existing gaps. The issue of Load Balancing in a Grid has been addressed while maintaining the resource utilization and response time for dynamic and decentralized Grid environment. Here, a hierarchical Load Balancing technique has been analyzed based on variable threshold value. The load is divided into different categories, like, lightly loaded, under-lightly loaded, overloaded, and normally loaded. A threshold value, which can be found out using load deviation, is responsible for transferring the task and flow of workload information.
Pagination: xiii, 149p.
URI: http://hdl.handle.net/10603/227198
Appears in Departments:Department of Computer Science and Engineering

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