An Efficient Dynamic & Decentralized Load Balancing Technique for Grid - Rathore, Neeraj Kumar
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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 ecient resource management in Grid, the resource overloading must be prevented which can be obtained by proper Load Balancing and Job Migration…mehr

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Produktbeschreibung
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 ecient 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 runtime 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.
Autorenporträt
El Dr. Neeraj es profesor asociado en el Departamento de Ciencias de la Computación en la Universidad Central IGNTU, Amarkantak, M.P., India. Tiene más de 13 años de experiencia en la enseñanza y el software. Ha realizado un doctorado (2014), un máster (2008) y una licenciatura (2006) en CSE. Sus áreas de interés incluyen Cloud, BigData, IoT, DBMS y DSA. Tiene más de 15 publicaciones en revistas SCI de renombre.