An Effective Load Balancing Grouping Based Job & Resource Scheduling Algorithms for Fine Grained Jobs in Distributed Environment?

Grid computing is a form of distributed computing that provides a platform for executing large-scale resource intensive applications on a number of heterogeneous computing systems across multiple administrative domains. Therefore, Grid platforms enable sharing, exchange, discovery, selection, and aggregation of distributed heterogeneous resources such as computers, databases and visualization devices. Job and resource scheduling is one of the key research area in grid computing. In a grid computing environment, a scheduler is responsible for selecting the best suitable computing resources in the grid for processing jobs to achieve high system throughput. Further, grouping the fine grained jobs according to the processing capability of available resources results in better throughput, resource utilization and low communication time. This paper focuses on lightweight jobs scheduling in Grid Computing. Therefore, this paper proposes ―An Effective Load Balancing Grouping Based Job & Resource Scheduling Algorithms for Fine Grained Jobs in Distributed Environment‖ with the objective of minimizing overhead time and computation time, thus reducing overall processing time of jobs. The work is verified through various observations made in simulated grid environments. The results obtained shows that the proposed grouping-based scheduling algorithm is on average or comparable to other grouping based scheduling algorithms.