NGA Based Load Balancing in Computational Grid
In the present grid computing environment, the scheduling approaches for resources only focus on the current state of the entire system. Most often they fail to consider the system variation and historical behavioral data which causes system load imbalance. To present a better approach for solving the problem of resource scheduling in a Grid computing environment, this paper demonstrates a genetic algorithm based resource scheduling strategy that focuses on system load balancing. The genetic algorithm approach computes the impact in advance that it will have on the system after the new resource is deployed in the system, by utilizing historical data and current state of the system.
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