Abstract

Scheduling is an active research area in the Computational Grid environment. The objective of grid scheduling is both to deliver both the Quality of Service (QoS) requirement of the grid users, and to raise the utilization of the resources. A large number of researchers have presented heuristic algorithms to find a near-global optimum for the static scheduling model of the grid. Relatively a smaller number of researchers have worked on the scheduling problem for the dynamic scheduling model. This paper proposes a new resource characteristic based optimization method, which may be combined with Earlier Gap, Earliest Deadline First (EG-EDF) policy to schedule jobs in a dynamic environment. The proposed algorithm generates near-optimal solutions, which are better than those reported in the literature for a specific range of datasets. Extensive experimentation and analysis has proved the efficacy of our method.

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