SUMMARYGrid computing emerges as an infrastructure for large-scale data processing, resource sharing, and scientific computing. In this paper we propose a Grid scheduling algorithm using multi-attribute utility theory and multi-objective optimization (MOO). The algorithm makes the optimal decisions based on the available set of objectives. By comparing with a deadline-and-budget algorithm with three objectives, we show that the proposed MOO scheduling algorithm is capable of obtaining a broader set of non-dominated solutions. The obtained solutions are also of higher quality, which are in close proximity to the Pareto optimal front.

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