The computational Grid provides a promising platform for the efficient execution of parameter sweep applications over very large parameter spaces. Scheduling such applications is challenging because target resources are heterogeneous, because their load and availability varies dynamically, and because tasks may share common data files. In this paper, we propose a scheduling algorithm for parameter sweep applications on the Grid. We consider standard heuristics for task/host assignment (Max-min, Min-min, Sufferage), and we propose an extension of Sufferage called XSufferage. Using simulation, we demonstrate 3 results: 1) that XSufferage can take advantage of file sharing to achieve better performance than the other heuristics under a wide variety of load conditions, 2) that it is possible to characterize the environments under which different heuristics perform best, and 3) that it is possible to characterize the performance of different heuristics under the (realistic) assumption of varying accuracy of performance estimations.
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