Multiprocessor Scheduling for High-Variability Service Time Distributions

Many disciplines have been proposed for scheduling and processor allocation in multiprogrammed multiprocessors for parallel processing. These have been, for the most part, designed and evaluated for workloads having relatively low variability in service demand. But with reports that variability in service demands at high performance computing centers can actually be quite high, these disciplines must be reevaluated. In this paper, we examine the performance of two well-known static scheduling disciplines, and propose preemptive versions of these that offer much better mean response times when the variability in service demand is high. We argue that, in systems in which dynamic repartitioning in applications is expensive or impossible, these preemptive disciplines are well suited for handling high variability in service demand.

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