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Parallel job scheduling on cluster computers involves the usage of several strategies to maximize both the utilization of the hardware as well as the throughput at which jobs are processed. Another consideration is the response times, or how quickly a job finishes after submission. One possible solution toward achieving these goals is the use of preemption. Preemptive scheduling techniques involve an overhead cost typically associated with swapping jobs in and out of memory. As memory and data sets increase in size, overhead costs increase. Here is presented a technique for reducing the overhead incurred by swapping jobs in and out of memory as a result of preemption. This is done in the context of the Scojo-PECT preemptive scheduler. Additionally a design for expanding the existing Cluster Simulator to support analysis of scheduling overhead in preemptive scheduling techniques is presented. A reduction in the overhead incurred through preemptive scheduling by the application of standard fitting algorithms in a multi-state job allocation heuristic is shown.
Esbaugh, Bryan, "Coarse-grain time sharing with advantageous overhead minimization for parallel job scheduling" (2010). Electronic Theses and Dissertations. 7964.