Concurrency and Computation Practice and Experience · 2013 · 13 citations · 21 references
ComputingComputer ScienceComputer SystemsCluster ComputingDistributed SystemsEnergy ManagementCompute Resource ManagementCpu SchedulingHigh-throughput ComputingPerformance OptimizationPower ManagementWorkload SchedulingEnergy ProfilingEnergy-aware OptimizationPower ConsumptionPower Consumption ModelingPower Consumption OptimizationPower Management TechniquesWorkloadWorkload Profiling
SUMMARY Reduction of power consumption for any computer system is now an important issue, although this should be carried out in a manner that is not detrimental to the users of that computer system. We present a number of policies that can be applied to multi‐use clusters where computers are shared between interactive users and high‐throughput computing. We evaluate policies by trace‐driven simulations to determine the effects on power consumed by the high‐throughput workload and impact on high‐throughput users. We further evaluate these policies for higher workloads by synthetically generating workloads based around the profiled workload observed through our system. We demonstrate that these policies could save 55% of the currently used energy for our high‐throughput jobs over our current cluster policies without affecting the high‐throughput users’ experience. Copyright © 2013 John Wiley & Sons, Ltd.
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