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Data center systems and workloads are increasing in importance, yet there are few methods for evaluating potential changes to these systems. This paper introduces a new methodology for exascale evaluation, called Statistical Queuing Simulation (SQS). At its heart, SQS is a parallel, large-scale stochastic discrete time simulation of generalized queueing models that are driven by empirically-observed arrival and service distributions. SQS provides numerous practical advantages over alternative large-scale simulation techniques (e.g., Trace-driven simulation), including statistical rigor and reduced turnaround time.
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