Stochastic Queuing Simulation for Data Center Workloads

Data center systems and workloads are increasing in importance, yet there are few methods for evaluating potential changes to these systems. We introduce 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. We detail our methodology, workload suite, and practical concerns associated with them. To demonstrate our technique, we carry out a casestudy of data center power capping for 1000 servers. Finally, we discuss open research challenges for making SQS more robust.