Towards a scalable data center-level evaluation methodology

As the popularity of Internet services continues to rise, the need to understand the design of the data center systems hosting these workloads becomes increasingly important. Unfortunately, research in this area has been stifled, primarily due to a lack of tools, workloads, and rigorous evaluation methodology. Traditional tools, such as architectural simulators, do not directly address data center-level issues and do not scale to simulate the thousands of machines needed for data center research. We introduce, Stochastic Queuing Simulation (SQS), our methodology for characterization and evaluation of data center systems. By leveraging techniques from stochastic modeling, queuing theory and statistical sampling, SQS uses discrete-event simulation to drive models that scale to tens of thousands of machines. Whereas detailed architectural simulations can last hours or days, SQS turnaround time is typically on the order of tens of minutes to an hour. Furthermore, computation can be distributed across cores and machines, achieving speedup using commodity clusters.