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Recently, there has been an explosive growth in Internet services, greatly increasing the importance of data center systems. Applications served from “the cloud” are driving data center growth and quickly overtaking traditional workstations. Although there are a many tools for evaluating components of desktop and server architectures in detail, scalable modeling tools are noticeably missing. We describe BigHouse a simulation infrastructure for data center systems. Instead of simulating servers using detailed microarchitectural models, BigHouse raises the level of abstraction. Using a combination of queuing theory and stochastic modeling, BigHouse can simulate server systems in minutes rather than hours. BigHouse leverages statistical simulation techniques to limit simulation turnaround time to the minimum runtime needed for a desired accuracy. In this paper, we introduce BigHouse, describe its design, and present case studies for how it has already been applied to build and validate models of data center workloads and systems. Furthermore, we describe statistical techniques incorporated into BigHouse to accelerate and parallelize its simulations, and demonstrate its scalability to model large cluster systems while maintaining reasonable simulation time.
Meisner et al. (Sun,) studied this question.