WASP: Workload Adaptive Energy-Latency Optimization in Server Farms Using Server Low-Power States

With the growing energy demands from server farms, it becomes necessary to understand the tradeoffs between energy consumption and application performance. Typically, server farms are provisioned for peak load even when they are mostly operating at low utilization levels. This results in wasteful energy consumption. At the same time, application workloads have Quality of Service (QoS) constraints that need to be satisfied. Optimizing server farm energy consumption with QoS constraints is a challenging task since the workload can have variabilities in job sizes, job arrival patterns and system utilization levels. In this paper, we present WASP, where we explore techniques that make smart use of the processor and system low-power states, and orchestrate their use with workload adaptivity for more effective energy management. We perform an extensive study of Energy-Latency tradeoffs with simulations, and evaluate WASP on a testbed with a cluster of servers. Our experiments on real systems show that WASP achieves up to 57% energy reduction over a naive policy that uses a shallow processor sleep state when there are no jobs to execute, and 39% over a delay timer based approach while maintaining the 90th percentile job service latency to be under 2x job execution time.