Modeling the Energy Efficiency of Heterogeneous Clusters

Abstract

Traditional datacenter systems advocate the use of high-performance hardware, resulting in increased power consumption and cooling costs. With increasing availability of systems having diverse performance-to-power ratios, we analyze the energy efficiency of mixing high-performance and low-power nodes in a cluster. Using a model-driven analysis, we predict the heterogeneous mix of nodes that is the most energy-efficient while maintaining a given deadline. Considering service demands of the workloads on cores, memory and I/O devices, we derive Pareto-optimal configurations by matching the execution rate of different nodes. Our mix and match approach determines heterogeneous configurations that exhibit a "sweet region", where energy usage reduces linearly as the deadline is relaxed. Our analysis shows that mixing high-performance and low-power nodes is more energy-efficient than homogeneous datacenter clusters.