A Workload-Aware, Eco-Friendly Daemon for Cluster Computing

This paper presents an eco-friendly daemon that reduces power consumption while better maintaining high performance via a novel behavioral quantification of workload. Specifically, our behavioral quantification achieves a more accurate workload characterization than previous approaches by inferring "processor stall cycles due to off-chip activities." This quantification, in turn, provides a foundation upon which we construct an interval-based, power-aware, run-time algorithm that is implemented within a system-wide daemon. We then evaluate our power-aware daemon in a cluster-computing environment with the NAS Parallel Benchmarks. The results indicate that our novel behavioral quantification of workload allows our power-aware daemon to more tightly control performance while delivering substantial energy savings.