UC Riverside

Energy proportionality of data center severs have improved drastically over the past decade to the point where near ideal energy proportional servers are now common. These highly energy proportional servers exhibit the unique property where peak efficiency no longer coincides with peak utilization. In this paper, we explore the implications of this property on data center scheduling. We identified that current state of the art data center schedulers does not efficiently leverage these properties, leading to inefficient scheduling decisions. We propose Peak Efficiency Aware Scheduling (PEAS) which can achieve better-than-ideal energy proportionality at the data center level. We demonstrate that PEAS can reduce average power by 25.5% with 3.0% improvement to TCO compared to state-of-the-art scheduling policies.