A Power Consumption Model for Cloud Servers Based on Elman Neural Network

Abstract

Leveraging power consumption models in software systems can achieve easy deployment of low-cost, high-availability power monitoring in cloud datacenters that are usually large-scale, heterogeneous and frequently scaling up. However, traditional regression-based power consumption models generally have two drawbacks. First, their mathematical forms are usually fixed and determined a priori. This may cause unacceptable increase of error or over-fitting as the power signatures of cloud servers are usually uncertain. Second, the characteristic of workload dispatched to cloud servers is constantly changing while regression-based models can hardly generalize to a wide range of servers and workload types. As a novel solution, we in this paper propose a server power consumption model based on Elman Neural Network (PCM-ENN), aiming to allow accurate and flexible power estimation. PCM-ENN is an end-to-end black box model capable of learning the temporal relation between samples in a time series of power consumption. We trained and evaluated PCM-ENN on two power sequence datasets collected from heterogeneous hardware and operating systems running quasi-production benchmarks like CloudSuite. Experimental result shows that PCM-ENN generated accurate estimates on server power consumption with only small errors, outperforming widely-used linear regression model and NARX model in terms of accuracy.