Experimental and quantitative analysis of server power model for cloud data centers

Abstract

Scientific computing applications like online social network analysis demand enormous computing capability from cloud service, but now the high energy consumption by cloud data centers has brought more concerns on power monitoring and management to cloud service providers (CSPs). Compared with hardware-based traditional techniques, server power monitoring based on power model is of higher scalability as well as lower deployment cost and thus, is more feasible for cloud data center power management. However, previous studies lack a systematic review and quantitative analysis on server power model. In this paper, we review and compare several popular power models of cloud server components including CPU, vCPU, memory and hard disk. We propose an I/O-mode aware disk power model based on our observation of disk power behavior. Experimentally, we first analyze the accuracy of different CPU power models by looking into a SPECpower_ssj2008 dataset. We also carried out experiments on a physical server to evaluate memory power models and disk power models. The experimental results indicate the advantage of polynomial CPU model, LLCM-based memory model and the proposed disk model. The ideology of component-level power modeling presented in this paper helps realize fine-grained power control. Moreover, the evaluation and comparison results provide CSPs with useful guidance on optimizing energy management of cloud data centers.