A Power Monitoring System Based on a Multi-Component Power Model

View Sample PDF

Author(s): Weiwei Lin (South China University of Technology, Guangzhou, China), Haoyu Wang (South China University of Technology, Guangzhou, China)and Wentai Wu (South China University of Technology, Guangzhou, China)
Copyright: 2018
Volume: 10
Issue: 1
Pages: 15
Source title: International Journal of Grid and High Performance Computing (IJGHPC)
Editor(s)-in-Chief: Emmanuel Udoh (Sullivan University, USA)and Ching-Hsien Hsu (Asia University, Taiwan)
DOI: 10.4018/IJGHPC.2018010102

Keywords: Computer Science & IT / Grid & High Performance Computing / High Performance Computing / Information Science Reference

Purchase

View A Power Monitoring System Based on a Multi-Component Power Model on the publisher's website for pricing and purchasing information.

Abstract

As the increasing IT energy consumption emerged as a prominent issue, computer system energy consumption monitoring and optimization has gradually become a significant research forefront. However, most existing energy monitoring methods are limited to hardware-based measurement or coarse-grained energy consumption estimation. They cannot provide fine-grained energy consumption data (i.e., component energy consumption) and high-scalability for distributed cloud environments. In this article, the authors first study widely-used power models of CPUs, memory and hard disks. Then, following an investigation into disk power behaviors in sequential I/O and random I/O, they propose an improved I/O-mode aware disk power model with multiple variables and thresholds. They developed EnergyMeter, a monitoring software utility that can provide accurate power estimate by exploiting a multi-component power model. Experiments based on PCMark prove that the average error of EnergyMeter is merely 5% under a variety of workloads

The IRMA Community

Research IRM

A Power Monitoring System Based on a Multi-Component Power Model

Purchase

Abstract

Related Content

IRMA Sponsors