Joint optimization of idle and cooling power in data centers while maintaining response time

Author:

Ahmad Faraz1,Vijaykumar T. N.1

Affiliation:

1. Purdue University, West Lafayette, IN, USA

Abstract

Server power and cooling power amount to a significant fraction of modern data centers' recurring costs. While data centers provision enough servers to guarantee response times under the maximum loading, data centers operate under much less loading most of the times (e.g., 30-70% of the maximum loading). Previous server-power proposals exploit this under-utilization to reduce the server idle power by keeping active only as many servers as necessary and putting the rest into low-power standby modes. However, these proposals incur higher cooling power due to hot spots created by concentrating the data center loading on fewer active servers, or degrade response times due to standby-to-active transition delays, or both. Other proposals optimize the cooling power but incur considerable idle power. To address the first issue of power, we propose PowerTrade , which trades-off idle power and cooling power for each other, thereby reducing the total power. To address the second issue of response time, we propose SurgeGuard to overprovision the number of active servers beyond that needed by the current loading so as to absorb future increases in the loading. SurgeGuard is a two-tier scheme which uses well-known over-provisioning at coarse time granularities (e.g., one hour) to absorb the common, smooth increases in the loading, and a novel fine-grain replenishment of the over-provisioned reserves at fine time granularities (e.g., five minutes) to handle the uncommon, abrupt loading surges. Using real-world traces, we show that combining PowerTrade and SurgeGuard reduces total power by 30% compared to previous low-power schemes while maintaining response times within 1.7%.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design,Software

Reference34 articles.

1. AirPAK. Computational fluid dynamics (CFD) software by Ansys Inc. http://www.ansys.com/products/airpak/default.asp. AirPAK. Computational fluid dynamics (CFD) software by Ansys Inc. http://www.ansys.com/products/airpak/default.asp.

2. O. Allen. Probability statistics and queuing theory with computer science applications. 1990. O. Allen. Probability statistics and queuing theory with computer science applications. 1990.

3. The Case for Energy-Proportional Computing

4. C. Belady. Green grid data center power efficiency metrics PUE and DCIE. White paper: Metrics & Measurements. http://www.thegreengrid.org 2007. C. Belady. Green grid data center power efficiency metrics PUE and DCIE. White paper: Metrics & Measurements. http://www.thegreengrid.org 2007.

5. P. Bohrer E. Elnozahy T. Keller M. Kistler C. Lefurgy C. McDowell and R. Rajamony. The case for power management in web servers. Power aware computing 2002. P. Bohrer E. Elnozahy T. Keller M. Kistler C. Lefurgy C. McDowell and R. Rajamony. The case for power management in web servers. Power aware computing 2002.

Cited by 72 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3