Stochastic modeling of dynamic right-sizing for energy-efficiency in cloud data centers

D. Shen, J. Luo, F. Dong, Xiang Fei, W. Wang, Guoqing Jin, Weidong Li

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

Large data centers are usually built to support increasing computational and data storage demand of growing global business and industry, which consume an enormous amount of energy, at a huge cost to both business and the environment. However, much of that energy is wasted to maintain excess service capacity during periods of low load. In this paper, we investigate the problem of “right-sizing“ data center for energy-efficiency through virtualization which allows consolidation of workloads into smaller number of servers while dynamically powering off the idle ones. In view of the dynamic nature of data centers, we propose a stochastic model based on Queueing theory to capture the main characteristics. Solving this model, we notice that there exists a tradeoff between the energy consumption and performance. We hereby develop a BFGS based algorithm to optimize the tradeoff by searching for the optimal system parameter values for the data center operators to “right-size“ the data centers. We implement our Stochastic Right-sizing Model (SRM) and deploy it in the real-world cloud data center. Experiments with two real-world workload traces show that SRM can significantly reduce the energy consumption while maintaining high performance.
Original languageEnglish
Pages (from-to)82-95
JournalFuture Generation Computer Systems
Volume48
Early online date5 Oct 2014
DOIs
Publication statusPublished - Jul 2015

Bibliographical note

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Funder

This work is supported by National Key Basic Research Program of China under Grant No. 2010CB328104, National Natural Science Foundation of China under Grant Nos. 61320106007, 61370207, 61202449, and 61300024, National High-tech R&D Program of China (863 Program) under Grant No. 2013AA013503, China National Key Technology R&D Program under Grant Nos. 2010BAI88B03 and 2011BAK21B02, China Specialized Research Fund for the Doctoral Program of Higher Education under Grant No. 20110092130002, Jiangsu Research Prospective Joint Research Project under Grant Nos. BY2012202 and BY2013073-01, Jiangsu Provincial Key Laboratory of Network and Information Security under Grant No. BM2003201, Key Laboratory of Computer Network and Information Integration of Ministry of Education of China under Grant No. 93 K-9. This research was carried out as a part of the CASES project which is supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme under the grant agreement No. 294931.

Keywords

  • cloud computing
  • data center
  • energy-efficiency
  • queueing theory
  • virtualization

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