• DocumentCode
    74889
  • Title

    Dynamic Right-Sizing for Power-Proportional Data Centers

  • Author

    Minghong Lin ; Wierman, Adam ; Andrew, Lachlan L. H. ; Thereska, Eno

  • Author_Institution
    California Inst. of Technol., Pasadena, CA, USA
  • Volume
    21
  • Issue
    5
  • fYear
    2013
  • fDate
    Oct. 2013
  • Firstpage
    1378
  • Lastpage
    1391
  • Abstract
    Power consumption imposes a significant cost for data centers implementing cloud services, yet much of that power is used to maintain excess service capacity during periods of low load. This paper investigates how much can be saved by dynamically “right-sizing” the data center by turning off servers during such periods and how to achieve that saving via an online algorithm. We propose a very general model and prove that the optimal offline algorithm for dynamic right-sizing has a simple structure when viewed in reverse time, and this structure is exploited to develop a new “lazy” online algorithm, which is proven to be 3-competitive. We validate the algorithm using traces from two real data-center workloads and show that significant cost savings are possible. Additionally, we contrast this new algorithm with the more traditional approach of receding horizon control.
  • Keywords
    cloud computing; computer centres; power consumption; probability; cloud services; dynamic right-sizing; excess service capacity; online algorithm; optimal offline algorithm; power-proportional data centers; receding horizon control; Delay; Heuristic algorithms; Load modeling; Optimization; Prediction algorithms; Servers; Switches; Capacity provisioning; data centers; energy efficiency; online algorithms;
  • fLanguage
    English
  • Journal_Title
    Networking, IEEE/ACM Transactions on
  • Publisher
    ieee
  • ISSN
    1063-6692
  • Type

    jour

  • DOI
    10.1109/TNET.2012.2226216
  • Filename
    6361254