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
Link To Document :
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