Reducing data center power with server consolidation: Approximation and evaluation

Chandrasekar Subramanian, Arunchandar Vasan, Anand Sivasubramaniam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations

Abstract

With the growing costs of powering data centers, power management is gaining importance. Server consolidation in data centers, enabled by virtualization technologies, is becoming a popular option for organizations to reduce costs and improve manageability. While consolidation offers these benefits, it is important to ensure proper resource provisioning so that performance is not compromised. In addition to reducing the number of servers, there are other knobs - such as frequency/voltage scaling - that are being offered by recent hardware for finer granularity of power control. In this paper, we look at exploiting server consolidation and frequency/voltage control to reduce power consumption, while meeting certain provisioning guarantees. We formulate the problem as a variant of variable-sized bin packing. We show that the problem is NP-hard, and present an approximation algorithm for the same. The algorithm takes O(n2 log n) time for n workloads, and has a provable approximation ratio. Experimental evaluation shows that in practice our algorithm obtains solutions very close (< 6.5% difference) to optimal.

Original languageEnglish (US)
Title of host publication17th International Conference on High Performance Computing, HiPC 2010
PublisherIEEE Computer Society
ISBN (Print)9781424485185
DOIs
StatePublished - 2010
Event17th International Conference on High Performance Computing, HiPC 2010 - Goa, India
Duration: Dec 19 2010Dec 22 2010

Publication series

Name17th International Conference on High Performance Computing, HiPC 2010

Other

Other17th International Conference on High Performance Computing, HiPC 2010
Country/TerritoryIndia
CityGoa
Period12/19/1012/22/10

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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