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Energy-aware workload management models for operation cost reduction in data centers

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  • Bodenstein, Christian
  • Schryen, Guido
  • Neumann, Dirk

Abstract

In the last century, the costs of powering datacenters have increased so quickly, that datacenter power bills now dwarf the IT hardware bills. Many large infrastructure programs have been developed in the past few years to reduce the energy consumption of datacenters, especially with respect to cooling requirements. Although these methods are effective in lowering the operation costs they do require large upfront investments. It is therefore not surprising that some datacenters have been unable to utilize the above means and as a result are still struggling with high energy bills. In this work we present a cheap addition to or an alternative to such investments as we propose the use of intelligent, energy efficient, system allocation mechanisms in place of current packaged system schedulers available in modern hardware infrastructure cutting server power costs by 40%. We pursue both the quest for (1) understanding the energy costs generated in operation as well has how to utilize this information to (2) allocate computing tasks efficiently in a cost minimizing optimization approach. We were able to underline the energy savings potential of our models compared to current state-of-the-art schedulers. However, since this allocation problem is complex (NP-hard) we investigated various model approximations in a trade-off between computational complexity and allocative efficiency. As a part of this investigation, we evaluate how changes in system configurations impact the goodness of our results in a full factorial parametric evaluation.

Suggested Citation

  • Bodenstein, Christian & Schryen, Guido & Neumann, Dirk, 2012. "Energy-aware workload management models for operation cost reduction in data centers," European Journal of Operational Research, Elsevier, vol. 222(1), pages 157-167.
  • Handle: RePEc:eee:ejores:v:222:y:2012:i:1:p:157-167
    DOI: 10.1016/j.ejor.2012.04.005
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    1. Böttcher, Jan & Drexl, A. & Kolisch, R. & Salewski, F., 1999. "Project scheduling under partially renewable resource constraints," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 345, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Jan Böttcher & Andreas Drexl & Rainer Kolisch & Frank Salewski, 1999. "Project Scheduling Under Partially Renewable Resource Constraints," Management Science, INFORMS, vol. 45(4), pages 543-559, April.
    3. Alberto Caprara & Matteo Fischetti & Paolo Toth, 2002. "Modeling and Solving the Train Timetabling Problem," Operations Research, INFORMS, vol. 50(5), pages 851-861, October.
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    Cited by:

    1. Lang, Fabian & Fink, Andreas & Brandt, Tobias, 2016. "Design of automated negotiation mechanisms for decentralized heterogeneous machine scheduling," European Journal of Operational Research, Elsevier, vol. 248(1), pages 192-203.
    2. Christoph Goebel & Hans-Arno Jacobsen & Victor Razo & Christoph Doblander & Jose Rivera & Jens Ilg & Christoph Flath & Hartmut Schmeck & Christof Weinhardt & Daniel Pathmaperuma & Hans-Jürgen Appelrat, 2014. "Energy Informatics," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(1), pages 25-31, February.

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