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A Stochastic Bin Packing Approach for Server Consolidation with Conflicts

In: Operations Research Proceedings 2019

Author

Listed:
  • John Martinovic

    (Technische Universität Dresden)

  • Markus Hähnel

    (Technische Universität Dresden)

  • Waltenegus Dargie

    (Technische Universität Dresden)

  • Guntram Scheithauer

    (Technische Universität Dresden)

Abstract

The energy consumption of large-scale data centers or server clusters is expected to grow significantly in the next couple of years contributing to up to 13% of the worldwide energy demand in 2030. As the involved processing units require a disproportional amount of energy when they are idle, underutilized or overloaded, balancing the supply of and the demand for computing resources is a key issue to obtain energy-efficient server consolidations. Whereas traditional concepts mostly consider deterministic predictions of the future workloads or only aim at finding approximate solutions, here we propose an exact bin packing based approach to tackle the problem of assigning jobs with (not necessarily independent) stochastic characteristics to a minimal amount of servers subject to further practical constraints. Finally, this new approach is tested against real-world instances obtained from a Google data center.

Suggested Citation

  • John Martinovic & Markus Hähnel & Waltenegus Dargie & Guntram Scheithauer, 2020. "A Stochastic Bin Packing Approach for Server Consolidation with Conflicts," Operations Research Proceedings, in: Janis S. Neufeld & Udo Buscher & Rainer Lasch & Dominik Möst & Jörn Schönberger (ed.), Operations Research Proceedings 2019, pages 159-165, Springer.
    • Handle: RePEc:spr:oprchp:978-3-030-48439-2_19
    DOI: 10.1007/978-3-030-48439-2_19
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    Cited by:

    1. John Martinovic & Markus Hähnel & Guntram Scheithauer & Waltenegus Dargie, 2022. "An introduction to stochastic bin packing-based server consolidation with conflicts," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 296-331, July.

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