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A comparative study of multi-objective machine reassignment algorithms for data centres

Author

Listed:
  • Takfarinas Saber

    (University College Dublin)

  • Xavier Gandibleux

    (Université de Nantes)

  • Michael O’Neill

    (University College Dublin)

  • Liam Murphy

    (University College Dublin)

  • Anthony Ventresque

    (University College Dublin)

Abstract

At a high level, data centres are large IT facilities hosting physical machines (servers) that often run a large number of virtual machines (VMs)—but at a lower level, data centres are an intricate collection of interconnected and virtualised computers, connected services, complex service-level agreements. While data centre managers know that reassigning VMs to the servers that would best serve them and also minimise some cost for the company can potentially save a lot of money—the search space is large and constrained, and the decision complicated as they involve different dimensions. This paper consists of a comparative study of heuristics and exact algorithms for the multi-objective machine reassignment problem. Given the common intuition that the problem is too complicated for exact resolutions, all previous works have focused on various (meta)heuristics such as First-Fit, GRASP, NSGA-II or PLS. In this paper, we show that the state-of-art solution to the single objective formulation of the problem (CBLNS) and the classical multi-objective solutions fail to bridge the gap between the number, quality and variety of solutions. Hybrid metaheuristics, on the other hand, have proven to be more effective and efficient to address the problem—but as there has never been any study of an exact resolution, it was difficult to qualify their results. In this paper, we present the most relevant techniques used to address the problem, and we compare them to an exact resolution ($$\epsilon $$ϵ-Constraints). We show that the problem is indeed large and constrained (we ran our algorithm for 30 days on a powerful node of a supercomputer and did not get the final solution for most instances of our problem) but that a metaheuristic (GeNePi) obtains acceptable results: more (+ 188%) solutions than the exact resolution and a little more than half (52%) the hypervolume (measure of quality of the solution set).

Suggested Citation

  • Takfarinas Saber & Xavier Gandibleux & Michael O’Neill & Liam Murphy & Anthony Ventresque, 2020. "A comparative study of multi-objective machine reassignment algorithms for data centres," Journal of Heuristics, Springer, vol. 26(1), pages 119-150, February.
  • Handle: RePEc:spr:joheur:v:26:y:2020:i:1:d:10.1007_s10732-019-09427-8
    DOI: 10.1007/s10732-019-09427-8
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    References listed on IDEAS

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    1. Franck Butelle & Laurent Alfandari & Camille Coti & Lucian Finta & Lucas Létocart & Gérard Plateau & Frédéric Roupin & Antoine Rozenknop & Roberto Wolfler Calvo, 2016. "Fast machine reassignment," Annals of Operations Research, Springer, vol. 242(1), pages 133-160, July.
    2. Christos Voudouris & Edward P.K. Tsang & Abdullah Alsheddy, 2010. "Guided Local Search," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 321-361, Springer.
    3. Felix Brandt & Jochen Speck & Markus Völker, 2016. "Constraint-based large neighborhood search for machine reassignment," Annals of Operations Research, Springer, vol. 242(1), pages 63-91, July.
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    Cited by:

    1. Takfarinas Saber & Dominik Naeher & Philippe Lombaerde, 2023. "On the Optimal Size and Composition of Customs Unions: An Evolutionary Approach," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1457-1479, December.

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