IDEAS home Printed from https://ideas.repec.org/a/spr/joheur/v26y2020i1d10.1007_s10732-019-09427-8.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10732-019-09427-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10732-019-09427-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shen, Yunzhuang & Sun, Yuan & Li, Xiaodong & Eberhard, Andrew & Ernst, Andreas, 2023. "Adaptive solution prediction for combinatorial optimization," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1392-1408.
    2. Mori, Masakatsu & Kobayashi, Ryoji & Samejima, Masaki & Komoda, Norihisa, 2017. "Risk-cost optimization for procurement planning in multi-tier supply chain by Pareto Local Search with relaxed acceptance criterion," European Journal of Operational Research, Elsevier, vol. 261(1), pages 88-96.
    3. Lakmali Weerasena & Aniekan Ebiefung & Anthony Skjellum, 2022. "Design of a heuristic algorithm for the generalized multi-objective set covering problem," Computational Optimization and Applications, Springer, vol. 82(3), pages 717-751, July.
    4. Jaszkiewicz, Andrzej, 2018. "Many-Objective Pareto Local Search," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1001-1013.
    5. Simona Mancini, 2013. "Multi-echelon distribution systems in city logistics," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 54, pages 1-2.
    6. Carl H. Häll & Anders Peterson, 2013. "Improving paratransit scheduling using ruin and recreate methods," Transportation Planning and Technology, Taylor & Francis Journals, vol. 36(4), pages 377-393, June.
    7. Alokananda Dey & Siddhartha Bhattacharyya & Sandip Dey & Debanjan Konar & Jan Platos & Vaclav Snasel & Leo Mrsic & Pankaj Pal, 2023. "A Review of Quantum-Inspired Metaheuristic Algorithms for Automatic Clustering," Mathematics, MDPI, vol. 11(9), pages 1-44, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joheur:v:26:y:2020:i:1:d:10.1007_s10732-019-09427-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.