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

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Author(s)
Saber, Takfarinas 
Gandibleux, Xavier 
O'Neill, Michael 
Murphy, Liam, B.E. 
Ventresque, Anthony 
Uri
http://hdl.handle.net/10197/11192
Date Issued
20 September 2019
Date Available
08T16:01:08Z November 2019
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 ( -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).
Sponsorship
Science Foundation Ireland
Type of Material
Journal Article
Publisher
Springer
Journal
Journal of Heuristics
Volume
26
Start Page
119
End Page
150
Copyright (Published Version)
2019 Springer
Keywords
  • Machine reassignment

  • Metaheuristics

  • Multi-objective

DOI
10.1007/s10732-019-09427-8
Language
English
Status of Item
Peer reviewed
ISSN
1381-1231
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Computer Science Research Collection
Scopus© citations
5
Acquisition Date
Jan 28, 2023
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