A comparative study of multi-objective machine reassignment algorithms for data centres
|Title:||A comparative study of multi-objective machine reassignment algorithms for data centres||Authors:||Saber, Takfarinas; Gandibleux, Xavier; O'Neill, Michael; Murphy, Liam, B.E.; Ventresque, Anthony||Permanent link:||http://hdl.handle.net/10197/11192||Date:||20-Sep-2019||Online since:||2019-11-08T16:01:08Z||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).||Funding Details:||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:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Computer Science Research Collection|
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