MILP for the Multi-objective VM Reassignment Problem

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Title: MILP for the Multi-objective VM Reassignment Problem
Authors: Saber, TakfarinasVentresque, AnthonyMarques-Silva, JoaoThorburn, JamesMurphy, Liam, B.E.
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Date: 11-Nov-2015
Online since: 2015-11-12T13:03:21Z
Abstract: Machine Reassignment is a challenging problem for constraint programming (CP) and mixed integer linear pro- gramming (MILP) approaches, especially given the size of data centres. The multi-objective version of the Machine Reassignment Problem is even more challenging and it seems unlikely for CP or MILP to obtain good results in this context. As a result, the first approaches to address this problem have been based on other optimisation methods, including metaheuristics. In this paper we study under which conditions a mixed integer optimisation solver, such as IBM ILOG CPLEX, can be used for the Multi-objective Machine Reassignment Problem. We show that it is useful only for small or medium scale data centres and with some relaxations, such as an optimality tolerance gap and a limited number of directions explored in the search space. Building on this study, we also investigate a hybrid approach, feeding a metaheuristic with the results of CPLEX, and we show that the gains are important in terms of quality of the set of Pareto solutions (+126.9% against the metaheuristic alone and +17.8% against CPLEX alone) and number of solutions (8.9 times more than CPLEX), while the processing time increases only by 6% in comparison to CPLEX for execution times larger than 100 seconds.
Funding Details: Science Foundation Ireland
Funding Details: Lero
Type of material: Conference Publication
Publisher: IEEE
Start page: 41
End page: 48
Keywords: Hybrid- MetaheuristicsMulti-objective optimisationVM/Machine reassignmentMixed integer linear programmingHybrid- metaheuristics
DOI: 10.1109/ICTAI.2015.20
Language: en
Status of Item: Peer reviewed
Is part of: Proceedings of the 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI)
Conference Details: 27th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Vietri Sul Mare, Italy, 9-11 November, 2015
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