TY - GEN
T1 - GeNePi
T2 - 9th International Workshop on Hybrid Metaheuristics, HM 2014
AU - Saber, Takfarinas
AU - Ventresque, Anthony
AU - Gandibleux, Xavier
AU - Murphy, Liam
PY - 2014
Y1 - 2014
N2 - Data centres are facilities with large amount of machines (i.e., servers) and hosted processes (e.g., virtual machines). Managers of data centres (e.g., operators, capital allocators, CRM) constantly try to optimise them, reassigning 'better' machines to processes. These managers usually see better/good placements as a combination of distinct objectives, hence why in this paper we define the data centre optimisation problem as a multi-objective machine reassignment problem. While classical solutions to address this either do not find many solutions (e.g., GRASP), do not cover well the search space (e.g., PLS), or even cannot operate properly (e.g., NSGA-II lacks a good initial population), we propose GeNePi, a novel hybrid algorithm. We show that GeNePi outperforms all the other algorithms in terms of quantity of solutions (nearly 6 times more solutions on average than the second best algorithm) and quality (hypervolume of the Pareto frontier is 106% better on average).
AB - Data centres are facilities with large amount of machines (i.e., servers) and hosted processes (e.g., virtual machines). Managers of data centres (e.g., operators, capital allocators, CRM) constantly try to optimise them, reassigning 'better' machines to processes. These managers usually see better/good placements as a combination of distinct objectives, hence why in this paper we define the data centre optimisation problem as a multi-objective machine reassignment problem. While classical solutions to address this either do not find many solutions (e.g., GRASP), do not cover well the search space (e.g., PLS), or even cannot operate properly (e.g., NSGA-II lacks a good initial population), we propose GeNePi, a novel hybrid algorithm. We show that GeNePi outperforms all the other algorithms in terms of quantity of solutions (nearly 6 times more solutions on average than the second best algorithm) and quality (hypervolume of the Pareto frontier is 106% better on average).
KW - Data Centres
KW - Evolutionary Algorithms
KW - Machine Reassignment
KW - Multi-Objective Optimisation
UR - https://www.scopus.com/pages/publications/84903584245
U2 - 10.1007/978-3-319-07644-7_9
DO - 10.1007/978-3-319-07644-7_9
M3 - Conference Publication
AN - SCOPUS:84903584245
SN - 9783319076430
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 115
EP - 129
BT - Hybrid Metaheuristics - 9th International Workshop, HM 2014, Proceedings
PB - Springer-Verlag
Y2 - 11 June 2014 through 13 June 2014
ER -