TY - GEN
T1 - MILP for the multi-objective VM reassignment problem
AU - Saber, Takfarinas
AU - Ventresque, Anthony
AU - Marques-Silva, Joao
AU - Thorburn, James
AU - Murphy, Liam
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/4
Y1 - 2016/1/4
N2 - Machine Reassignment is a challenging problem for constraint programming (CP) and mixed integer linear programming (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.
AB - Machine Reassignment is a challenging problem for constraint programming (CP) and mixed integer linear programming (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.
KW - Hybrid-Metaheuristics
KW - Mixed Integer Linear Programming
KW - Multi-objective Optimisation
KW - VM/Machine Reassignment
UR - https://www.scopus.com/pages/publications/84963563063
U2 - 10.1109/ICTAI.2015.20
DO - 10.1109/ICTAI.2015.20
M3 - Conference Publication
AN - SCOPUS:84963563063
T3 - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
SP - 41
EP - 48
BT - Proceedings - 2015 IEEE 27th International Conference on Tools with Artificial Intelligence, ICTAI 2015
PB - IEEE Computer Society
T2 - 27th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2015
Y2 - 9 November 2015 through 11 November 2015
ER -