MILP for the Multi-objective VM Reassignment Problem
This addresses optimization challenges for data center resource management, but it is incremental as it builds on existing methods with a hybrid approach.
The paper tackles the multi-objective VM reassignment problem in data centers by evaluating conditions under which MILP solvers like CPLEX are effective, showing they work only for small-to-medium scales with relaxations, and proposes a hybrid approach that improves Pareto solution quality by 126.9% over a metaheuristic alone and increases solution count by 8.9 times compared to CPLEX alone.
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.