MILP for the Multi-objective VM Reassignment Problem

Abstract

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.

VM reassignment in hybrid clouds for large decentralised companies: A multi-objective challenge

“…The authors proposed a three-step method to address the problem, using a modified version of the ROADEF challenge [43]. In [44] and [41] the authors proposed a linear formulation of the problem and studied the relevance of using MILP and Constraint-Based LNS (CBLNS) solvers. They showed that MILP solvers (e.g., IBM ILOG CPLEX) can only solve small instances, whereas CBLNS achieves worse results than MILP solvers on small instances, but scales well to larger ones.…”

“…However, most solutions to this problem in the literature use a weak definition of multiobjective [35], i.e., a linear aggregation of objectives [1,29,23]. Related work also often miss the complexity of large companies, where the infrastructure is not monolithic but distributed over decentralised hosting departments with their own preferences [43,44]. The optimisation of the companies' IT infrastructure is then more challenging as decisions have to take into account the preferences of the capital allocators of each hosting departments and the interests of the company as a whole.…”

A Hybrid Algorithm for Multi-Objective Test Case Selection

“…The initial population is generated by the first step of our hybrid algorithm, which gives a good bootstrap to the evolutionary algorithm. Note that we use all individuals from the first step as seeds [24], unlike in [26], [27] where only few good individuals are added to the initial population.…”

CloudLightning: a Self-Organized Self-Managed Heterogeneous Cloud

“…Fuzzy pattern recognition [44], heuristic algorithms [45], [46] and evolutionary algorithms [47] are commonly used in multiobjective optimization for high-level (i.e., coarse-grained) resource allocation, which attempt to find an appropriate solution between system performance (e.g., response time) and useroriented constraints (e.g., SLAs). Similarly, liner programming or dynamic programming can be used to solve VM placement problems [48]- [50] for low-level (fine-grained) resource placement, focusing on optimizing resource utilization and power consumption [51].…”