Virtual machine placement based on multi-objective reinforcement learning

Abstract

Multi-objective virtual machine (VM) placement is a powerful tool, which can achieve different goals in data centers. It is an NP-hard problem, and various works have been proposed to solve it. However, almost all of them ignore the selection of weights. The selection of weights is difficult, but it is essential for multi-objective optimization. The inappropriate weights will cause the obtained solution set deviating from the Pareto optimal set. Fortunately, we find that this problem can be easily solved by using the Chebyshev scalarization function in multi-objective reinforcement learning (RL). In this paper, we propose a VM placement algorithm based on multi-objective RL (VMPMORL). VMPMORL is designed based on the Chebyshev scalarization function. We aim to find a Pareto approximate set to minimize energy consumption and resource wastage simultaneously. Compared with other multi-objective RL algorithms in the field of VM placement, VMPMORL not only uses the concept of the Pareto set but also solves the weight selection problem. Finally, VMPMORL is compared with some state-of-the-art algorithms in recent years. The results show that VMPMORL can achieve better performance than the approaches above.