Dynamic Virtual Machine Consolidation: A Multi Agent Learning Approach

Abstract

Distributed dynamic virtual machine (VM) consolidation (DDVMC) is a virtual machine management strategy that uses a distributed rather than a centralized algorithm for finding a right balance between saving energy and attaining best possible performance in cloud data center. One of the significant challenges in DDVMC is that the optimality of this strategy is highly dependent on the quality of the decision-making process. In this paper we propose a cooperative multi agent learning approach to tackle this challenge. The experimental results show that our approach yields far better results w.r.t. The energy-performance tradeoff in cloud data centers in comparison to state-of-the-art algorithms.