Integrating Vm Selection Criteria In Distributed Dynamic Vm Consolidation Using Fuzzy Q-Learning

Distributed dynamic VM consolidation can be an effective strategy to improve energy efficiency in cloud environments. In general, this strategy can be decomposed into four decision-making tasks: (1) Host overloading detection, (2) VM selection, (3) Host underloading detection, and (4) VM placement. The goal is to consolidate virtual machines dynamically in a way that optimizes the energy-performance tradeoff online. In fact, this goal is achieved when each of the aforementioned decisions are made in an optimized fashion. In this paper we concentrate on the VM selection task and propose a Fuzzy Q-Learning (FQL) technique so as to make optimal decisions to select virtual machines for migration. We validate our approach with the CloudSim toolkit using real world PlanetLab workload. Experimental results show that using FQL yields far better results w.r.t. the energy-performance trade-off in cloud data centers in comparison to state of the art algorithms.