An Energy Efficient and Interference Aware Virtual Machine Consolidation Algorithm Using Workload Classification

Abstract

Inefficient resource usage is one of the greatest causes of high energy consumption in cloud data centers. Virtual Machine (VM) consolidation is an effective method for improving energy related costs and environmental sustainability for modern data centers. While dynamic VM consolidation can improve energy efficiency, virtualisation technologies cannot guarantee performance isolation between co-located VMs resulting in interference issues. We address the problem by introducing a energy and interference aware VM consolidation algorithm. The proposed algorithm utilizes the predictive capabilities of a Machine Learning (ML) model in an attempt to classify VM workloads to make more informed consolidation decisions. Furthermore, using recent workload data from Microsoft Azure we present a comparative study of two popular classification algorithms and select the model with the best performance to incorporate into our proposed approach. Our empirical results demonstrate how our approach improves energy efficiency by 31% while also reducing service violations by 69%.