A Predictive Anti-Correlated Virtual Machine Placement Algorithm for Green Cloud Computing

Abstract

Energy related costs and environmental sustainability present a significant challenge for cloud computing practitioners and the development of next generation data centers. In efficient resource management is one of the greatest causes of high energy consumption in the operation of data centers today. Virtual Machine (VM) placement is a promising technique to save energy and improve resource management. A key challenge for VM placement algorithms is the ability to accurately forecast future resource demands due to the dynamic nature of cloud applications. Furthermore, the literature rarely considers placement strategies based on co-located resource consumption which has the potential to improve allocation decisions. Using real workload traces this work presents a comparative study of the most widely used prediction models and introduces a novel predictive anti-correlated VM placement approach. Our empirical results demonstrate how the proposed approach reduces energy by 18% while also reducing service violations by over 47% compared to some of the most commonly used placement policies.