An energy efficient anti-correlated virtual machine placement algorithm using resource usage predictions

Energy related costs and environmental sustainability present a significant challenge for cloud computing practitioners and the development of next generation data centers. Virtual Machine (VM) placement provides a promising technique to save energy and improve resource management which is one of the greatest causes of high energy consumption in the operation of data centers today. 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 our novel Predictive Anti-Correlated Placement Algorithm (PACPA) which considers both CPU and bandwidth resource consumption. Our empirical results demonstrate how the proposed approach reduces energy by 18% while also reducing service violations by over 34% compared to some of the most commonly used placement algorithms.