A system for online power prediction in virtualized environments using gaussian mixture models

In this paper we present a system for online power prediction in vir-tualized environments. It is based on Gaussian mixture models that use architectural metrics of the physical and virtual machines (VM) collected dynamically by our system to predict both the physical machine and per VM level power consumption. A real implementation of our system shows that it can achieve average prediction error of less than 10%, outperforming state of the art regression based approaches at negligible runtime overhead.