Adaptive virtual machine migration mechanism for energy efficiency

Cloud systems have become a popular platform for business applications due to the flexibility in usage and payment they offer. One of the caveats of Cloud systems is their high energy consumption. Minimizing energy consumption while maintaining a high service level has become a relevant optimization task for Cloud providers. Opportunities for energy savings arise when server hosts are overloaded, which also entails unnecessary delays.

To address the problem, researchers have devised strategies how to choose the server host to deploy an application to and how to choose a running application for migration when a host has been identified as overloaded. In this work, we introduce a Bayesian Belief Network which learns over time which of the virtual machines are best removed from a host that has been identified as overloaded. The probabilistic choice is made among virtual machines that are grouped by their degree of CPU usage. Given the feedback in the form of the computing resources saved, the system learns which virtual machine profiles should be shifted for best performance. This strategy compares favourably to two existing methods for load balancing.