Bayesian analysis of resource allocation policies in data centers in terms of virtual machine migrations
C. Crăciun, Ioan Salomie · 2017
One technique of solving energy consumption and performance issues in virtualized data centers consists in migrating the virtual machines between the physical hosts, in order to achieve either resource consolidation or load balancing. These migrations, however, may degrade the performance of the virtualized applications and of the servers and network involved in the migration process. A possible solution to reduce the number of virtual machine migrations is to use efficient resource allocation policies, which avoid resource over or under-usage. In this context, we compare two recently proposed Gaussian-type policies with greedy consolidation methods, in terms of virtual machine migrations. The outcomes of repeated simulation experiments are analyzed using Bayesian statistics. We assess different hierarchical Bayesian models to describe the virtual machine migration number, which enables us to ponder the average behavior of the resource allocation policies.