Energy-efficient adaptive virtual machine migrationrnmechanism for private Clouds

摘要

Private Cloud provides Cloud services with its relatively limited resources compared to publicrnClouds. Resources in private Clouds should be used energy efficiently. The resource utilizationrnis determined by the assignment of virtual machines (VMs) to hosts. Because of the frequentrnchanges in the resource requests on VMs, the systemmight become imbalanced, some hosts arernoverloaded/underloaded. Virtual machinemigration is a solution to ease the imbalance problem.rnVirtual machine migration includes the selection of a VM for migration and decision upon wherernit should be taken to (mapped). In this work, VM selection and VM mapping are integrated andrnaimed to ease the imbalance problem for energy efficiency. Moreover, selection and mappingrnhave become adaptive to ease imbalance, while optimizing energy consumption and adaptivelyrnresponding to changes in the system. Our proposed adaptive mechanism applies Bayesian inferencernto estimate the likelihood of a VMmigration decision, both VM selection for migration andrnVMmapping, optimizing energy consumption.Theproposedmechanismis evaluatedonCloudSimrnusing PlanetLab workload on a heterogeneous Cloud. It is demonstrated to reduce energy consumptionrnsignificantly, (onaverage)by116%,while its total execution time is also, (onaverage)5.39rntimes, shorter than the competing state-of-the-art policies.