Energy-Aware Migration of Virtual Machines Driven by Predictive Data Mining Models

Abstract

Consolidation of virtual machines (VM) is one of the key strategies used to reduce the power consumption of Cloud servers. For this reason it is extensively studied. Nevertheless, the effectiveness of a consolidation strategy strongly depends on the forecast of the VM resource needs. This paper describes the design and development of a system for energy-aware allocation of virtual machines, driven by predictive data mining models. In particular, migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. Experimental results, performed on data of a real Cloud data centre, show encouraging benefits in terms of energy saving.