Predictive Models for Energy-Efficient Clouds: An Analysis on Real-Life and Synthetic Data

Abstract

The success of Cloud Computing and the resulting expansion of large data centers result in a huge rise of electrical power consumption by hardware facilities. Consolidation of virtual machines (VM) is one of the key strategies used to reduce the energy consumed by Cloud servers. Nevertheless, the effectiveness of a consolidation strategy strongly depends on the forecast of the needs of the VM resources. This paper describes the experimental evaluation 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 both on a real Cloud and synthetic data, show encouraging benefits in terms of energy saving.