PCVM.ARIMA: predictive consolidation of virtual machines applying ARIMA method

Abstract

Cloud computing adopts virtualization technology, including migration and consolidation of virtual machines, to overcome resource utilization problems and minimize energy consumption. Most of the approaches have focused on minimizing the number of physical machines and rarely have devoted attention to minimizing the number of migrations. They also decide based on the current resources utilization without considering the demand for resources in the future. Some approaches minimize the number of active physical machines and Service Level Agreement (SLA) violations with the number of unnecessary migrations. They consider the current resource utilization of physical machines and neglect from demands for future resource requirements. As a result, as time passes, the number of unnecessary migrations, and subsequently, the rate of SLA violations in data centers increases. Alternatively, several approaches only focus on a hardware level and reduce the physical machine’s dynamic power consumption. The lack of control over the overload of physical machines increases the amount of violation. In this paper, a framework called PCVM.ARIMA is presented that focuses on the dynamic consolidation of virtual machines over the minimum number of physical machines, minimize the number of unnecessary migrations, detect the physical machine overloading, and SLA based on the ARIMA prediction model. Moreover, the Dynamic Voltage and Frequency Scaling (DVFS) technique is used to apply the optimal frequency to heterogeneous physical machines. The experimental results show that the presented framework significantly reduces energy consumption while it improves the QoS factors in comparison to some baseline methods.