LiRCUP: Linear Regression Based CPU Usage Prediction Algorithm for Live Migration of Virtual Machines in Data Centers

Virtualization is a vital technology of cloud computing which enables the partition of a physical host into several Virtual Machines (VMs). The number of active hosts can be reduced according to the resources requirements using live migration in order to minimize the power consumption in this technology. However, the Service Level Agreement (SLA) is essential for maintaining reliable quality of service between data centers and their users in the cloud environment. Therefore, reduction of the SLA violation level and power costs are considered as two objectives in this paper. We present a CPU usage prediction method based on the linear regression technique. The proposed approach approximates the short-time future CPU utilization based on the history of usage in each host. It is employed in the live migration process to predict over-loaded and under-loaded hosts. When a host becomes over-loaded, some VMs migrate to other hosts to avoid SLA violation. Moreover, first all VMs migrate from a host while it becomes under-loaded. Then, the host switches to the sleep mode for reducing power consumption. Experimental results on the real workload traces from more than a thousand Planet Lab VMs show that the proposed technique can significantly reduce the energy consumption and SLA violation rates.