Energy Aware Consolidation Algorithm Based on K-Nearest Neighbor Regression for Cloud Data Centers

In this paper, we propose a dynamic virtual machine consolidation algorithm to minimize the number of active physical servers on a data center in order to reduce energy cost. The proposed dynamic consolidation method uses the k-nearest neighbor regression algorithm to predict resource usage in each host. Based on prediction utilization, the consolidation method can determine (i) when a host becomes over-utilized (ii) when a host becomes under-utilized. Experimental results on the real workload traces from more than a thousand Planet Lab virtual machines show that the proposed technique minimizes energy consumption and maintains required performance levels.