Utilization prediction-based VM consolidation approach

Abstract

Reducing energy consumption and optimizing resource usage in large cloud data centers is still an essential target for the current researchers and cloud providers. The state-of-the-art highlights the effectiveness of VM consolidation and live migrations in achieving reasonable solutions. However, most proposals consider only the real-time workload variations to decide whether a host is overloaded or underloaded, or to trigger migration actions. Such approaches may apply frequent and needless VM migrations leading to energy waste, performance degradation, and service-level agreement (SLA) violations. In this paper, we propose a consolidation approach based on the resource utilization prediction to determine the overloaded and underloaded hosts. The prediction method combines a Kalman filter and support vector regression (SVR) to forecast the host's future CPU utilization. Simulations are conducted on Cloudsim using real PlanetLab workloads to verify the performance of our proposal against existing benchmark algorithms. Experimental results demonstrate that our consolidation technique significantly reduces the SLA violation rate, number of VM migrations, and energy consumed in the datacenter.