Host utilization prediction using hybrid kernel based support vector regression in cloud data centers

Abstract

The rapid growth in the cloud data center needs a dynamic resource provision to maintain the Quality of Services parameters. To guarantee it, Virtual Machine Migration as part of VM Consolidation has a significant role. Efficient VM migration requires knowledge of the host’s future utilization in advance. Because of the high variation in cloud resource usage and dynamic workloads, predicting host utilization using utilization history is challenging. This paper proposes a Support Vector Regression-based methodology to predict a host‘s future utilization using multiple resource's utilization history. A Hybrid Kernel function that includes radial basis function and polynomial kernel function has been proposed and then trains the Support Vector Machine using multiple-resource utilization history. Compared to the existing approaches: multiple linear regression-based prediction, Euclidean distance, and Absolute Summation based regression, the proposed method performs better in terms of root mean square error, mean square error, mean absolute percentage error, mean absolute error, and R2. The result section concludes that on evaluating error percent, the prediction error is 16% for the proposed approach and predicts host utilization with 7%, 64%, and 67% more accuracy than MRHOD, MDRHU-ED, MDRHU-AS approaches, respectively.