Machine Learning Based Statistical Prediction Model for Improving Performance of Live Virtual Machine Migration

Abstract

Service can be delivered anywhere and anytime in cloud computing using virtualization. The main issue to handle virtualized resources is to balance ongoing workloads. The migration of virtual machines has two major techniques: (i) reducing dirty pages using CPU scheduling and (ii) compressing memory pages. The available techniques for live migration are not able to predict dirty pages in advance. In the proposed framework, time series based prediction techniques are developed using historical analysis of past data. The time series is generated with transferring of memory pages iteratively. Here, two different regression based models of time series are proposed. The first model is developed using statistical probability based regression model and it is based on ARIMA (autoregressive integrated moving average) model. The second one is developed using statistical learning based regression model and it uses SVR (support vector regression) model. These models are tested on real data set of Xen to compute downtime, total number of pages transferred, and total migration time. The ARIMA model is able to predict dirty pages with 91.74% accuracy and the SVR model is able to predict dirty pages with 94.61% accuracy that is higher than ARIMA.