WSPML:An Approach for Enhancing Live VM Migration

Abstract

Live VM migration has been a powerful tool to facilitate system maintenance, load balancing, fault tolerance, and power-saving in data centers. Precopy technique is the best suited approach for live migration. Although pre-copy based live migration provides minimal service downtime, total migration time is prolonged which affect on the degradation of VM’s performance. VM needs the improvement in performance of migration process by reducing the total migration time. In this paper, working set prediction using machine learning (WSPML) is proposed to reduce the total migration time. It uses the prediction model with historical data during the live VM migration process. At first, it trains experimental dataset which includes the performance parameters collected from various workloads by machine learning techniques to build the best prediction model and then predict the working set which can affect the total migration time. We evaluated the effectiveness of the working set prediction algorithm with various workloads with simulation model and the experimental result shows that WSPML can more reduce the total migration time in live VM migration than XEN’s default precopy based live migration.