Virtual Machines Performance Modeling with Support Vector Regressions

Virtualization is a key technology in cloudcomputing to render on-demand provisioning of virtual services.Xen, an open source paravirtualized virtual machine monitor(hypervisor), has been adopted by many leading data centersof the world today. A scheduler in Xen handles CPU resourcessharing among virtual machines hosted on the same physicalsystem. This study is focused on a scheduler in the currentXen release - the Credit scheduler. Credit uses two parameters(weight and cap) to fine tune CPU resources sharing. Previousstudies have shown that these two parameters can impact variousperformance measures of virtual machines hosted on Xen. In thisstudy, we present a holistic procedure to establish performancemodels of virtual machines. Empirical data of two commonly usedmeasures, namely calculation power and network throughput,were collected by simulations under various settings of weightand cap. We then employed a powerful machine learning tool(multi-kernel support vector regression) to learn performancemodels from the empirical data. These models were evaluatedsatisfactorily by using established procedures in machinelearning