Virtual Machines Performance Modeling with Support Vector Regressions

Abstract

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