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.