Comparison of the three CPU schedulers in Xen

TLDR

Virtualization enables agile, cost‑effective IT by consolidating servers and scaling resources on demand, yet managing virtualized environments requires dynamic VM resizing and migration, and resource multiplexing—particularly CPU scheduling—remains poorly understood and largely inherited from operating‑system process schedulers. The study investigates whether directly porting process schedulers to virtual machine schedulers yields comparable performance. Using the Xen hypervisor, the authors compare three VM CPU schedulers, analyze how scheduler choice and parameter settings affect application performance, and discuss challenges in estimating application resource needs in virtualized settings.

Abstract

The primary motivation for enterprises to adopt virtualization technologies is to create a more agile and dynamic IT infrastructure -- with server consolidation, high resource utilization, the ability to quickly add and adjust capacity on demand -- while lowering total cost of ownership and responding more effectively to changing business conditions. However, effective management of virtualized IT environments introduces new and unique requirements, such as dynamically resizing and migrating virtual machines (VMs) in response to changing application demands. Such capacity management methods should work in conjunction with the underlying resource management mechanisms. In general, resource multiplexing and scheduling among virtual machines is poorly understood. CPU scheduling for virtual machines, for instance, has largely been borrowed from the process scheduling research in operating systems. However, it is not clear whether a straight-forward port of process schedulers to VM schedulers would perform just as well. We use the open source Xen virtual machine monitor to perform a comparative evaluation of three different CPU schedulers for virtual machines. We analyze the impact of the choice of scheduler and its parameters on application performance, and discuss challenges in estimating the application resource requirements in virtualized environments.