Resource Leasing and the Art of Suspending Virtual Machines

Abstract

Using virtual machines as a resource provisioning mechanism offers multiple benefits, most recently exploited by "infrastructure-as-a-service" clouds, but also poses several scheduling challenges. More specifically, although we can use the suspend/resume/migrate capability of virtual machines to support advance reservation of resources efficiently, by using suspension/resumption as a preemption mechanism, this requires adequately modeling the time and resources consumed by these operations to ensure that preemptions are completed before the start of a reservation. In this work we present a model for predicting various runtime overheads involved in using virtual machines, allowing us to efficiently support advance reservations. We extend our lease management software, Haizea, to use this new model in its scheduling decisions, and we use Haizea with the OpenNebula virtual infrastructure manager so the scheduling decisions will be enacted in a Xen cluster. We present both physical and simulated experimental results showing the degree of accuracy of our model and the long-term effects of variables in our model on several workloads.