Quantifying and Improving I/O Predictability in Virtualized Systems

Abstract—Virtualization enables the consolidation of virtual machines (VMs) to increase the utilization of physical servers in Infrastructure-as-a-Service (IaaS) cloud providers. However, our experience shows that storage I/O performance varies wildly in the face of consolidation. Since many users may desire consistent performance, we argue that IaaS providers should offer a class of predictable-performance service in addition to existing (predictability-oblivious) services. Thus, we propose VirtualFence, a storage system that provides predictable VM performance. Vir-tualFence uses three main techniques: (1) non-work-conserving time-division I/O scheduling, (2) a small solid-state (SSD) cache in front of a much larger hard disk drive (HDD), and (3) space-partitioning of both the SSD cache and the HDD. Our evaluation shows that VirtualFence improves predictability significantly, while allowing cloud providers to reach any desired compromise between predictability and performance. I