Abstrakt
Server virtualization is a key technology to share physical resources efficiently and flexibly. With the increasing popularity of I/O-intensive applications, however, the virtualized storage used in shared environments can easily become a bottleneck and cause performance and scalability issues.
Performance modeling and evaluation techniques applied prior to system deployment help to avoid such issues. In current practice, however, virtualized storage and its effects on the overall system performance are often neglected or treated as a black-box.
In this paper, we present a systematic I/O performance modeling approach for virtualized storage systems based on queueing theory. We first propose a general performance model building methodology.
Then, we demonstrate our methodology creating I/O queueing models of a real-world representative environment based on IBM System z and IBM DS8700 server hardware. Finally, we present an in-depth evaluation of our models considering both interpolation and extrapolation scenarios as well as scenarios with multiple virtual machines.
Overall, we effectively create performance models with less than 11% mean prediction error in the worst case and less than 5% prediction error on average.