Abstract-Modern IT systems frequently employ virtualization technology to maximize resource efficiency. By sharing physical resources, however, the virtualized storage used in such environments can quickly become a bottleneck. Performance modeling and evaluation techniques applied prior to system deployment help to avoid performance issues. In current practice, however, modeling I/O performance is usually avoided due to the increasing complexity of modern virtualized storage systems. In this paper, we present an automated modeling approach based on statistical regression techniques to analyze I/O performance and interference effects in the context of virtualized storage systems. We demonstrate our approach in three case studies creating performance models with two I/O benchmarks. The case studies are conducted in a real-world environment based on IBM System z and IBM DS8700 server hardware. Using our approach, we effectively create performance models with excellent prediction accuracy for both I/O-intensive applications and I/O performance interference effects with a mean prediction error up to 7%
