Predictive performance modeling of virtualized storage systems using optimized statistical regression techniques
Proceedings of the ACM/SPEC international conference on International conference on performance engineering - ICPE '13
Modern virtualized environments are key for reducing the operating costs of data centers. By enabling the sharing of physical resources, virtualization promises increased resource efficiency with decreased administration costs. With the increasing popularity of I/O-intensive applications, however, the virtualized storage used in such environments can quickly become a bottleneck and lead to performance and scalability issues. Performance modeling and evaluation techniques applied prior to system
... ied prior to system deployment help to avoid such issues. In current practice, however, virtualized storage and its performance-influencing factors are often neglected or treated as a black-box. In this paper, we present a measurement-based performance prediction approach for virtualized storage systems based on optimized statistical regression techniques. We first propose a general heuristic search algorithm to optimize the parameters of regression techniques. Then, we apply our optimization approach and create performance models using four regression techniques. Finally, we present an in-depth evaluation of our approach in a real-world representative environment based on IBM System z and IBM DS8700 server hardware. Using our optimized techniques, we effectively create performance models with less than 7% prediction error in the most typical scenario. Furthermore, our optimization approach reduces the prediction error by up to 74%.