Evaluating Approaches for Performance Prediction in Virtualized Environments

Performance management and performance prediction of services deployed in virtualized environments is a challenging task. On the one hand, the virtualization layer makes the estimation of performance model parameters difficult and inaccurate. On the other hand, it is difficult to model the hyper visor scheduler in a representative and practically feasible manner. In this paper, we describe how to obtain relevant parameters, such as the virtualization overhead, depending on the amount and type of available monitoring data. We adapt classical queueing-theory-based modeling techniques to make them usable for different configurations of virtualized environments. We provide answers how to include the virtualization overhead into queueing network models, and how to take the contention between different VMs into account. Finally, we evaluate our approach in representative scenarios based on the SPECjEnterprise2010 standard benchmark and XenServer 5.5, showing significant improvements in the prediction accuracy and discussing further open issues for performance prediction in virtualized environments.