Two Papers accepted at IEEE MASCOTS 2013
Two papers of the Descartes Research Group, one on storage systems modeling and one on performance prediction in virtualized environments, will be presented at IEEE MASCOTS 2013.
Two papers of the Descartes Research Group have been accepted for publication and will be presented at the IEEE 21st International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS 2013) in San Francisco, USA. Out of 163 submissions, 44 papers have been accepted as full papers, leading to an acceptance rate of 27%.
Abstract—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.
Abstract—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 hypervisor 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.