Paravirtualization for Scientific Computing: Performance Analysis and Prediction

Resource virtualization technologies have recently increased in popularity. The emergence of cloud computing, which requires provisioning isolated environments on shared resources, is one reason for this. Virtualization adds flexibility in terms of resource provisioning, but it can impact application performance. In this work, we analyze the performance of medical image processing and computational fluid dynamics applications when run on virtualized resources. We then apply the observed performance characteristics to a performance prediction model. We measure the impact of virtualization by performing several benchmarks on virtualized and non-virtualized resources. We evaluate the accuracy of the performance prediction model in this environment. We find that virtualization can slow down some applications by more than 200%, but usually the performance impact is below 15%. The overhead itself is predictable if general application characteristics are known. Execution time in a virtual environment can be predicted to within 13% using a simple mathematical prediction model.