Towards Performance Prediction for Cloud Computing Environments based on Goal-oriented Measurements

Scalability and performance are critical quality attributes of applications developed for the cloud. Many of these applications have to support hundreds or thousands of concurrent users with strongly fluctuating workloads. Existing approaches for software performance evaluation do not address the new challenges that arise for applications executed in cloud computing environments. The effects of virtualization on response times, throughput, and resource utilisation as well as the massive number of resources available require new platform and resource models for software performance evaluation. Modelling cloud environments using established approaches for software performance prediction is a cumbersome task that requires a detailed understanding of virtualization techniques and their effect on software performance. Additional complexity comes from the fact that cloud environments may combine multiple virtualization platforms which differ in implementation and performance properties. In this position paper, we propose an approach to infer performance models of cloud computing environments automatically through goal-oriented measurements. The resulting performance models can be directly combined with established model-driven performance prediction approaches. We outline the research challenges that have to be addressed in order to employ the approach for design-time performance predictions of software systems running in cloud computing environments.