Using Performance Models for Planning the Redeployment to Infrastructure-as-a-Service Environments: A Case Study

Context: Performance models allow software architects to conduct what-if analyses, e.g., to assess deployment scenarios regarding performance. While a typical scenario is the redeployment to Infrastructure-as-a-Service (IaaS) environments, there is currently no empirical evidence that architects can apply performance models in such scenarios for accurate performance analyses and how much effort is required. Objectives: Therefore, we explore the applicability of software performance engineering for planning the redeployment of existing software applications to IaaS environments. Methods: We conduct a case study in which we apply performance engineering to redeploy a realistic existing application to IaaS environments. We select an online book shop implementation (CloudStore) as existing application and engineer a corresponding Palladio performance model. Subsequently, we compare analysis results with measurements gathered from operating CloudStore within (I) a classical on-premise setup, (II) OpenStack, and (III) Amazon EC2. Results: Our case study shows that performance models have a relative accuracy error of less than 12% even for IaaS environments (scenarios (II) and (III)). For scenarios (II) and (III), we saved up to 98% model creation effort by reusing the model from scenario (I), we just re-calibrated processing rates of CPUs within our deployment model. Conclusions: Software architects can plan redeployments by reusing performance models of their existing system, thus, with only minor effort. This is particularly possible for virtualized third-party environments like for Amazon EC2.