The increasing heterogeneity of the VM offerings on public IaaS clouds gives rise to a very large number ofdeployment optionsfor constructing distributed, multi-component cloud applications. However, selecting an appropriatedeployment variant, i.e., a valid combination of deployment options, to meet required performance levels is non-trivial. The combinatorial explosion of thedeployment spacemakes it infeasible to measure the performance of all deployment variants to build a comprehensive empirical performance model. To address this problem, we proposeFeature-Oriented Cloud(FOCloud), a performance engineering approach for deployment configurable cloud applications. FOCloud (i) uses feature modeling to structure and constrain the valid deployment space by modeling the commonalities and variations in the different deployment options and their inter-dependencies, (ii) uses sampling and machine learning to incrementally and cost-effectively build a performance prediction model whose input variables are the deployment options, and the output variable is the performance of the resulting deployment variant, and (iii) uses Explainable AI techniques to provide explanations for the prediction outcomes of valid deployment variants in terms of the deployment options. We demonstrate the practicality and feasibility of FOCloud by applying it to an extension of the RuBiS benchmark application deployed on Google Cloud.

J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2022.3142853