An automated approach to create, store, and analyze large-scale experimental data in clouds

The flexibility and scalability of computing clouds make them an attractive application migration target; yet, the cloud remains a black-box for the most part. In particular, their opacity impedes the efficient but necessary testing and tuning prior to moving new applications into the cloud. A natural and presumably unbiased approach to reveal the cloud's complexity is to collect significant performance data by conducting more experimental studies. However, conducting large-scale system experiments is particularly challenging because of the practical difficulties that arise during experimental deployment, configuration, execution and data processing. In this paper we address some of these challenges through Expertus - a flexible automation framework we have developed to create, store and analyze large-scale experimental measurement data. We create performance data by automating the measurement processes for large-scale experimentation, including: the application deployment, configuration, workload execution and data collection processes. We have automated the processing of heterogeneous data as well as the storage of it in a data warehouse, which we have specifically designed for housing measurement data. Finally, we have developed a rich Web portal to navigate, statistically analyze and visualize the collected data. Expertus combines template-driven code generation techniques with aspect-oriented programming concepts to generate the necessary resources to fully automate the experiment measurement process. In Expertus, a researcher provides only the high-level description about the experiment, and the framework does everything else. At the end, the researcher can graphically navigate and process the data in the Web portal.