Abstract
We present the A2Cloud-cc suite: a multi-agent recommender system to facilitate Cloud resource selection for scientific applications. The suite comprises two components: A2Cloud framework and Council-of-Classifiers (CC). A2Cloud provides the first level of instance recommendation by profiling the target application and selected Cloud instances using hardware benchmarks. The framework uses this information to generate objective scores that are representatives of an application's execution time and cost on the chosen instances. A2Cloud saves this analysis to a public database for scalable machine-learning (ML). CC comprises three ML agents including collaborative filtering, Naïve Bayes', and multinomial logistic regression classifiers; and one decision-making agent: analytic hierarchy process (AHP). CC downloads the A2Cloud analysis from the database to train the three ML agents for instance recommendation. During testing, AHP pools the instance recommendations from ML agents to yield a single recommendation that satisfies the user's preference for performance and cost. We train the suite using 8 applications and 20 Cloud instances from multiple providers. Our testing with two scientific applications yield prediction accuracy over 90%. The suite's aim is to provide users with a sustainable and user-contributed platform that assists with cost-effective instance selection.