Using Information from Prior Runs to Improve Automated Tuning Systems

Active Harmony is an automated runtime performance tuning system. In this paper we describe a parameter prioritizing tool to help focus on those parameters that are performance critical. Historical data is also utilized to further speed up the tuning process. We first verify our proposed approaches with synthetic data and finally we verify all the improvements on a real cluster-based web service system. Taken together, these changes allow the Active Harmony system to reduce the time spent tuning from 35% up to 50% and at the same time, reduce the variation in performance while tuning.