Automatic Database Management System Tuning Through Large-scale Machine Learning
Authors: Dana Van Aken, Andrew Pavlo, Geoffrey J. Gordon, Bohan Zhang Venue: SIGMOD 2017 The paper presented an automated framework for tuning database configuration knobs called OtterTune. OtterTune uses a hybrid of offline and online learning, which enables it to recognize similar behaviors and perform online optimization faster. Before final optimization of a workload, a database of various knob settings and workloads must be collected. Next, OtterTune uses factor analysis to to first prune the set of performance metrics (a dimensionality reduction technique). Next performance tuning knobs are ranked via Lasso regularization. This allows for the automated tuner to reduce the search space by prioritizing the most impactful knobs. Online, the OtterTune first tries to find a match between a previous workload and the current workload. The next phase is the exploration/exploitation phase which is guided by a Gaussian Process....