Abstract
Database (DB) performance tuning is a difficult task that requires a vast amount of skill, experience and efforts in tweaking a DB for optimum results. With the hundreds of parameters to be considered under the diverse application configurations, business logic and software technology, getting a true global optimum setting is difficult for a DB administrator. We propose a novel approach based on Reinforcement Learning to tune a DB adaptively with minimum risk to the production setup. It results in a new set of parameters tailored to the production DB. Empirical results show that there is a significant gain in performance for the DB in its overall efficiency while reducing the IO overheads, based on a set of key performance statistics collected before and after the optimization process.
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Wee, C.K., Nayak, R. (2019). Adaptive Database’s Performance Tuning Based on Reinforcement Learning. In: Ohara, K., Bai, Q. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2019. Lecture Notes in Computer Science(), vol 11669. Springer, Cham. https://doi.org/10.1007/978-3-030-30639-7_9
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DOI: https://doi.org/10.1007/978-3-030-30639-7_9
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