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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Hoffer, J., Ramesh, V., Topi, H.: Modern Database Management. Prentice Hall, New Jersey (2015)
Colle, R., et al.: Oracle database replay. Proc. VLDB Endow. 2(2), 1542–1545 (2009)
Mellouk, A.: Advances in Reinforcement Learning. InTech, London (2011)
Ding, Z., Wei, Z., Chen, H.: A software cybernetics approach to self-tuning performance of on-line transaction processing systems. J. Syst. Softw. 124, 247–259 (2017)
Rabinovitch, G., Wiese, D.: Non-linear optimization of performance functions for autonomic database performance tuning. In: Third International Conference on Autonomic and Autonomous Systems, ICAS 2007. IEEE (2007)
Rodd, S., Kulkarni, U.P.: Adaptive self-tuning techniques for performance tuning of database systems: a fuzzy-based approach with tuning moderation. Soft. Comput. 19(7), 2039–2045 (2015)
Mahgoub, A., et al.: Rafiki: a middleware for parameter tuning of NoSQL datastores for dynamic metagenomics workloads. In: Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference. ACM (2017)
Van Aken, D., et al.: Automatic database management system tuning through large-scale machine learning. In: Proceedings of the 2017 ACM International Conference on Management of Data. ACM (2017)
Oracle Corporation: Master Note: Database Performance Overview (Doc ID 402983.1) (2018)
Antognini, C.: Troubleshooting Oracle Performance. Apress, New York (2014)
Coronel, C., Morris, S.: Database Systems: Design, Implementation, & Management. Cengage Learning, Boston (2016)
Alapati, S.R., et al.: Oracle Database 12c Performance Tuning Recipes: A Problem-Solution Approach. The Expert’s Voice in Oracle. 1 online resource (li, 581 p.)
Kans, M., Ingwald, A.: Common database for cost-effective improvement of maintenance performance. Int. J. Prod. Econ. 113(2), 734–747 (2008)
Habibi, A., Sarafrazi, A., Izadyar, S.: Delphi technique theoretical framework in qualitative research. Int. J. Eng. Sci. 3(4), 8–13 (2014)
Alapati, S., Kuhn, D., Padfield, B.: Oracle Database 12c Performance Tuning Recipes: A Problem-Solution Approach. Apress, New York (2014)
Wei, Z., Ding, Z., Hu, J.: Self-tuning performance of database systems based on fuzzy rules. In: 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE (2014)
Kuhn, D., Alapati, S., Nanda, A.: Performing flashback recovery. In: Kuhn, D., Alapati, S., Nanda, A. (eds.) RMAN Recipes for Oracle Database 12c, pp. 395–442. Apress, Bereley (2013). https://doi.org/10.1007/978-1-4302-4837-8_13
Ngai, G., et al.: Automatic workload repository battery of performance statistics. Google Patents (2009)
Oracle Corporation: Oracle Database 12c Release 2 (12.2) New Features (2018)
Kuhn, D., et al.: RMAN Recipes for Oracle Database 12c: A Problem-Solution Approach. The Expert’s Voice in Oracle, 2nd edn. Apress, Berkeley (2013). 1 online resource (730 p.)
Van Hasselt, H., Guez, A., Silver, D.: Deep Reinforcement Learning with Double Q-Learning. In: AAAI (2016)
Gryglewicz-Kacerka, W., Kacerka, J.: Analysis of the effect of chosen initialization parameters on database performance. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2015. CCIS, vol. 521, pp. 60–68. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18422-7_5
Sharma, H.K., Nelson, S.: Performance enhancement using SQL statement tuning approach. Database Syst. J. 8(1), 12–21 (2017)
Wiese, D., Rabinovitch, G.: Knowledge management in autonomic database performance tuning. In: Fifth International Conference on Autonomic and Autonomous Systems (ICAS 2009). IEEE (2009)
Zhou, J., et al.: Improving database performance on simultaneous multithreading processors. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB Endowment (2005)
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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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