Papers › Cloud Database Tuning with Reinforcement Learning
Cloud Database Tuning with Reinforcement Learning
Chenxia Han, Chaokun Chang
In database management systems (DBMSs), especially cloud DBMSs, configuration tuning is one of the key factors that influence database performance. For a long period of time, the tuning job of databases is done by experienced database administrators (DBAs), which is time-consuming and sub-optimal. Recently, with the development of machine learning, automatic tuning tool starts to play a significant role in DBMSs. Among all the learning-based methods, reinforcement learning has the greatest potentiality to find the optimal or near-optimal configuration. There are already some reinforcement learning applications in database tuning, nonetheless, none of them provide fully executable code. In this project, we implement an independent version of auto-tuner to re-produce the current works on MySQL. Based on a careful design of the system, our code can be easily applied to other databases like JDB, LevelDB, etc. We believe it will benefit a lot to those database administrators who know a lot about databases but lack the knowledge of reinforcement learning. The code is available at https://github.com/ChaokunChang/GDBTuner. The 5 minutes video can be found at Google Drive https://drive.google.com/drive/folders/1Z1MvtBnQ522zPElNu-VSrAMvKcX523LE?usp=sharing.
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