{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cloud-database-tuning-with-reinforcement","title":"Cloud Database Tuning with Reinforcement Learning","arxiv_id":null,"date":"2020-12-14","proceeding":"CUHK Course IERG5350 2020 12","authors":["Chenxia Han","Chaokun Chang"],"abstract":"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.","url_abs":"https://openreview.net/forum?id=64vORA8HUNv","url_pdf":"https://openreview.net/pdf?id=64vORA8HUNv","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cloud-database-tuning-with-reinforcement","repo_url":"https://github.com/chaokunchang/gdbtuner","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"management","task_name":"Management"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}