External vs. Internal: An Essay on Machine Learning Agents for Autonomous Database Management Systems
External vs. Internal: An Essay on Machine Learning Agents for Autonomous Database Management Systems
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DOI：
10.37745/ejcsit.2013/vol10n52431
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发表时间：
2022-05
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通讯作者：
Andrew Pavlo;Matthew Butrovich;Ananya Joshi;Lin Ma;Prashanth Menon;Dana Van Aken;Lisa Lee;R. Salakhutdinov
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文献类型：
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作者：
Andrew Pavlo;Matthew Butrovich;Ananya Joshi;Lin Ma;Prashanth Menon;Dana Van Aken;Lisa Lee;R. Salakhutdinov
有许多可能的方法来配置数据库管理系统（DBMS），这些方法具有管理和设置的挑战性。在具有数千或数百万个单独DBMS的大规模部署中，每个DBMS都有其设置要求，这个问题会增加。最近的研究探索了使用基于机器学习（ML）的代理来克服DBMS的自动调优问题。这些代理从DBMS中提取性能指标和行为信息，然后使用这些数据训练模型，以选择它们预测将具有最大益处的调优操作。本文讨论了在DBMS中集成ML代理的两种工程方法。第一种方法是构建一个外部调优控制器，将DBMS视为黑盒。第二个是将ML代理原生地合并到DBMS的架构中。
There are many possible ways to configure database management systems (DBMSs) have challenging to manage and set.The problem increased in large-scale deployments with thousands or millions of individual DBMS that each have their setting requirements. Recent research has explored using machine learning-based (ML) agents to overcome this problem's automated tuning of DBMSs. These agents extract performance metrics and behavioral information from the DBMS and then train models with this data to select tuning actions that they predict will have the most benefit. This paper discusses two engineering approaches for integrating ML agents in a DBMS. The first is to build an external tuning controller that treats the DBMS as a black box. The second is to incorporate the ML agents natively in the DBMS's architecture.