A Sample-Aware Database Tuning System With Deep Reinforcement Learning

Based on the relationship between client load and overall system performance, the authors propose a sample-aware deep deterministic policy gradient model. Specifically, they improve sample quality by filtering out sample noise caused by the fluctuations of client load, which accelerates the model co...

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Bibliographic Details
Published inJournal of database management Vol. 35; no. 1; pp. 1 - 25
Main Authors Ma, Zongmin, Li, Zhongliang, Tu, Yaofeng
Format Journal Article
LanguageEnglish
Published Hershey IGI Global 01.01.2024
Subjects
Aeronautics
Algorithms
Artificial intelligence
Closed loops
Deep learning
Mathematical models
Parameters
Simulation
Training
Tuning
Workloads
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Summary:Based on the relationship between client load and overall system performance, the authors propose a sample-aware deep deterministic policy gradient model. Specifically, they improve sample quality by filtering out sample noise caused by the fluctuations of client load, which accelerates the model convergence speed of the intelligent tuning system and improves the tuning effect. Also, the hardware resources and client load consumed by the database in the working process are added to the model for training. This can enhance the performance characterization ability of the model and improve the recommended parameters of the algorithm. Meanwhile, they propose an improved closed-loop distributed comprehensive training architecture of online and offline training to quickly obtain high-quality samples and improve the efficiency of parameter tuning. Experimental results show that the configuration parameters can make the performance of the database system better and shorten the tuning time.
ISSN:1063-8016
1533-8010
DOI:10.4018/JDM.333519