A Collaborative Multi-Agent Model for Database Parameter Tuning

被引：0

作者：

Li, Jiang-Min ^{[1
]}

Qiao, Shao-Jie ^{[1
]}

Han, Nan ^{[2
]}

Wu, Tao ^{[3
]}

Gao, Rui-Wei ^{[1
]}

Peng, Yu-Han ^{[1
]}

Xie, Tian-Cheng ^{[1
]}

Ran, Li-Qiong ^{[1
]}

机构：

[1] School of Software Engineering, Chengdu University of Information Technology, Sichuan, Chengdu

[2] School of Management, Chengdu University of Information Technology, Sichuan, Chengdu

[3] School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2024年 / 52卷 / 11期

基金：

中国国家自然科学基金;

关键词：

database system; joint training model; multi-agent; parameter tuning; self-learning;

D O I：

10.12263/DZXB.20230724

中图分类号：

学科分类号：

摘要：

Database parameter tuning is one of the crucial tasks in improving the performance of database systems. Database parameters can be classified based on their scopes and functionalities. It plays an essential role in investigating the mutual influence of parameters within a specific category or between different categories. But, the existing methods do not take into consideration this aspect. A collaborative multi-agent model called DBT-MADDPG (DataBase Tuning-Multi-Agent Deep Deterministic Policy Gradient) is proposed for database parameter tuning. A single-agent pre-training model called SA (Single Agent), a multi-agent joint training model called JAM (Joint Action Model), and a joint training model based on probabilistic selection called JAPM (Joint Action Probability Model) are designed for tuning the database parameters at different stages. The experimental results show that the DBT-MADDPG model is capable of tuning the database parameters at different functional and parameter levels, and can reach the performance of mainstream algorithms in the training stage of the SA model, and is 17.85% faster than the state-of-the-art algorithms to obtain the optimal configuration. © 2024 Chinese Institute of Electronics. All rights reserved.

引用

页码：3751 / 3756

页数：5

共 15 条

[1]

LI G L, ZHOU X H, SUN J, Et al., A survey of machine learning based database techniques, Chinese Journal of Computers, 43, 11, pp. 2019-2049, (2020)

[2]

ZHAO X Y, ZHOU X H, LI G L., Automatic database knob tuning: A survey, IEEE Transactions on Knowledge and Data Engineering, 35, 12, pp. 12470-12490, (2023)

[3]

CEREDA S, VALLADARES S, CREMONESI P, Et al., CGPTuner, Proceedings of the VLDB Endowment, 14, 8, pp. 1401-1413, (2021)

[4]

ZHU Y Q, LIU J X, GUO M Y, Et al., BestConfig: Tapping the performance potential of systems via automatic configuration tuning, Proceedings of the 2017 Symposium on Cloud Computing, pp. 338-350, (2017)

[5]

ZHANG B, VAN AKEN D, WANG J, Et al., A demonstration of the ottertune automatic database management system tuning service, Proceedings of the VLDB Endowment, 11, 12, pp. 1910-1913, (2018)

[6]

LI W G, GAN P, XIE L, Et al., A few-shot image classification method by pairwise-based meta learning, Acta Electronica Sinica, 50, 2, pp. 295-304, (2022)

[7]

ZHANG X Y, WU H, CHANG Z, Et al., ResTune: Resource oriented tuning boosted by meta-learning for cloud databases, Proceedings of the 2021 International Conference on Management of Data, pp. 2102-2114, (2021)

[8]

LI G L, ZHOU X H, LI S F, Et al., QTune, Proceedings of the VLDB Endowment, 12, 12, pp. 2118-2130, (2019)

[9]

ZHANG J, LIU Y, ZHOU K, Et al., An end-to-end automatic cloud database tuning system using deep reinforcement learning, Proceedings of the 2019 International Conference on Management of Data, pp. 415-432, (2019)

[10]

TAN J, ZHANG T, LI F, Et al., iBTune: Individualized buffer tuning for large-scale cloud databases, Proceedings of the VLDB Endowment, 12, 10, pp. 1221-1234, (2019)

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