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A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning

Summary: Presents a unified coordinating framework for autonomous DBMS tuning with inter-agent dependencies. Introduces a propagation protocol and Thompson Sampling with a memory buffer to allocate tuning budgets under non-stationary rewards. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
ha798a243cf172481
Venue
SIGMOD
Year
2023
Pagerank
5.6733557e-05
Overall Rank
6,752 | 54.75%
DOI
10.1145/3589331

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{zhang_sigmod23,
        title = {{A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning}},
        author = {Zhang, Xinyi and Chang, Zhuo and Wu, Hong and Li, Yang and Chen, Jia and Tan, Jian and Li, Feifei and Cui, Bin},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3589331},
        url = {https://dl.acm.org/doi/10.1145/3589331},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Rank Citing Paper Year Venue Pagerank
5,304 An Efficient Transfer Learning Based Configuration Adviser for Database Tuning 2024 VLDB 6.1758143e-05
6,464 Towards General and Efficient Online Tuning for Spark 2023 VLDB 5.759941e-05
7,997 The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions 2024 VLDB 5.3938336e-05
10,323 This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch! 2026 SIGMOD 5.0241309e-05
11,207 Automated Database Tuning vs. Human-Based Tuning in a Simulated Stressful Work Environment: A Demonstration of the Database Gym 2025 SIGMOD 4.9605343e-05
11,273 High-Throughput Ingestion for Video Warehouse: Comprehensive Configuration and Effective Exploration 2025 SIGMOD 4.9605343e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 27 of 27 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061463175
74 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00037164008
151 An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server 1997 VLDB 0.00028611216
309 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.0002143249
313 Tuning Database Configuration Parameters with iTuned 2009 VLDB 0.00021288111
382 Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources 2018 SIGMOD 0.0001946654
411 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.0001874043
1,282 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011197535
1,294 An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems 2021 VLDB 0.00011129451
1,400 Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms 2020 VLDB 0.00010759064
1,700 Black or White? How to Develop an AutoTuner for Memory-based Analytics 2020 SIGMOD 9.8224761e-05
1,888 WeTune: Automatic Discovery and Verification of Query Rewrite Rules 2022 SIGMOD 9.4148912e-05
2,389 A Learned Query Rewrite System using Monte Carlo Tree Search 2022 VLDB 8.5228689e-05
2,481 Learning a Partitioning Advisor for Cloud Databases 2020 SIGMOD 8.3862675e-05
2,572 ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases 2021 SIGMOD 8.2581875e-05
2,779 Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation 2022 VLDB 8.0085666e-05
2,929 iBTune: Individualized Buffer Tuning for Large-scale Cloud Databases 2019 VLDB 7.830745e-05
3,057 LlamaTune: Sample-Efficient DBMS Configuration Tuning 2022 VLDB 7.6865211e-05
3,172 CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions 2021 VLDB 7.5556865e-05
3,340 VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition 2021 VLDB 7.3886578e-05
3,486 HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements 2022 SIGMOD 7.2428834e-05
3,655 UDO: Universal Database Optimization using Reinforcement Learning 2021 VLDB 7.1137504e-05
4,101 Towards Dynamic and Safe Configuration Tuning for Cloud Databases 2022 SIGMOD 6.7930727e-05
4,765 Machine Learning for Databases 2021 VLDB 6.4166895e-05
4,990 Budget-aware Index Tuning with Reinforcement Learning 2022 SIGMOD 6.3120056e-05
7,405 Lachesis: Automatic Partitioning for UDF-Centric Analytics 2021 VLDB 5.5172664e-05
12,288 MSQL+: A Plugin Toolkit for Similarity Search under Metric Spaces in Distributed Relational Database Systems 2018 VLDB 4.9605343e-05
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