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Auto-Tuning with Reinforcement Learning for Permissioned Blockchain Systems

Summary: Athena: an auto-tuner for Hyperledger Fabric using a novel PB‑MADDPG multi‑agent RL to optimize heterogeneous, distributed node parameters. By selecting high‑impact knobs, Athena yields up to 470% throughput and 75% latency improvements on a 12-peer/7-orderer Fabric, competitive with CDBTune/Qtune/ResTune. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
h2763908dce2f6556
Venue
VLDB
Year
2023
Pagerank
4.9605343e-05
Overall Rank
11,790 | 20.97%
DOI
10.14778/3579075.3579076
PDF
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Authors

BibTeX Citation

@article{li_vldb23,
        title = {{Auto-Tuning with Reinforcement Learning for Permissioned Blockchain Systems}},
        author = {Li, Mingxuan and Wang, Yazhe and Ma, Shuai and Liu, Chao and Huo, Dongdong and Wang, Yu and Xu, Zhen},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {5},
        pages = {1000--1012},
        doi = {10.14778/3579075.3579076},
        url = {https://doi.org/10.14778/3579075.3579076},
        year = {2023}
}

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Outgoing Citations (Sorted by Pagerank)

Showing 16 of 16 cited papers.

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

Rank Cited Paper Year Venue Pagerank
74 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00037164008
309 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.0002143249
411 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.0001874043
666 Towards Scaling Blockchain Systems via Sharding 2019 SIGMOD 0.00014946867
1,042 Blurring the Lines between Blockchains and Database Systems: the Case of Hyperledger Fabric 2019 SIGMOD 0.00012325917
1,294 An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems 2021 VLDB 0.00011129451
1,579 Blockchain Meets Database: Design and Implementation of a Blockchain Relational Database 2019 VLDB 0.00010155728
2,146 A Transactional Perspective on Execute-order-validate Blockchains 2020 SIGMOD 8.9604977e-05
2,572 ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases 2021 SIGMOD 8.2581875e-05
2,868 LedgerDB: A Centralized Ledger Database for Universal Audit and Verification 2020 VLDB 7.9050362e-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
3,655 UDO: Universal Database Optimization using Reinforcement Learning 2021 VLDB 7.1137504e-05
5,785 Blockchains vs. Distributed Databases: Dichotomy and Fusion 2021 SIGMOD 5.9771676e-05
7,229 Why Do My Blockchain Transactions Fail? A Study of Hyperledger Fabric 2021 SIGMOD 5.5636862e-05
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