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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
13159
Venue
VLDB
Year
2023
Pagerank
5.093636e-05
Overall Rank
11,426 | 21.61%
DOI
10.14778/3579075.3579076

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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
86 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00035316107
334 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00020875082
498 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.00017440583
657 Towards Scaling Blockchain Systems via Sharding 2019 SIGMOD 0.00015236213
1,020 Blurring the Lines between Blockchains and Database Systems: the Case of Hyperledger Fabric 2019 SIGMOD 0.00012616838
1,344 An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems 2021 VLDB 0.00011094717
1,545 Blockchain Meets Database: Design and Implementation of a Blockchain Relational Database 2019 VLDB 0.00010408717
2,098 A Transactional Perspective on Execute-order-validate Blockchains 2020 SIGMOD 9.1706238e-05
2,803 LedgerDB: A Centralized Ledger Database for Universal Audit and Verification 2020 VLDB 8.1076616e-05
3,116 ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases 2021 SIGMOD 7.7390737e-05
3,219 iBTune: Individualized Buffer Tuning for Large-scale Cloud Databases 2019 VLDB 7.6283153e-05
3,343 LlamaTune: Sample-Efficient DBMS Configuration Tuning 2022 VLDB 7.4983591e-05
3,346 CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions 2021 VLDB 7.4967834e-05
3,926 UDO: Universal Database Optimization using Reinforcement Learning 2021 VLDB 7.0128068e-05
5,655 Blockchains vs. Distributed Databases: Dichotomy and Fusion 2021 SIGMOD 6.1321148e-05
7,096 Why Do My Blockchain Transactions Fail? A Study of Hyperledger Fabric 2021 SIGMOD 5.7051369e-05
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