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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
- 12972
- Venue
- VLDB
- Year
- 2023
- Pagerank
- 5.1725247e-05
- Overall Rank
- 11,227 | 21.98%
- DOI
-
10.14778/3579075.3579076
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Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
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 |
| 89 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00035598024 |
| 334 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.00020961385 |
| 493 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00017600244 |
| 649 |
Towards Scaling Blockchain Systems via Sharding |
2019 |
SIGMOD |
0.00015471765 |
| 997 |
Blurring the Lines between Blockchains and Database Systems: the Case of Hyperledger Fabric |
2019 |
SIGMOD |
0.00012839982 |
| 1,324 |
An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems |
2021 |
VLDB |
0.0001126122 |
| 1,520 |
Blockchain Meets Database: Design and Implementation of a Blockchain Relational Database |
2019 |
VLDB |
0.00010567219 |
| 2,042 |
A Transactional Perspective on Execute-order-validate Blockchains |
2020 |
SIGMOD |
9.3376886e-05 |
| 2,727 |
LedgerDB: A Centralized Ledger Database for Universal Audit and Verification |
2020 |
VLDB |
8.2578082e-05 |
| 3,161 |
ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases |
2021 |
SIGMOD |
7.7501079e-05 |
| 3,168 |
iBTune: Individualized Buffer Tuning for Large-scale Cloud Databases |
2019 |
VLDB |
7.7448853e-05 |
| 3,298 |
LlamaTune: Sample-Efficient DBMS Configuration Tuning |
2022 |
VLDB |
7.6094798e-05 |
| 3,430 |
CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions |
2021 |
VLDB |
7.4959348e-05 |
| 3,858 |
UDO: Universal Database Optimization using Reinforcement Learning |
2021 |
VLDB |
7.119457e-05 |
| 5,581 |
Blockchains vs. Distributed Databases: Dichotomy and Fusion |
2021 |
SIGMOD |
6.2269951e-05 |
| 6,879 |
Why Do My Blockchain Transactions Fail? A Study of Hyperledger Fabric |
2021 |
SIGMOD |
5.8169158e-05 |
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