Auto-Tuning with Reinforcement Learning for Permissioned Blockchain Systems

【Author】 Li, Mingxuan; Wang, Yazhe; Ma, Shuai; Liu, Chao; Huo, Dongdong; Wang, Yu; Xu, Zhen

【Source】PROCEEDINGS OF THE VLDB ENDOWMENT

【影响因子】3.557

【Abstract】In a permissioned blockchain, performance dictates its development, which is substantially influenced by its parameters. However, research on auto-tuning for better performance has somewhat stagnated because of the difficulty posed by distributed parameters; thus, it is possible only with difficulty to propose an effective auto-tuning optimization scheme. To alleviate this issue, we lay a solid basis for our research by first exploring the relationship between parameters and performance in Hyperledger Fabric, a permissioned blockchain, and we propose Athena, a Fabric-based auto-tuning system that can automatically provide parameter configurations for optimal performance. The key of Athena is designing a new Permissioned Blockchain Multi-Agent Deep Deterministic Policy Gradient (PB-MADDPG) to realize heterogeneous parameter-tuning optimization of different types of nodes in Fabric. Moreover, we select parameters with the most significant impact on accelerating recommendation. In its application to Fabric, a typical permissioned blockchain system, with 12 peers and 7 orderers, Athena achieves a throughput improvement of 470.45% and a latency reduction of 75.66% over the default configuration. Compared with the most advanced tuning schemes (CDBTune, Qtune, and ResTune), our method is competitive in terms of throughput and latency.

【Keywords】

【发表时间】2023 JAN

【收录时间】2023-06-24

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-强化学习