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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.<\/jats:p>","DOI":"10.14778\/3579075.3579076","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T17:10:26Z","timestamp":1678122626000},"page":"1000-1012","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Auto-Tuning with Reinforcement Learning for Permissioned Blockchain Systems"],"prefix":"10.14778","volume":"16","author":[{"given":"Mingxuan","family":"Li","sequence":"first","affiliation":[{"name":"People's Public Security, University of China"}]},{"given":"Yazhe","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhongguancun Laboratory"}]},{"given":"Shuai","family":"Ma","sequence":"additional","affiliation":[{"name":"SKLSDE Lab, Beihang University"}]},{"given":"Chao","family":"Liu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences"}]},{"given":"Dongdong","family":"Huo","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences"}]},{"given":"Zhen","family":"Xu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences"}]}],"member":"320","published-online":{"date-parts":[[2023,3,6]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"1","volume-title":"Distributed Databases: Dichotomy and Fusion. 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