自学习星型链空间自适应分配方法  

Self-learning Star Chain Space Adaptive Allocation Method

作　　者：杜立宽 刘晨 王俊陆[1] 宋宝燕[1] DU Likuan;LIU Chen;WANG Junlu;SONG Baoyan(School of Information,Liaoning University,Shenyang 110031,China)

机构地区：[1]辽宁大学信息学院,沈阳110031

出　　处：《计算机科学》2025年第3期359-365,共7页Computer Science

基　　金：国家重点研发计划(2021YFF0901004);辽宁省教育厅高校基本科研项目(理工类)面上项目(揭榜挂帅服务地方项目)(JYTMS20230761);辽宁大学青年科研基金项目(LDYBJB2301);辽宁省应用基础研究计划(2022JH2/101300250)。

摘　　要：区块链分片技术是提高区块链系统吞吐量的有效方法。现有的区块链分片方法大多采用静态平行架构分片方案,未解决跨分片交易比例高的问题,导致吞吐量降低以及潜在的无限交易确认延迟。针对这些问题,提出一种基于自学习的星型链空间自适应分配架构。首先,针对区块链分片系统中跨分片交易比例高的问题,提出一种信标链-分片链架构吞吐量模型;其次,综合分片区块链的吞吐量和时延的关系,在星型链空间的动态决策过程中设计星型链空间奖励函数;最后,提出一种分布式多智能体强化学习动态聚类方法,将每个分片作为智能体共同学习合作策略。实验结果表明,所提方法在吞吐量、跨分片交易比率和交易确认延迟等方面,相比现有方法分别约提升31.74%,35.96%和37.13%。Blockchain sharding technology is an effective method to improve the throughput of blockchain systems.Existing blockchain sharding methods mostly adopt parallel architecture sharding schemes,which have not solved the problem of high cross-shard transaction ratios,leading to reduced throughput and potential infinite transaction confirmation delays.To address these issues,a self-learning-based star chain space adaptive allocation structure is proposed.Firstly,to address the issue of high cross-shard transaction ratios in blockchain sharding systems,a beacon chain-shard chain architecture throughput model is proposed.Secondly,considering the relationship between the throughput and latency of sharded blockchain,a star chain space dyna-mic decision-making process is designed,with a reward function for star chain space.Finally,a distributed multi-agent reinforcement learning dynamic clustering method is proposed,treating each shard as an agent to collectively learn cooperative strategies.Experimental results show that the proposed method improves throughput,cross-shard transaction ratio,and transaction confirmation delay by approximately 31.74%,35.96%,and 37.13% respectively,compared with existing methods.

关 键 词：区块链 分片 深度强化学习 跨分片交易 

分 类 号：TP314[自动化与计算机技术—计算机软件与理论]