Self-learning Star Chain Space Adaptive Allocation Method

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.