支持动态反馈决策的拜占庭容错共识算法

doi:10.19665/j.issn1001-2400.20240902

Abstract

Abstract:

The blockchain is popular as a distributed ledger that can record encryptions,ensuring the consistency of transactions in the ledger through consensus algorithms across a network of untrusted nodes.The broadcasting process of voting-based consensus protocols requires a large number of network forwards,with a huge communication overhead that severely affects the on-chain performance.With the increase in the number of nodes,the performance deteriorates dramatically,and the scalability of large-scale nodes is severely constrained.To address the above problems,the consensus process is treated as an optimization problem,and a Byzantine fault-tolerant consensus algorithm using learning automata for voting decision is proposed.Learning automata are embedded into blockchain nodes instead of nodes to complete consensus voting-related operations,reducing the impact of malicious operations of nodes on the system.The consensus decision is made by the master node and its neighboring learning nodes,the master node gives feedback to the learning nodes based on the criterion function and the overall voting result,the learning nodes adjust their voting strategy based on the feedback,and the consensus is reached when the criterion function of the master node converges.The proposed strategy accelerates the convergence of consensus,adjusts the rules of learning automata to reduce the influence of faulty nodes,and uses the reward and punishment mechanism to improve the enthusiasm of normal nodes to participate in the consensus process and reduce the consensus delay.Experimental results show that the consensus algorithm proposed in this paper has a lower complexity compared to the existing algorithms in large-scale node scenarios,and also shows a better fault tolerance in the face of Byzantine nodes,which reduces the impact of the faulty nodes while maintaining the fast transactions and ensures the scalability and fault tolerance of large-scale node networks.

Key words: blockchain, consensus algorithm, learning automata, convergence strategy

CLC Number:

TP311.1

ZHAI Sheping, CAO Yongqiang, YANG Rui, ZHANG Ruiting. Algorithm for byzantine fault-tolerant consensus to support dynamic feedback decision-making[J].Journal of Xidian University, 2024, 51(6): 132-148.

Figures/Tables 13

模拟参数	值
总节点数,N	150
主节点的数量,R	5
学习节点的数量,P	100
平均交易大小,x	1 000 B
区块大小,T_I	2 MB
学习节点故障节点的百分比,f_p	30%
主节点故障节点的百分比,f_r	20%
每个主节点的执行轮次的最大数量,K	500
熵阈值,T_min	0.01
节点初次批准区块的概率,P_approve	0.8
节点初次反对区块的概率,P_disapprove	0.2
主节点批准区块的最佳预定义比例, $λ a p p r o v e *$	0.9
主节点拒绝区块的最佳预定义比例, $λ d i s a p p r o v e *$	0.1
学习节点的奖励学习参数,a	0.15
平均无效区块比率	0.1

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Algorithm for byzantine fault-tolerant consensus to support dynamic feedback decision-making

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