基于加权PageRank算法的Raft共识算法优化解的研究

Yu-shi Wu, Yuansai Wu, Yiran Liu, Tingjun Shi
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引用次数: 5

摘要

针对Raft共识算法在区块链中节点活跃度低、无法防范恶意节点而不能很好应用于联邦链的问题,提出了一种基于节点活跃度和信用机制的Raft共识算法。首先,通过加权PageRank算法,根据不同节点的历史活跃度分配不同的PR值,然后引入信用机制来提高PR值,对恶意节点进行惩罚,对正常节点进行奖励,使每个节点得到最终的权重值WV。通过仿真实验,优化后的Raft共识算法比联邦链中主流的PBFT算法具有更高的吞吐量和更低的共识延迟
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The research of the optimized solutions to Raft consensus algorithm based on a weighted PageRank algorithm
To tackle the problem that the Raft consensus algorithm in blockchain has low node activity and cannot prevent malicious nodes, and thus cannot be well applied to the federated chain, a Raft consensus algorithm based on node activity and credit mechanism is proposed. First of all, through the weighted PageRank algorithm, different PR values are assigned according to the historical activity of different nodes, and then a credit mechanism is introduced to improve the PR values, giving punishment to malicious nodes and reward to normal nodes, so that each node can get the final weight value WV. Through simulation experiments, the optimized Raft consensus algorithm can better resist malicious nodes and has higher throughput and lower consensus latency than the mainstream PBFT algorithm in the federated chain
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