基于K-medoids的改进PBFT共识机制

Improved PBFT Consensus Mechanism Based on K-medoids

随着数字货币的普及与发展,区块链技术进入了大众的视野,并被誉为信用历史上第四个里程碑,是未来信用的基石[1]。但与此同时,区块链技术也面临着共识效率低、算力浪费等问题。文中利用K-medoids聚类算法对参与区块链共识的大规模网络节点根据特征进行聚类与层次划分,再将改进的多中心化实用拜占庭容错算法应用于这种聚类后的分层模型中。另外,为了提升聚类算法在多种场景下对区块链模型中共识节点进行聚类的可控性,对K-medoids算法进行了改进。网络拓扑仿真环境实验表明,当选择了适当的聚类特征评判节点间的相似度时,改进后的算法K-PBFT在1000个网络节点参与共识的场景中相较于传统实用拜占庭容错算法(Practical Byzantine Fault Tolerance,PBFT)算法,单次共识耗时缩短了20%,共识过程的通信次数最佳能够降低3个数量级。结果证明K-PBFT算法优化了较大规模共识节点参与的共识过程,使区块链模型能够适用于更广泛的场景中。

With the popularization and development of digital currency,the blockchain technology enters the public’s vision,and has been hailed as the fourth milestone in credit history,the cornerstone of future credit[1].However,blockchain technology is also facing problems such as low efficiency of consensus and waste of computing power.The K-medoids clustering algorithm is used to cluster and hierarchically divide the large scale network nodes participating in the blockchain consensus based on features,and then the improved multi-centered PBFT(Practical Byzantine Fault Tole-rance)consensus algorithm is applied to the clustered model.Moreover,in order to improve the controllability of clustering algorithm to cluster nodes in blockchain model under various scenarios,this paper improved K-medoids algorithm.Simulation results show that when appropriate clustering features are selected to evaluate the similarity between nodes,the improved algorithm K-PBFT reduces the single-consensus time consumption by 20%,and the consensus process communication times can be reduced by three orders of magnitude.The experimental results show that K-PBFT optimizes the consensus process involving large-scale nodes,so that the blockchain technology can be applied to a wider range of application scenarios.