计算机科学 ›› 2019, Vol. 46 ›› Issue (12): 101-107.doi: 10.11896/jsjkx.181002014
陈子豪, 李强
CHEN Zi-hao, LI Qiang
摘要: 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。
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