基于随机森林的授权节点优化改进Raft算法

Hao Tang, Wenlong Yi, Yingding Zhao, Jinlong Huang, H. Xiong, Qiude Li
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引用次数: 2

摘要

传统的Raft算法存在候选人之间的“抢票”问题,导致在没有获得过半数选票的情况下出现异常选举。为了解决这个问题,引入了一种基于随机森林的方法来识别授权节点。首先,在原Raft算法中引入授权属性作为授权节点的标签,即只有授权节点才有资格获得候选资格。其次,引入随机森林算法,根据授权节点的任期、广播时间、超时时间等特征对授权节点进行分类,将所有节点划分为首选节点和普通节点。最后,优选节点组成的主共识组采用积分累加的方式,将积分最高的优选节点顶出作为授权节点。结果表明,优化后的Raft算法避免了异常选举,选举效率优于原算法。该方法为联盟区块链提供了一种有序的节点运行机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved Raft Algorithm for Optimizing Authorized Nodes Based on Random Forest
The traditional Raft algorithm has such an issue as "vote snatching" among candidates that results in abnormal elections in the absence of a majority. To address this issue, a random forest-based method is introduced for the identification of authorized nodes. First, the attributes of authorization are introduced to the original Raft algorithm to be the label of authorized nodes, i.e., only authorized nodes are eligible for candidacy. Second, the random forest algorithm is introduced to classify the authorized nodes according to their term, broadcasting duration, timeout duration, and other characteristics to divide all nodes into preferred nodes and ordinary nodes. In the end, the main consensus group makes up of preferred nodes adopted integral accumulation to top out the preferred node with the highest integral as the authorized nodes. According to the results, the optimized Raft algorithm avoids abnormal elections and outperforms the original algorithm in election efficiency. The method offers an orderly mechanism of node operation for the Consortium Blockchain.
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