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Hyperledger Fabric共识机制优化方案

孟吴同 张大伟

孟吴同,  张大伟.  Hyperledger fabric共识机制优化方案.  自动化学报,  2021,  47(8): 1885−1898 doi: 10.16383/j.aas.c190516
引用本文: 孟吴同,  张大伟.  Hyperledger fabric共识机制优化方案.  自动化学报,  2021,  47(8): 1885−1898 doi: 10.16383/j.aas.c190516
Meng Wu-Tong,  Zhang Da-Wei.  Optimization scheme for hyperledger fabric consensus mechanism.  Acta Automatica Sinica,  2021,  47(8): 1885−1898 doi: 10.16383/j.aas.c190516
Citation: Meng Wu-Tong,  Zhang Da-Wei.  Optimization scheme for hyperledger fabric consensus mechanism.  Acta Automatica Sinica,  2021,  47(8): 1885−1898 doi: 10.16383/j.aas.c190516

Hyperledger Fabric共识机制优化方案

doi: 10.16383/j.aas.c190516
基金项目: 国家留学基金(201807095023)资助
详细信息
    作者简介:

    孟吴同:北京交通大学硕士研究生. 2017年获得河北大学网络工程学士学位. 主要研究方向为区块链.E-mail: mengwt@bjtu.edu.cn

    张大伟:北京交通大学计算机与信息技术学院副教授. 2004年获得北京航空航天大学通信与信息系统专业博士学位. 主要研究方向为区块链, 安全协议, 可信计算. 本文通信作者.E-mail: dwzhang@bjtu.edu.cn

Optimization Scheme for Hyperledger Fabric Consensus Mechanism

Funds: Supported by China Scholarship Council (201807095023)
More Information
    Author Bio:

    MENG Wu-Tong Master student at the School of Computer and Information Technology, Beijing Jiaotong University. He received his bachelor degree in network engineering from Hebei University in 2017. His main research interest is blockchain

    ZHANG Da-Wei Associate professor at the School of Computer and Information Technology, Beijing Jiaotong University. He received his Ph. D. degree in communication and information system from Beihang University in 2004. His research interest covers blockchain, security protocol and trusted computing. Corresponding author of this paper

  • 摘要:

    针对Hyperledger Fabric使用固定背书节点处理交易所带来的安全风险和性能瓶颈问题, 提出了一种非交互、可验证的随机化背书节点优化方案. 基于“背书−排序−验证”的Hyperledger fabric共识模型, 引入背书节点候选集, 使用可验证随机函数随机抽取背书节点进行交易背书, 实现了背书节点的非交互式可验证随机选取和背书过程的并行处理. 分析和实验表明, 优化后的共识机制具有更高的安全性和更快的交易处理速度.

  • 图  1  Hyperledger fabric共识机制

    Fig.  1  Hyperledger fabric consensus mechanism

    图  2  优化后的Hyperledger fabric共识机制

    Fig.  2  Optimized hyperledger fabric consensus mechanism

    图  3  敌手攻击成功的概率

    Fig.  3  Probability of successful enemy attack

    图  4  实验网络拓扑图

    Fig.  4  Network topology of experiment

    图  5  原有方案与优化方案交易时间对比

    Fig.  5  The comparison of transaction time between original scheme and optimized scheme

    图  6  原有方案与优化方案交易延迟对比

    Fig.  6  The comparison of transaction delay between original scheme and optimized scheme

    图  7  原有方案与优化方案通信成本对比

    Fig.  7  The comparison of communication cost between original scheme and optimization scheme

    表  1  优化方案与其他共识机制的对比

    Table  1  Comparison of optimization scheme with other consensus mechanisms

    共识机制 VRF 的作用 共识原理 资源消耗 容错能力
    Algorand 出块节点的选取 VRF + PBFT $3f+1$
    Definity 出块节点的选取 VRF + PoS 较高 $2f+1$
    Ouroboros Praos 出块节点的选取 VRF + PoS 较高 $2f+1$
    优化方案 背书节点的选取 VRF + 背书 + 排序 + 验证 F($m,t$)
    下载: 导出CSV

    表  2  敌手攻击成功次数

    Table  2  Number of successful attacks by adversary

    交易次数 敌手成功次数 攻击成功概率
    原始方案 1 000 1 000 100 %
    优化方案 100 000 686 6.86 %
    下载: 导出CSV

    表  3  无背书节点情况发生次数

    Table  3  Frequency of nonoccurence of endorsing peer

    交易次数 是否使用计时重传 无背书节点情况发生次数
    1 000 2
    100 000 17
    1 000 0
    100 000 0
    下载: 导出CSV

    表  4  可验证随机函数各部分算法运行时间

    Table  4  Running time of each part of the VRF algorithm

    算法 次数 总时间 (ms) 平均时间 (ms)
    生成密钥 10000 2878.3546 0.2878
    生成随机数和证明 10000 10395.3927 1.0395
    验证随机数和证明 10000 12874.6190 1.2875
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-07-07
  • 录用日期:  2019-12-15
  • 网络出版日期:  2020-01-20
  • 刊出日期:  2021-08-20

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