Journal of Guangxi Normal University(Natural Science Edition) ›› 2025, Vol. 43 ›› Issue (4): 108-119.doi: 10.16088/j.issn.1001-6600.2024101101

• Intelligence Information Processing • Previous Articles     Next Articles

Byzantine Fault-Tolerant Consensus Mechanism Based on Raft Improvement

LI Li*, JIANG Miao   

  1. College of Computer and Control Engineering, Northeast Forestry University, Harbin Heilongjiang 150040, China
  • Received:2024-10-11 Revised:2025-01-16 Online:2025-07-05 Published:2025-07-14

Abstract: With the in-depth application of blockchain technology in industries such as finance and healthcare, the challenges it faces are becoming increasingly prominent. Among them, the optimization and development of consensus mechanisms are the most crucial. Currently, the two consensus protocols are mainly adopted by consortium chains, PBFT and Raft, and both have certain limitations. The communication volume of the PBFT consensus mechanism increases exponentially with the increase in the number of nodes in the blockchain network, leading to a decrease in efficiency. Although the Raft consensus mechanism has been optimized in terms of efficiency, its ability to resist Byzantine attacks is weak. To address these issues, a consensus mechanism MRBFT based on Raft that resists Byzantine attacks is proposed. Firstly, a reputation value mechanism is introduced in the Raft election process. By electing nodes with higher reputation values, the reliability of the elected nodes is enhanced. At the same time, a certain proportion of Monitor nodes are elected along with the Leader. Secondly, during the consensus process, the Monitor nodes supervise the behavior of the other nodes to enhance the algorithm’s ability to resist Byzantine attacks. At the same time, the reputation values of the nodes are updated to ensure that the most trustworthy nodes are elected in each round, improving the security of the algorithm. Experimental results show that, under the same conditions of resisting Byzantine attacks as PBFT, as the number of nodes increases, MRBFT has lower communication volume, with communication overhead being 44% of PBFT and throughput being 1.5 times that of PBFT. Compared with similar algorithms, in scenarios with similar security, it has more obvious optimization effects in terms of throughput and consensus delay.

Key words: blockchain, consensus mechanism, Byzantine attacks, supervisory strategy, reputation value mechanism

CLC Number:  TP311.13
[1] 代闯闯, 栾海晶, 杨雪莹, 等. 区块链技术研究综述[J]. 计算机科学, 2021,48(增刊2): 500-508. DOI: 10.11896/jsjkx.201200163.
[2] RAJASEKARAN A S, AZEES M, AL-TURJMAN F. A comprehensive survey on blockchain technology[J]. Sustainable Energy Technologies and Assessments, 2022, 52(Part A): 102039. DOI: 10.1016/j.seta.2022.102039.
[3] AZBEG K, OUCHETTO O, JAI ANDALOUSSI S, et al. An overview of blockchain consensus algorithms: comparison, challenges and future directions[C]// Advances on Smart and Soft Computing. Singapore: Springer, 2021: 357-369. DOI: 10.1007/978-981-15-6048-4_31.
[4] NAKAMOTO S. Bitcoin: a peer-to-peer electronic cash system[EB/OL]. (2008-10-31)[2024-10-11]. https://nakamotoinstitute.org/library/bitcoin/.
[5] SALEH F. Blockchain without waste: proof-of-stake[J]. The Review of Financial Studies, 2021, 34(3): 1156-1190. DOI: 10.1093/rfs/hhaa075.
[6] WANG B, LI H L, PAN L. Optimized DPoS consensus strategy: credit-weighted comprehensive election[J]. Ain Shams Engineering Journal, 2023, 14(2): 101874. DOI: 10.1016/j.asej.2022.101874.
[7] JIANG W X, WU X X, SONG M Y, et al. Improved PBFT algorithm based on comprehensive evaluation model[J]. Applied Sciences, 2023, 13(2): 1117. DOI: 10.3390/app13021117.
[8] HUANG X, LIANG Z H, ZHANG Q K, et al. Research on edge cloud data storage method of power operation site in Internet of things environment based on Paxos algorithm[J]. Journal of Testing and Evaluation, 2024, 52(3): 1738-1751. DOI: 10.1520/JTE20220713.
[9] YANG S J, TAN P L, FU H W. Improved raft consensus algorithm based on NSGA-II and K-Means++[C]// 2024 10th International Symposium on System Security, Safety, and Reliability (ISSSR). Los Alamitos, CA: IEEE Computer Society, 2024: 383-390. DOI: 10.1109/ISSSR61934.2024.00055.
[10] DREYER J, FISHER M, TÖNJES R. Performance analysis of hyperledger fabric 2.0 blockchain platform[C]// Proceedings of the Workshop on Cloud Continuum Services for Smart IoT Systems. New York, NY: Association for Computing Machinery, 2020: 32-38. DOI: 10.1145/3417310.3431398.
[11] 陈子豪, 李强. 基于K-medoids的改进PBFT共识机制[J]. 计算机科学, 2019,46(12): 101-107. DOI: 10.11896/jsjkx.181002014.
[12] 高娜, 周创明, 杨春晓, 等. 基于网络自聚类的PBFT算法改进[J]. 计算机应用研究, 2021, 38(11): 3236-3242. DOI: 10.19734/j.issn.1001-3695.2021.03.0098.
[13] 胡继圆, 于瓅. 基于信誉机制分组的改进PBFT算法[J]. 湖北民族大学学报(自然科学版), 2023,41(1): 85-89, 95. DOI: 10.13501/j.cnki.42-1908/n.2023.03.013.
[14] CHEN J H, ZHANG X, SHANGGUAN P F. Improved PBFT algorithm based on reputation and voting mechanism[J]. Journal of Physics: Conference Series, 2020, 1486(3): 032023. DOI: 10.1088/1742-6596/1486/3/032023.
[15] TANG S, WANG Z Q, JIANG J, et al. Improved PBFT algorithm for high-frequency trading scenarios of alliance blockchain[J]. Scientific Reports, 2022, 12(1): 4426. DOI: 10.1038/s41598-022-08587-1.
[16] GOLAN GUETA G, ABRAHAM I, GROSSMAN S, et al. SBFT: a scalable and decentralized trust infrastructure[C]// 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). Los Alamitos, CA: IEEE Computer Society, 2019: 568-580. DOI: 10.1109/DSN.2019.00063.
[17] 陈苏明, 王冰, 陈玉全, 等. 基于节点分组信誉模型的改进PBFT共识算法[J]. 计算机应用研究, 2023,40(10): 2916-2921. DOI: 10.19734/j.issn.1001-3695.2023.03.0091.
[18] 翟社平, 廉佳颖, 杨锐, 等. 基于Raft分组的实用拜占庭容错共识算法[J]. 计算机应用研究, 2023,40(11): 3218-3224, 3234. DOI: 10.19734/j.issn.1001-3695.2023.03.0100.
[19] 黄冬艳, 李浪, 陈斌, 等. RBFT:基于Raft集群的拜占庭容错共识机制[J]. 通信学报, 2021,42(3): 209-219. DOI: 10.11959/j.issn.1000-436x.2021043.
[20] 李淑芝, 邹懿杰, 邓小鸿, 等. RB-Raft:一种抗拜占庭节点的Raft共识算法[J]. 计算机应用研究, 2022, 39(9): 2591-2596. DOI: 10.19734/j.issn.1001-3695.2022.03.0090.
[21] 杨州, 周创明. 融合可验证随机函数的改进Raft共识算法[J]. 空军工程大学学报, 2023, 24(6): 104-111. DOI: 10.3969/j.issn.2097-1915.2023.06.014.
[22] 李莉, 李昊泽, 李涛. 基于Raft的多主节点拜占庭容错共识机制[J]. 广西师范大学学报(自然科学版), 2024,42(3): 121-130. DOI: 10.16088/j.issn.1001-6600.2023100805.
[23] 袁昊天, 李飞. 基于改进Raft共识算法和PBFT共识算法的双层共识算法[J]. 计算机应用研究, 2024,41(5): 1314-1320. DOI: 10.19734/j.issn.1001-3695.2023.08.0390.
[24] WANG L E, BAI Y, JIANG Q, et al. Beh-raft-chain: a behavior-based fast blockchain protocol for complex networks[J]. IEEE Transactions on Network Science and Engineering, 2021, 8(2): 1154-1166. DOI: 10.1109/TNSE.2020.2984490.
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