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A novel Raft consensus algorithm combining comprehensive evaluation partitioning and Byzantine fault tolerance

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Abstract

Currently, Raft, as an mainstream consensus mechanism, has received widespread attention. Partition consensus can reduce the number of nodes involved in a single consensus and improve consensus efficiency. However, existing algorithms suffer from unreasonable partitioning and intolerance of Byzantine nodes. To address these problems, this paper proposes a novel Raft consensus algorithm combining comprehensive evaluation partitioning and Byzantine fault tolerance, CB-Raft. First, a comprehensive evaluation of nodes is conducted from the perspectives of consensus behavior and location, and the nodes are evenly divided based on the parity of the comprehensive ranking. Second, the leader is selected from the nodes with the top rankings in the comprehensive evaluation, and the nodes communicate with each other based on BLS signatures. Finally, a fast response mechanism based on cross-partition leader-follower communication is proposed to avoid the continued evil behavior of the leader, and a pipeline mechanism based on changeable signature thresholds is proposed to solve consensus blocking. The experimental results show that compared with the existing partitioning methods, the proposed partitioning scheme has significant advantages in terms of consensus latency, throughput, and the probability of partition success. Compared with the similar Raft algorithms, CB-Raft has high consensus performance and good resistance to Byzantine nodes.

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The coding data that support the findings of this study are available from the corresponding author upon request.

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Funding

This work was supported by the National Natural Science Foundation of China (No. 61762046, No.62166019), the National Natural Science Foundation of Jiangxi Province (No. 20224BAB202019), and the Science and Technology Research Project of the Education Department of Jiangxi Province (No. GJJ218506).

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Authors and Affiliations

  1. School of Information Engineering, Gannan University of Science and Technology, Ganzhou, 341000, Jiangxi, China

    Xiaohong Deng, Weizhi Xiong, Kangting Li & Huiwen Liu

  2. School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, Jiangxi, China

    Xiaohong Deng & Zhiwei Yu

  3. Ganzhou Cloud Computing and Big Data Key Laboratory, Ganzhou, 341000, Jiangxi, China

    Xiaohong Deng

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  1. Xiaohong Deng
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  2. Zhiwei Yu
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  3. Weizhi Xiong
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  4. Kangting Li
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Contributions

XH Deng, ZW Yu, and HW Liu contributed the main ideas and wrote the main manuscript texts. WZ Xiong and KT Li established the evaluation and provided the simulation experimental ideas. All authors reviewed the manuscript.

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Correspondence to Huiwen Liu.

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Deng, X., Yu, Z., Xiong, W. et al. A novel Raft consensus algorithm combining comprehensive evaluation partitioning and Byzantine fault tolerance. J Supercomput 80, 26363–26393 (2024). https://doi.org/10.1007/s11227-024-06438-6

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