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

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.