Improvement of Practical Byzantine Fault Tolerant Consensus Algorithm for Blockchain

Among various consensus algorithms, the Byzantine Fault Tolerance (BFT)-based consensus algorithms are broadly used for private blockchain. However, as BFT-based consensus algorithms are structured for all participants to take part in a consensus process, a scalability issue becomes more noticeable. In this approach, we introduce a consensus coordinator to execute a conditionally BFT-based consensus algorithm by classifying transactions. Transactions are divided into equal and unequal transactions. Moreover, unequal transactions are divided again and classified as common and trouble transactions. After that, a consensus algorithm is only executed for trouble transactions, and BFT-based consensus algorithms can achieve scalability. For evaluating our approach, we carried out three experiments in response to three research questions. By applying our approach to PBFT, we obtained 4.75 times better performance than using only PBFT. In the other experiment, we applied our approach to IBFT of Hyperledger Besu, and our result shows a 61.81% performance improvement. In all experiments depending on the change of the number of blockchain nodes, we obtained the better performance than original BFT-based consensus algorithms; thus, we can conclude that our approach improved the scalability of original BFT-based consensus algorithms. We also showed a correlation between performance and trouble transactions associated with transaction issue intervals and the number of blockchain nodes.

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Research on Dynamic PBFT Consensus Algorithm

10.5121/csit.2020.101907 ◽

2020 ◽

Author(s):

Cao Xiaopeng ◽

Shi Linkai

Keyword(s):

Fault Tolerance ◽

Connected Graph ◽

Fault Tolerant ◽

Consensus Algorithm ◽

Byzantine Fault Tolerance ◽

Master Node ◽

Practical Application ◽

Network Bandwidth ◽

Byzantine Fault

The practical Byzantine fault-tolerant algorithm does not add nodes dynamically. It is limited in practical application. In order to add nodes dynamically, Dynamic Practical Byzantine Fault Tolerance Algorithm (DPBFT) was proposed. Firstly, a new node sends request information to other nodes in the network. The nodes in the network decide their identities and requests. Then the nodes in the network reverse connect to the new node and send block information of the current network, the new node updates information. Finally, the new node participates in the next round of consensus, changes the view and selects the master node. This paper abstracts the decision of nodes into the undirected connected graph. The final consistency of the graph is used to prove that the proposed algorithm can adapt to the network dynamically. Compared with the PBFT algorithm, DPBFT has better fault tolerance and lower network bandwidth.

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Improved Byzantine Fault-Tolerant Algorithm Based on Alliance Chain

Wireless Communications and Mobile Computing ◽

10.1155/2021/8455180 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Wuqi Gao ◽

Wubing Mu ◽

Shanshan Huang ◽

Man Wang ◽

Xiaoyan Li

Keyword(s):

Fault Tolerance ◽

Fault Tolerant ◽

Feedback Mechanism ◽

Consensus Algorithm ◽

Byzantine Fault Tolerance ◽

Advantages And Disadvantages ◽

Byzantine Fault ◽

Credit Mechanism ◽

Low Efficiency ◽

System Nodes

Alliance chain is a typical multicenter block chain and is easily implemented, so it is supported by more and more enterprises and governments. This paper analyzes the advantages and disadvantages of the Practical Byzantine Fault Tolerance (PBFT) in the alliance chain application scene. Aiming at the low efficiency of multinode consensus of the PBFT algorithm, the C-Raft-PBFT consensus algorithm is proposed. By integrating the Raft algorithm and the PBFT algorithm with the credit mechanism, designing node credit evaluation and grading protocols, and increasing Byzantine node detection based on feedback mechanism and other methods, the system efficiency is improved. The experiment results show that the improved algorithm has better throughput and lower delay, and the system’s fault tolerance is also improved. Among them, the delay is reduced by 1.93 seconds on average; in the case of an increase in system nodes, the number of nodes in the experimental data is between 200 and 225, and the throughput is increased by 6.46% on average.

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