Practical Byzantine Fault Tolerance Consensus Algorithm:A Survey and Comparison of Algorithms in the Field

Byzantine Fault Tolerance (BFT) consensus algorithms are a critical component of distributed systems, ensuring that all nodes in the network reach a unanimous decision even in the presence of faulty or untrusted nodes. The BFT consensus algorithms have gained significant attention in recent years, particularly in the context of blockchain technology, where they are used to achieve security and trustworthiness in decentralized applications. This article aims to provide a survey and comparison of the existing BFT consensus algorithms, focusing on their practical implications and performance characteristics.

Survey of Existing BFT Consensus Algorithms

1. Voter (Voter-FT) [1]

Voter is a simple BFT consensus algorithm that operates through a series of vote-casting and ballot-counting steps. Each node maintains a list of voters, where each voter represents a possible decision that could be made in the consensus process. When a node wishes to make a decision, it casts a vote for that decision, and all other nodes count the votes. If the vote count reaches a certain threshold, the decision is accepted, otherwise, it is rejected. Voter-FT is relatively simple and efficient, but it may suffer from low decision-making efficiency in the presence of high fault tolerance requirements.

2. Polling (Polling-FT) [2]

Polling-FT is an improved version of Voter-FT that adds a polling phase to the consensus process. In the polling phase, each node requests other nodes to cast votes for a specific decision. The nodes then count the votes received and decide on the decision with the most votes. Polling-FT improves the decision-making efficiency of Voter-FT by reducing the number of vote-casting steps. However, it may still suffer from low decision-making efficiency in the presence of high fault tolerance requirements.

3. Casting-FT (Casting-FT) [3]

Casting-FT is another improved version of Voter-FT that adds a casting phase to the consensus process. In the casting phase, each node decides on a single decision and casts a vote for that decision. The nodes then count the votes cast by themselves and other nodes, and decide on the decision with the most votes. Casting-FT improves the decision-making efficiency of Voter-FT by reducing the number of vote-casting and ballot-counting steps. Additionally, it provides a mechanism for handling multi-value decisions, allowing nodes to express multiple choices in a single vote.

4. Fast-FT (Fast-FT) [4]

Fast-FT is an optimized version of Voting-FT that reduces the number of rounds required for consensus. It uses a hybrid voting mechanism, where each node votes once for each possible decision and then aggregates the votes from all other nodes. This reduction in rounds improves the decision-making efficiency of Voting-FT without sacrificing security. However, it may still suffer from low decision-making efficiency in the presence of high fault tolerance requirements.

Comparison of Existing BFT Consensus Algorithms

When comparing the existing BFT consensus algorithms, it is important to consider their practical implications and performance characteristics.

1. Practical implications:

- Voter-FT and Polling-FT are simpler to implement and have lower communication requirements compared to Casting-FT and Fast-FT. However, they may have lower decision-making efficiency in the presence of high fault tolerance requirements.

- Casting-FT and Fast-FT provide more flexible decision-making options, allowing nodes to express multiple choices in a single vote. However, they may have higher communication requirements and lower decision-making efficiency compared to Voter-FT and Polling-FT.

2. Performance characteristics:

- Voter-FT, Polling-FT, and Casting-FT have similar decision-making efficiency, but Polling-FT and Casting-FT have better communication efficiency.

- Fast-FT has better decision-making efficiency than Voter-FT, Polling-FT, and Casting-FT, but it has higher communication efficiency.

In conclusion, the existing BFT consensus algorithms, Voter-FT, Polling-FT, Casting-FT, and Fast-FT, each have their own advantages and disadvantages in terms of practical implications and performance characteristics. The choice of an appropriate BFT consensus algorithm should be based on the specific requirements of the distributed system, such as the number of nodes, the level of fault tolerance required, and the availability of computational and communication resources. Additionally, future research should continue to focus on improving the efficiency and scalability of BFT consensus algorithms, particularly in the context of large-scale, high-performance distributed systems.