PlatON Tips #16 | PlatON Consensus Solution-1

PlatON Tips #16 | PlatON Consensus Solution-1

PlatON Tips #16 | PlatON Consensus Solution-1

PlatON Tips #16 | PlatON Consensus Solution-1

PlatON Tips #16 | PlatON Consensus Solution-1

PlatON Tips #16 | PlatON Consensus Solution-1

Welcome to PlatON Tips-Episode 16

We are going to talk about the PlatON Consensus Solution

We propose a concurrent Byzantine fault tolerance protocol (CBFT) to solve the efficiency problem of partially synchronous networks. This article analyzes consensus protocols such as PBFT, Tendermint, and Hotstuff. With reference to their best practices, CBFT designed pipeline validation, batch production, and low-complexity view-change to improve consensus efficiency.

Distributed network model

According to the distributed system theory, the network model of the distributed system is divided into three categories:

  • Synchronous network model: messages sent by nodes will definitely reach the target node within a certain time
  • Asynchronous network model: messages sent by nodes cannot be sure to reach the target node
  • Partial synchronous network model: messages sent by nodes, although delayed, will eventually reach the target node

The synchronous network model is a very ideal situation, and the asynchronous network model is closer to the actual model, but according to the principle of FLP Impossibility , under the assumption of the asynchronous network model, the consensus algorithm cannot simultaneously satisfy the security safety and liveness, most current BFT consensus algorithms are based on the assumption of a partially synchronous network model. We also discuss based on the assumption of a partially synchronous network model.

BFT consensus protocol

A distributed system is composed of multiple nodes, and the nodes need to send messages to communicate with each other. According to the protocol they follow, consensus is reached on a certain task message and is executed consistently. There are many types of errors in this process, but they can basically be divided into two categories.

  • The first type of error is node crash, network failure, packet loss, etc. This type of error node is not malicious and belongs to non-Byzantine error.
  • The second type of error is that the node may be malicious and does not follow the protocol rules. For example, the validator can delay or reject messages in the network, the proposer can propose invalid blocks, or the node can send different messages to different peers. In the worst case, malicious nodes may cooperate with each other. These are called Byzantine errors.

Considering these two errors, we hope that the system can always maintain two attributes: safety and liveness.

  • Security: When the above two types of errors occur, the consensus system cannot produce wrong results. In the semantics of blockchain, it means that there will be no double-spending and forks.
  • Liveness: The system has been able to continuously generate submissions. Under the semantics of the blockchain, it means that consensus will continue and will not get stuck. If the consensus of a blockchain system is stuck at a certain height, then the new transaction is not responding, that is, it does not meet the liveness.

Byzantine Fault Tolerance Protocol (BFT) is a protocol that can guarantee the security and activity of distributed systems even if malicious nodes exist in the system. According to the classic paper of Lampport , all BFT protocols have a basic assumption: when the total number of nodes is greater than 3f, the maximum number of malicious nodes is f, and honest nodes can reach a correct result that is consistent.

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