# Performance Analysis of the Raft Consensus Algorithm for Private Blockchains

arXiv: https://arxiv.org/abs/1808.01081 · HTML: https://mlsanity.com/paper/1808.01081/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2018.

## Built on by (most cited first, 5 of 5)

- [Secure and Efficient Federated Learning Through Layering and Sharding Blockchain](https://mlsanity.com/paper/2104.13130/) (2021)
- [Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions](https://mlsanity.com/paper/2403.00873/) (2024)
- [LLM-Agent-UMF: LLM-based Agent Unified Modeling Framework for Seamless Design of Multi Active/Passive Core-Agent Architectures](https://mlsanity.com/paper/2409.11393/) (2024)
- [Performance Evaluation, Optimization and Dynamic Decision in Blockchain Systems: A Recent Overview](https://mlsanity.com/paper/2211.15907/) (2022)
- [BALLAST: Bandit-Assisted Learning for Latency-Aware Stable Timeouts in Raft](https://mlsanity.com/paper/2512.21165/) (2025)

## Abstract (arXiv)

Consensus is one of the key problems in blockchains. There are many articles analyzing the performance of threat models for blockchains. But the network stability seems lack of attention, which in fact affects the blockchain performance. This paper studies the performance of a well adopted consensus algorithm, Raft, in networks with non-negligible packet loss rate. In particular, we propose a simple but accurate analytical model to analyze the distributed network split probability. At a given time, we explicitly present the network split probability as a function of the network size, the packet loss rate, and the election timeout period. To validate our analysis, we implement a Raft simulator and the simulation results coincide with the analytical results. With the proposed model, one can predict the network split time and probability in theory and optimize the parameters in Raft consensus algorithm.