Hybrid Consensus Model for Blockchain Networks: Integrating Raft and PBFT for Enhanced Scalability and Performance

Zeqi Wang

2024

Abstract

This research investigates the integration of Reliable, Replicated, Redundant, And Fault-Tolerant (Raft) algorithm and Practical Byzantine Fault Tolerance (PBFT) consensus mechanisms to address scalability and performance limitations in blockchain networks, with a focus on financial applications. The objective is to develop a hybrid consensus model that combines Raft’s efficient and rapid consensus process with PBFT’s robust fault tolerance. The proposed model leverages Raft's decentralized voting procedure to improve system responsiveness while utilizing PBFT to ensure secure and accurate data consensus in environments prone to Byzantine faults. Extensive simulations were conducted to evaluate the hybrid model's performance. The results demonstrate that integrating Raft and PBFT not only preserves high throughput and reliability but also significantly enhances scalability, making the model particularly suitable for complex and security-sensitive applications, such as payment systems and digital currencies. This research highlights the practical benefits of hybrid consensus models in overcoming the inherent challenges of deploying blockchain technology in critical financial services. It suggests future research directions for broader applications across various sectors that require robust, scalable, and secure blockchain solutions.

Paper Citation

in Harvard Style

Wang Z. (2024). Hybrid Consensus Model for Blockchain Networks: Integrating Raft and PBFT for Enhanced Scalability and Performance. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 365-368. DOI: 10.5220/0013517800004619

in Bibtex Style

@conference{daml24,
author={Zeqi Wang},
title={Hybrid Consensus Model for Blockchain Networks: Integrating Raft and PBFT for Enhanced Scalability and Performance},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={365-368},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013517800004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Hybrid Consensus Model for Blockchain Networks: Integrating Raft and PBFT for Enhanced Scalability and Performance
SN - 978-989-758-754-2
AU - Wang Z.
PY - 2024
SP - 365
EP - 368
DO - 10.5220/0013517800004619
PB - SciTePress