Description
BiniBFT Implementation aims to make the BiniBFT consensus designed earlier into a fully functional plugin for Fabric. The project's objective is to enhance the security, scalability, and throughput of BFT consensus for enterprise blockchain, mainly focusing on Hyperledger Fabric.
Problem/Opportunity: Blockchain technology has a wide range of day-to-day applications ranging from electronic voting to supply chain management to financial transactions to healthcare management, energy management etc in a distributed and a decentralized environment. The enterprise blockchain provides transparency and immutability in a permissioned setting. The consensus protocols play a crucial role in establishing trustworthiness within these systems, facilitating secure and reliable operations across various sectors. The seamless integration of Byzantine Fault Tolerance (BFT) further fortifies the trustworthiness of enterprise blockchain. To achieve high security and scalability this project implements the BiniBFT consensus for Hyperledger Fabric, which was successfully innovated and designed after careful research through the CLP 2023 project, BiniBFT - An Optimized BFT on Fabric. This mentorship project highlights the pivotal role of blockchain and BiniBFT in revolutionizing the reliability and security of day-to-day operations across industries, offering a glimpse into the transformative potential of decentralized technologies in shaping the future of sustainable digital ecosystems.
Implementation Path: The project will start directly with the implementation of BiniBFT consensus. Both the versions of BiniBFT namely BiniBFT with Random Polling, and The Time-Weighted BiniBFT Consensus protocol will be implemented. The documentation phase will happen in overlapping with the implementation to have better clarity of the project for the users.
Learning Objectives
Technical Proficiency:
- Gain hands-on experience with Hyperledger Fabric technology, including understanding its architecture, components, and configuration parameters.
- Gain experience on developing BFT plugin for Fabric using the GO programming.
Problem-Solving and Iterative Development:
- Learn how to address complex problems in software development through iterative design and implementation cycles, driven by user feedback and testing.
- Enhance critical thinking and troubleshooting abilities by identifying and resolving issues encountered during the development process.
Open Source Collaboration:
- Gain exposure to the collaborative nature of open source projects by contributing to implementing BiniBFT, alongside other developers and community members.
- Learn best practices for communication, collaboration, and version control within an open source environment, fostering skills essential for future contributions to open source projects.
Documentation and Support:
- Learn the importance of clear and comprehensive documentation in open source projects by documenting development processes, codebase structure, and usage instructions for BiniBFT Implementation.
- Develop skills in providing user support and assistance, addressing user inquiries, issues, and feedback effectively to foster a positive user experience and project adoption.
Expected Outcome and Deliverables
The following are the expected outcomes from this project :
- Coding and development of the BiniBFT consensus library for Hyperledger Fabric.
- Documentation and demonstration of how BiniBFT protocols can be integrated with Fabric.
Relation to Hyperledger and Impact on the community
This mentorship project is related to Hyperledger Fabric. BiniBFT Implementation will create the BiniBFT consensus mechanism for Hyperledger Fabric. Two versions of BiniBFT namely, BiniBFT with Random Polling, and The Time-Weighted BiniBFT Consensus protocols will be made available as open source projects. The low latency, high security, throughput, and scalability of the BiniBFT can make it to be used as the consensus for day-to-day applications using enterprise blockchain.
Recommended Skills
- In-depth knowledge of Hyperledger Fabric
- GO programming language
- Documentation Skills
Additional Resources
- Hyperledger Collaborative Learning Spotlight: BiniBFT - An Optimized BFT on Fabric
- Anasuya Threse Innocent .A, Riddhi Ravindra Katarki, Ashna P S, Ajitesh Kumar Soni, Abhishek Ranjan, Sahilsher Singh, Siddhant Prateek Mahanayak, BiniBFT - A Secure, Scalable BFT for Hyperledger Fabric - Whitepaper on the design of BiniBFT consensus.
- Hyperledger Fabric v3.0.0-preview Release Notes - September 1, 2023
Mentor Name and Contact Info
Dr Anasuya Threse Innocent, Director, BiniWorld Innovations Private Limited
binibft@biniworld.com, A Anasuya Threse Innocent
Hyperledger Affiliation(s):
Co-Chair, Hyperledger Healthcare SIG
Lead, Hyperledger India Chapter Student Society Chapter