EpicChain is an innovative project focusing on Optimized Delegated Byzantine Fault Tolerance (O-DBFT) aimed at enhancing consensus mechanisms within the Neo ecosystem. Currently in its early stages, this project lays the groundwork for developing advanced optimization strategies tailored to improve the Neo Consensus framework.
EpicChain is designed to explore and implement optimization strategies that enhance the efficiency and robustness of consensus protocols. Leveraging the flexibility of metaheuristics and mathematical programming models, EpicChain aims to address the complexities of trust and efficiency in decentralized systems.
- Optimization Strategies: Investigates and implements metaheuristics and mathematical programming models to improve consensus protocols.
- Multi-Objective Optimization: Seeks to balance risk, quality, and communication speed between Neo ecosystem interfaces, aiming for an optimal Pareto Front.
- Real-Time Decision Making: Incorporates multi-criteria decision-making techniques to assist nodes in making informed decisions during blockchain operations.
- High-Performance Computing: Utilizes advanced computing techniques to enhance consensus speed and system robustness.
To get started with EpicChain, follow these steps:
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Clone the Repository:
git clone https://github.com/your-repo/EpicChain.git
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Install Dependencies: Follow the installation guide to set up the necessary dependencies.
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Explore Documentation: Review the docs for detailed information and instructions. Check out the quick start tutorial for an introductory guide.
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Run Experiments: EpicChain builds on neo-integration-test, which will serve as the base project for running experiments.
- Graph Mathematical Programming: Potentially involves solving complex graph mathematical programming models.
- NP-Hard Problems: Anticipates dealing with NP-Hard problems, which may require significant computational resources for real-time operations.
- Integration with Neo-Integration-Test: Possible future integration with neo-integration-test for real-time self-tuning parameters and enhanced testing.
We welcome contributions and ideas from the community. If you have suggestions, encounter issues, or wish to get involved, please:
- Report Issues: Submit issues or feature requests directly on our GitHub Issues page.
- Contact Us: Reach out directly if you prefer to discuss your ideas or contributions.
Our team consists primarily of researchers and professors with limited availability, but we appreciate any assistance and contributions.
This project is licensed under the MIT License. See the LICENSE file for details.
EpicChain Team
Part of the NeoResearch Team
Original authors: @xmoohad
Copyleft 2021-2024