Agent-Based System for Multi-Access Edge Computing
This project implements an agent-based system designed to optimize resource allocation in Multi-Access Edge Computing (MEC) environments. The system enables offloading of computational tasks from mobile devices to edge servers, taking into consideration factors such as battery level, bandwidth, and task size. The primary goal is to enhance the efficiency of resource usage in distributed edge computing scenarios.
- Task Offloading: Dynamically offloads tasks from mobile devices to edge servers based on predefined criteria.
- Resource Optimization: Considers various parameters such as battery level, bandwidth, and task size to optimize resource allocation.
- Agent-Based Architecture: Utilizes agents to manage and coordinate task offloading, ensuring efficient use of MEC resources.
- Scalability: Designed to handle multiple agents and devices, making it suitable for large-scale MEC deployments.
- Simulation Environment: Includes a simulation setup to test and validate the agent-based system under different conditions.
- Python 3.x
- Required Python packages (listed in
requirements.txt
) - A compatible simulation environment (e.g., SimPy)
-
Clone the repository:
git clone https://github.com/yourusername/MEC-Agent-System.git
-
Navigate to the project directory:
cd MEC-Agent-System
- Install the required Python packages:
pip install -r requirements.txt
- Run the simulation:
python test.py
- Use the command line interface to start the simulation and monitor the results.
- The simulation output will display the performance metrics, including resource utilization, task completion times, and agent decisions.
Contributions to this project are welcome! If you have any ideas, suggestions, or bug reports, please open an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE
file for more details.
For any questions or further information, please contact me at laghaahmedfouad@gmail.com