https://github.com/louis-alexandre-laguet/multi-objective-task-allocation-fog-cloud
This project optimizes task allocation in Fog and Cloud Computing environments using multi-objective optimization techniques. It computes and analyzes Pareto fronts using MOCS and MOFA algorithms. The project includes Jupyter notebooks for data preparation, Pareto front calculation, and solution analysis.
https://github.com/louis-alexandre-laguet/multi-objective-task-allocation-fog-cloud
cloud-computing firefly-algorithm fog-computing jupyter-notebooks machine-learning mocs mofa multi-objective-optimization pareto-fronts python swarm-intelligence task-allocation
Last synced: 2 months ago
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This project optimizes task allocation in Fog and Cloud Computing environments using multi-objective optimization techniques. It computes and analyzes Pareto fronts using MOCS and MOFA algorithms. The project includes Jupyter notebooks for data preparation, Pareto front calculation, and solution analysis.
- Host: GitHub
- URL: https://github.com/louis-alexandre-laguet/multi-objective-task-allocation-fog-cloud
- Owner: louis-alexandre-laguet
- Created: 2025-03-22T10:42:23.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-03-22T10:43:26.000Z (2 months ago)
- Last Synced: 2025-03-22T11:33:06.299Z (2 months ago)
- Topics: cloud-computing, firefly-algorithm, fog-computing, jupyter-notebooks, machine-learning, mocs, mofa, multi-objective-optimization, pareto-fronts, python, swarm-intelligence, task-allocation
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Machine Learning Project - Task Allocation in Fog and Cloud Computing
This project was carried out as part of the **Multi-objective Optimization** course, under the supervision of **Sonia Yassa**.
## Project Description
The objective of this project is to optimize task allocation in a **Fog Computing** and **Cloud Computing** environment using multi-objective optimization approaches.
## Prerequisites
- **Python** installed on your machine.
- Use an editor like **VS Code** to run the notebooks.
## Notebook Organization
The notebooks should be executed in the following order:
1. **`01_data_preparation.ipynb`**
→ Generates two CSV files containing tasks and virtual machines (**Fog** and **Cloud**).
2. **`02_pareto_fronts.ipynb`**
→ Computes the **Pareto fronts** using two methods: **MOCS** and **MOFA**.
3. **`03_pareto_analysis.ipynb`**
→ Analyzes the obtained Pareto fronts to compare the performance of the approaches.
4. **`04_solution_analysis.ipynb`**
→ Studies the distribution of solutions between **Fog** and **Cloud**.
## Execution
Open and execute the notebooks in **VS Code**, following the indicated order.