Consolidation of Services in Mobile Edge Clouds using a Learning-based Framework

Source code of "Consolidation of Services in Mobile Edge Clouds using a Learning-based Framework" paper link: https://ieeexplore.ieee.org/abstract/document/9284153

Setup the Python environment

Download source code from GitHub

 git clone https://github.com/saeid93/edge_consolidation_paper.git

The code is separated into two modules inside the /code folder
1. /code/src/edge_simulator: contains the source code of the environemnt that is build on top of gym library
2. /code/experiments_scripts: contains the set of codes to replicate the results of the paper and do experiments with the environments

go to the /code/src/edge_simulator and install the library in the editable mode with
```
pip install -e .
```

First go to the constants.py and add the address of data, model and results to the Python file
Go to the code/experiments_scripts, the generate_initial_states.py can genearte a dataset of user specified criteria
1. Specify the dataset specifications in config_generate_dataset.json
2. Use the generate_initial_states.py from the command line to generate the dataset
```
python generate_initial_states.py config_generate_dataset.json
```
the results will be saved in the data folder.
Specify your desired config in one of the config_v(0-3).json based on the environment you want to run the experiments
```
python runner.py config_v0.json
```
the results will be saved in the models folder.
In order to plot the results of several experiments in a single plot go to the config_aggregate.json
```
python runner.py config_aggregate.json
```
the results will be saved in the results folder.