Skip to content

Latest commit

 

History

History
52 lines (48 loc) · 2.08 KB

README.md

File metadata and controls

52 lines (48 loc) · 2.08 KB

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

  1. Download source code from GitHub
     git clone https://github.com/saeid93/edge_consolidation_paper.git
    
  2. Create conda virtual-environment
     conda create --name EdgeSim python=3
    
  3. Activate conda environment
     source activate EdgeSim
    
  4. Install requirements
     pip install -r requirements.txt
    

Description

  1. 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

Setting up the simulator

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

Running the experiments

  1. First go to the constants.py and add the address of data, model and results to the Python file
  2. 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.
  3. 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.
  4. 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.