The code requires python3 (>=3.5) with the development headers. You'll also need system packages CMake, OpenMPI, graphviz and zlib. Those can be installed as follows
sudo apt-get update && sudo apt-get install cmake libopenmpi-dev python3-dev zlib1g-dev
sudo apt-get install graphvizIt is better to use a virtual environment (e.g., Anaconda) to run the code. About how to install anaconda, please refer to the official website: https://www.anaconda.com
Once you have anaconda installed, run
conda env create -f environment.yaml
To create the virtual environment. The current version of the code only supports TensorFlow 1.x (>=1.5).
We implemented two DRL-based algorithms for task offloading: DRLTO and DDQNTO.
To train and evaluate DRLTO under different scenarios, run
# train and evaluate DRLTO with different number of tasks and LO target. python train.py --algo DRLTO --scenario Number --goal LO --dependency True # train and evaluate DRLTO with different number of tasks and EE target. python train.py --algo DRLTO --scenario Number --goal EE --dependency True # train and evaluate DRLTO with different transmission rate and LO target. python train.py --algo DRLTO --scenario Trans --goal LO --dependency True # train and evaluate DRLTO with different transmission rate and EE target. python train.py --algo DRLTO --scenario Trans --goal EE --dependency True
To train DRLTO without considering task dependency, run
# train and evaluate DRLTO with different number of tasks and LO target without considering dependency
python train.py --algo DRLTO --scenario Number --goal LO --dependency False To train and evaluate DDQNTO under different scenarios, run
# train and evaluate DDQNTO with different number of tasks and LO target. In DDQNTO we do not consider the dependency. python train.py --algo DDQNTO --scenario Number --goal LO --dependency False # train and evaluate DDQNTO with different number of tasks and EE target. python train.py --algo DDQNTO --scenario Number --goal EE --dependency False # train and evaluate DDQNTO with different transmission rate and LO target. python train.py --algo DDQNTO --scenario Trans --goal LO --dependency False # train and evaluate DDQNTO with different transmission rate and EE target. python train.py --algo DDQNTO --scenario Trans --goal EE --dependency False
The running results can be found in the log folder (default path of log folder is './log/Result')
To evaluate the heuristic algorithms, run
python evaluate_heuristic_algo.py --scenario Number --goal LO python evaluate_heuristic_algo.py --scenario Number --goal EE python evaluate_heuristic_algo.py --scenario Trans --goal LO python evaluate_heuristic_algo.py --scenario Trans --goal EE
If you are interested in this work, please cite the paper
@article{Wang2021Depedent,
author={Wang, Jin and Hu, Jia and Min, Geyong and Zhan, Wenhan and Zomaya, Albert and Georgalas, Nektarios},
journal={IEEE Transactions on Computers},
title={Dependent Task Offloading for Edge Computing based on Deep Reinforcement Learning},
year={2021},
doi={10.1109/TC.2021.3131040}}