This is a Pytorch implementation of DRL-OR-S on TPDS 2023.
To run DRL-OR-S
- Firstly you should install mininet
git clone git://github.com/mininet/mininet
cd mininet
util/install.sh -a
- Then install ryu-manager and other required packages
pip3 install ryu
pip3 install -r requirements.txt
We have uploaded the training and testing data for NN-simulator.
To training DRL-OR-S offline
- Run NN-simulator
cd NN-simulator
python3 main.py --mode simulator --log-dir final-loss-100queuesize --use-cuda --topo Abi --simu-port 5010
- Run DRL-OR-S algorithm
python3 main.py --mode train --use-gae --num-mini-batch 1 --use-linear-lr-decay --num-env-steps 3000000 --env-name Abi --log-dir ./log/test --model-save-path ./model/test --model-load-path ./model/Abi-heavyload-gcn-sharepolicy-SPRsafe-mininet-2penalty-test-Abi-dynamic-extra2 --num-pretrain-epochs 0 --simu-port 5010
To run DRL-OR code as an example
- Run testbed
cd testbed
sudo ./run.sh
- Run ryu controller
cd ryu-controller
./run.sh
- Run DRL-OR-S algorithm
cd drl-or-s
./run.sh
If you have any questions, please post an issue or send an email to [email protected].
@article{liu2023scalable,
title={Scalable Deep Reinforcement Learning-Based Online Routing for Multi-Type Service Requirements},
author={Liu, Chenyi and Wu, Pingfei and Xu, Mingwei and Yang, Yuan and Geng, Nan},
journal={IEEE Transactions on Parallel and Distributed Systems},
year={2023},
publisher={IEEE}
}