PyTorch implementation of our paper "DRL-SFCP: Adaptive Service Function Chains Placement with Deep Reinforcement Learning" which is accepted by ICC 2021.
Note: This algorithm has been integrated into Virne, a NFV simulator, where you can find more details.
# only cpu
bash install.sh -c 0
# use cuda (optional version: 10.2, 11.3)
bash install.sh -c 11.3python main.py --solver_name=$SOLVER_NAMEHere, you can choose SOLVER_NAME from a3c_gcn_seq2seq, grc_rank, mcts, etc. And you can find more detailed usage in main.py and config.py.
Please refer to settings/p_net_setting.yaml and settings/v_sim_setting.yaml for more details.
If you find this code useful in your research, please consider citing:
@INPROCEEDINGS{tfw-icc-2021-drl-sfcp,
author={Wang, Tianfu and Fan, Qilin and Li, Xiuhua and Zhang, Xu and Xiong, Qingyu and Fu, Shu and Gao, Min},
booktitle={ICC 2021 - IEEE International Conference on Communications},
title={DRL-SFCP: Adaptive Service Function Chains Placement with Deep Reinforcement Learning},
year={2021},
volume={},
number={},
pages={1-6},
doi={10.1109/ICC42927.2021.9500964}
}