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262-cfr-rl-traffic-engineering-with-reinforcement-learning-in-sdn

https://github.com/szu-advtech-2024/262-cfr-rl-traffic-engineering-with-reinforcement-learning-in-sdn

Science Score: 54.0%

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Citation

https://github.com/SZU-AdvTech-2024/262-CFR-RL-Traffic-Engineering-With-Reinforcement-Learning-in-SDN/blob/main/ 

# CFR-RL: Traffic Engineering with Reinforcement Learning in SDN

This is a Tensorflow implementation of CFR-RL as described in our paper:

Junjie Zhang, Minghao Ye, Zehua Guo, Chen-Yu Yen, H. Jonathan Chao, "[CFR-RL: Traffic Engineering With Reinforcement Learning in SDN](https://arxiv.org/abs/2004.11986)," in IEEE Journal on Selected Areas in Communications, vol. 38, no. 10, pp. 2249-2259, Oct. 2020, doi: 10.1109/JSAC.2020.3000371.

# Prerequisites

- Install prerequisites (test with Ubuntu 20.04, Python 3.8.5, Tensorflow v2.2.0, PuLP 2.3, networkx 2.5, tqdm 4.51.0)
```
python3 setup.py
```

# Training

- To train a policy for a topology, put the topology file (e.g., Abilene) and the traffic matrix file (e.g., AbileneTM) in `data/`, then specify the file name in config.py, i.e., topology_file = 'Abilene' and traffic_file = 'TM', and then run 
```
python3 train.py
```
- Please refer to `data/Abilene` for more details about topology file format. 
- In a traffic matrix file, each line belongs to a N*N traffic matrix, where N is the node number of a topology.
- Please refer to `config.py` for more details about configurations. 

# Testing

- To test the trained policy on a set of test traffic matrices, put the test traffic matrix file (e.g., AbileneTM2) in `data/`, then specify the file name in config.py, i.e., test_traffic_file = 'TM2', and then run 
```
python3 test.py
```

# Reference

Please cite our paper if you find our paper/code is useful for your work.

@ARTICLE{jzhang,
  author={J. {Zhang} and M. {Ye} and Z. {Guo} and C. -Y. {Yen} and H. J. {Chao}},
  journal={IEEE Journal on Selected Areas in Communications}, 
  title={CFR-RL: Traffic Engineering With Reinforcement Learning in SDN}, 
  year={2020},
  volume={38},
  number={10},
  pages={2249-2259},
  doi={10.1109/JSAC.2020.3000371}}

Owner

  • Name: SZU-AdvTech-2024
  • Login: SZU-AdvTech-2024
  • Kind: organization

Citation (citation.txt) 

@article{REPO262,
    author = "Zhang, Junjie and Ye, Minghao and Guo, Zehua and Yen, Chen-Yu and Chao, H. Jonathan",
    journal = "IEEE Journal on Selected Areas in Communications",
    number = "10",
    pages = "2249-2259",
    title = "{CFR-RL: Traffic Engineering With Reinforcement Learning in SDN}",
    volume = "38",
    year = "2020"
}

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