cfr-rl's Introduction

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," 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}}

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