Code for our CNSM'2022 paper "A Novel Network Delay Prediction Model with Mixed Multi-layer Perceptron Architecture for Edge Computing"
If you find this code useful in your research please cite
@INPROCEEDINGS{fang2022a,
author={Fang, Honglin and Yu, Peng and Wang, Ying and Li, Wenjing and Zhou, Fanqin and Ma, Run},
booktitle={2022 18th International Conference on Network and Service Management (CNSM)},
title={A Novel Network Delay Prediction Model with Mixed Multi-layer Perceptron Architecture for Edge Computing},
year={2022},
volume={},
number={},
pages={191-197},
doi={10.23919/CNSM55787.2022.9964552}
}
The main environment is (latest is also available):
- cuda 11.3
- torch 1.8.0
- networkx 2.6.3
- einops 0.4.0
NSFNET and GEANT2 datasets are publicly available here
cd dataset
# For NSFNET
wget "http://knowledgedefinednetworking.org/data/datasets_v0/nsfnet.tar.gz"
tar -xvzf nsfnet.tar.gz
# For GEANT2
wget "http://knowledgedefinednetworking.org/data/datasets_v0/geant2.tar.gz"
tar -xvzf geant2.tar.gz
- Train with data process(first time)
# for NSFNET
python run.py --net nsfnetbw
# for GEANT2
python run.py --net geant2bw
- Train
# for NSFNET
python run.py --net nsfnetbw --process False
# for GEANT2
python run.py --net geant2bw --process False
Also other model parameters and training hyper-parameters can be changed by adding argparse like --lr 3e-4 --dim 64
.