Awesome
<h1 align="center"><b>GAL-VNE</b></h1> <p align="center"> <a href="https://dl.acm.org/doi/10.1145/3580305.3599358"><img alt="Publication" src="https://img.shields.io/static/v1?label=Pub&message=KDD%2723&color=purple"></a> <a href="https://github.com/Thinklab-SJTU/GAL-VNE/pulls"><img src="https://img.shields.io/badge/PRs-Welcome-yellow" alt="PRs"></a> <a href="https://github.com/Thinklab-SJTU/GAL-VNE/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/github/license/Thinklab-SJTU/GAL-VNE?color=green"></a> <a href="https://github.com/Thinklab-SJTU/GAL-VNE/stargazers"><img src="https://img.shields.io/github/stars/Thinklab-SJTU/GAL-VNE?color=red&label=Star" alt="Stars"></a> </p>Official Implementation of KDD 2023 paper:
"GAL-VNE: Solving the VNE Problem with Global Reinforcement Learning and Local One-Shot Neural Prediction"
VNE Problem
The NP-hard combinatorial Virtual Network Embedding (VNE) Problem refers to finding the node and edge mapping between a virtual network (VN) and the substrate network (SN).
GAL-VNE
We propose Global-And-Local VNE framework (GAL-VNE) by a pretrain-then-finetune training paradigm.
For more details, please see this project.
Requirements
easydict==1.10
gym==0.26.2
Jinja2==3.1.2
matplotlib==3.8.3
networkx==3.0
numpy==1.23.5
ortools==9.4.1874
pandas==2.2.2
PyYAML==6.0
scikit_learn==1.2.2
scipy==1.13.0
torch==1.12.1
torch_geometric==2.2.0
tqdm==4.64.1
Specify configurations
Customize "--pn_setting_path" and "--vns_setting_path".
The default setting is:
--pn_setting_path=settings/pn_setting/pn_setting.yaml --vns_setting_path=settings/vns_setting/vns_setting.yaml
| Parameters | SN | VN |
|---|---|---|
| Number of SN | 1 | 2000 |
| Number of Nodes | 100 | $2 \sim 10$ |
| Number of Links | 500 | $Random(0.5)$ |
| Node CPU (uniform dist.) | $50 \sim 100$ | $0 \sim 50$ |
| Bandwidth (uniform dist.) | $50 \sim 100$ | $0 \sim 50$ |
| Arrival Time (Possion dist.) | - | $Poisson(0.1)$ |
| Life Time (exponential dist.) | - | $Exp(500) $ |
The setting VN dataset during training is specified in "--vns_sub_setting_path".
Generate data
Run any solver with:
--generate_vn --generate_pn
Remind that generate the dataset once, and keep fixed afterwards. The default dataset is put in "dataset/" folder.
Reproduce the results
bash run.sh
Citation
If you find our work useful in your research, please consider citing:
@inproceedings{geng2023galvne,
author = {Geng, Haoyu and Wang, Runzhong and Wu, Fei and Yan, Junchi},
title = {GAL-VNE: Solving the VNE Problem with Global Reinforcement Learning and Local One-Shot Neural Prediction},
year = {2023},
isbn = {9798400701030},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3580305.3599358},
doi = {10.1145/3580305.3599358},
booktitle = {Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {531–543},
numpages = {13},
keywords = {combinatorial optimization, virtual network embedding, reinforcement learning},
location = {Long Beach, CA, USA},
series = {KDD '23}
}
Acknowledgement
We sincerely thank the repository virne for their well-implemented pipeline upon which we build our codebase.