Link to paper: [here]
Contact: paulalmasan@gmail.com
Storing network traffic data is key to efficient network management; however, it is becoming more challenging and costly due to the ever-increasing data transmission rates, traffic volumes, and connected devices. In this paper, we explore the use of neural architectures for network traffic compression. Specifically, we consider a network scenario with multiple measurement points in a network topology. Such measurements can be interpreted as multiple time series that exhibit spatial and temporal correlations induced by network topology, routing, or user behavior. We present Atom, a neural traffic compression method that leverages spatial and temporal correlations present in network traffic. Atom implements a customized spatio-temporal graph neural network design that effectively exploits both types of correlations simultaneously. The experimental results show that Atom can outperform GZIP's compression ratios by 50%--65% on three real-world networks.
See the execution instructions
To know more details about the implementation used in the experiments contact: paulalmasan@gmail.com
Please cite the corresponding article if you use the code from this repository:
@inproceedings{10.1145/3630049.3630170,
author = {Almasan, Paul and Rusek, Krzysztof and Xiao, Shihan and Shi, Xiang and Cheng, Xiangle and Cabellos-Aparicio, Albert and Barlet-Ros, Pere},
title = {Atom: Neural Traffic Compression with Spatio-Temporal Graph Neural Networks},
year = {2023},
isbn = {9798400704482},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3630049.3630170},
doi = {10.1145/3630049.3630170},
abstract = {Storing network traffic data is key to efficient network management; however, it is becoming more challenging and costly due to the ever-increasing data transmission rates, traffic volumes, and connected devices. In this paper, we explore the use of neural architectures for network traffic compression. Specifically, we consider a network scenario with multiple measurement points in a network topology. Such measurements can be interpreted as multiple time series that exhibit spatial and temporal correlations induced by network topology, routing, or user behavior. We present Atom, a neural traffic compression method that leverages spatial and temporal correlations present in network traffic. Atom implements a customized spatio-temporal graph neural network design that effectively exploits both types of correlations simultaneously. The experimental results show that Atom can outperform GZIP's compression ratios by 50\%--65\% on three real-world networks.},
booktitle = {Proceedings of the 2nd on Graph Neural Networking Workshop 2023},
pages = {1–6},
numpages = {6},
keywords = {neural traffic compression, spatio-temporal graph neural networks},
location = {<conf-loc>, <city>Paris</city>, <country>France</country>, </conf-loc>},
series = {GNNet '23}
}