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Atom: Neural Traffic Compression with Spatio-Temporal Graph Neural Networks
- Publication Year :
- 2023
-
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 \textit{Atom}, a neural traffic compression method that leverages spatial and temporal correlations present in network traffic. \textit{Atom} implements a customized spatio-temporal graph neural network design that effectively exploits both types of correlations simultaneously. The experimental results show that \textit{Atom} can outperform GZIP's compression ratios by 50\%-65\% on three real-world networks.<br />Comment: 7 pages, 6 figures, 2nd International Workshop on Graph Neural Networking (GNNet '23)
- Subjects :
- Computer Science - Networking and Internet Architecture
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2311.05337
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1145/3630049.3630170