Atom: Neural Traffic Compression with Spatio-Temporal Graph Neural Networks

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 $\mathit{\text{Atom}}$, a neural traffic compression method that leverages spatial and temporal correlations present in network traffic. $\mathit{\text{Atom}}$ implements a customized spatio-temporal graph neural network design that effectively exploits both types of correlations simultaneously. The experimental results show that $\mathit{\text{Atom}}$ can outperform GZIP’s compression ratios by 50%-65% on three real-world networks.