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.

翻译：摘要：存储网络流量数据对于高效的网络管理至关重要；然而，由于日益增长的数据传输速率、流量规模和连接设备数量，这一任务正变得愈发具有挑战性和成本高昂。本文探讨了利用神经架构进行网络流量压缩的方法。具体而言，我们考虑一个在网络拓扑中具有多个测量点的网络场景。这些测量数据可被视为多个时间序列，它们呈现出由网络拓扑、路由或用户行为引发的空间和时间相关性。我们提出了Atom，一种利用网络流量中空间与时间相关性的神经流量压缩方法。Atom采用定制的时空图神经网络设计，能够同时有效利用这两种相关性。实验结果表明，在三个真实世界网络上，Atom的压缩比相比GZIP提升了50%-65%。