Year: 2023
Type: conference-paper
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.
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Awards: G1175115578, G148081399, G2064873908, G210041235, G2262748287, Network intelligence for aDAptive and sElf-Learning MObile Networks, G2601397997, G3005324705, G300979063, G34346518, G3480869486, G3681454997, G4042783231, G425668148, G434337789, G4865226722, G5080475149, G5195188938, G6879670291, G69051668, G7084143925
OpenAlex ID: W4388585441