Building a Graph-based Deep Learning network model from captured traffic traces

TL;DR

This paper introduces a graph neural network model trained on real captured traffic traces to better represent complex network behaviors, overcoming limitations of traditional simulation-based models.

Contribution

The paper presents a novel GNN encoding and message passing method tailored for real network traffic, enabling improved generalization to unseen scenarios.

Findings

Effective learning from real traffic data

Enhanced generalization to new network scenarios

Improved modeling of network dependencies

Abstract

Currently the state of the art network models are based or depend on Discrete Event Simulation (DES). While DES is highly accurate, it is also computationally costly and cumbersome to parallelize, making it unpractical to simulate high performance networks. Additionally, simulated scenarios fail to capture all of the complexities present in real network scenarios. While there exists network models based on Machine Learning (ML) techniques to minimize these issues, these models are also trained with simulated data and hence vulnerable to the same pitfalls. Consequently, the Graph Neural Networking Challenge 2023 introduces a dataset of captured traffic traces that can be used to build a ML-based network model without these limitations. In this paper we propose a Graph Neural Network (GNN)-based solution specifically designed to better capture the complexities of real network scenarios. This is done through a novel encoding method to capture information from the sequence of captured packets, and an improved message passing algorithm to better represent the dependencies present in physical networks. We show that the proposed solution it is able to learn and generalize to unseen captured network scenarios.