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.

翻译：当前最先进的网络模型基于或依赖于离散事件仿真（DES）。尽管DES具有高精度，但其计算成本高昂且难以并行化，使其不适用于高性能网络的模拟。此外，仿真场景无法完全捕捉真实网络场景中存在的复杂性。尽管存在基于机器学习（ML）技术构建的网络模型来缓解这些问题，但这些模型仍使用仿真数据进行训练，因而面临同样的局限性。为此，图表神经网络挑战赛2023引入了一个由捕获流量轨迹组成的数据集，可用于构建无此类局限性的ML网络模型。本文提出了一种专门设计的图神经网络（GNN）解决方案，以更好捕捉真实网络场景的复杂性。该方案通过一种新颖的编码方法从捕获的数据包序列中提取信息，并采用改进的消息传递算法更准确地表示物理网络中存在的依赖关系。实验表明，所提出的方案能够学习并泛化至未见过的捕获网络场景。