Network models are an essential block of modern networks. For example, they are widely used in network planning and optimization. However, as networks increase in scale and complexity, some models present limitations, such as the assumption of Markovian traffic in queuing theory models, or the high computational cost of network simulators. Recent advances in machine learning, such as Graph Neural Networks (GNN), are enabling a new generation of network models that are data-driven and can learn complex non-linear behaviors. In this paper, we present RouteNet-Fermi, a custom GNN model that shares the same goals as Queuing Theory, while being considerably more accurate in the presence of realistic traffic models. The proposed model predicts accurately the delay, jitter, and packet loss of a network. We have tested RouteNet-Fermi in networks of increasing size (up to 300 nodes), including samples with mixed traffic profiles -- e.g., with complex non-Markovian models -- and arbitrary routing and queue scheduling configurations. Our experimental results show that RouteNet-Fermi achieves similar accuracy as computationally-expensive packet-level simulators and scales accurately to larger networks. Our model produces delay estimates with a mean relative error of 6.24% when applied to a test dataset of 1,000 samples, including network topologies one order of magnitude larger than those seen during training. Finally, we have also evaluated RouteNet-Fermi with measurements from a physical testbed and packet traces from a real-life network.

翻译：网络模型是现代网络的重要组成部分，例如在网络规划与优化中被广泛使用。然而，随着网络规模和复杂性的增加，部分模型存在局限性，如排队论模型中的马尔可夫流量假设，或网络模拟器的高计算成本。机器学习的最新进展（如图神经网络）正催生新一代数据驱动的网络模型，能够学习复杂的非线性行为。本文提出RouteNet-Fermi，一种定制化的GNN模型，其目标与排队论一致，但在处理现实流量模型时显著提升准确性。该模型可精确预测网络中的延迟、抖动和丢包率。我们在规模递增（最多300个节点）的网络中测试了RouteNet-Fermi，涵盖混合流量模式（例如复杂的非马尔可夫模型）及任意路由与队列调度配置。实验结果表明，RouteNet-Fermi在保持与计算密集型数据包级模拟器相近精度的同时，能准确扩展到更大规模网络。在包含1000个样本的测试数据集（其中网络拓扑尺寸比训练数据大一个数量级）上，模型对延迟的预测平均相对误差为6.24%。此外，我们还利用物理测试平台的测量数据和真实网络的报文跟踪记录对RouteNet-Fermi进行了评估。