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关键组成部分。例如，它们被广泛用于网络规划和优化。然而，随着网络规模和复杂性的增加，某些模型呈现出局限性，如排队论模型中对马尔可夫流量特征的假设，或网络模拟器的高计算成本。机器学习的最新进展，例如图神经网络（GNN），正在推动新一代数据驱动的网络模型发展，使其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，其目标与排队论相同，但在面对实际流量模型时具有显著更高的准确性。该模型能够精确预测网络的时延、抖动和丢包率。我们已在规模递增的网络中（最高达300个节点）测试了RouteNet-Fermi，包括具有混合流量剖面（例如复杂的非马尔可夫模型）以及任意路由和队列调度配置的样本。实验结果表明，RouteNet-Fermi在精度上与计算开销极高的数据包级模拟器相当，并能准确扩展至更大规模的网络。在包含1000个样本的测试数据集上，该模型对时延估计的平均相对误差为6.24%，其中网络拓扑规模比训练时所见的大一个数量级。最后，我们还利用物理测试台的测量数据和真实网络的报文轨迹对RouteNet-Fermi进行了评估。