Accurate routing network status estimation is a key component in Software Defined Networking. However, existing deep-learning-based methods for modeling network routing are not able to extrapolate towards unseen feature distributions. Nor are they able to handle scaled and drifted network attributes in test sets that include open-world inputs. To deal with these challenges, we propose a novel approach for modeling network routing, using Graph Neural Networks. Our method can also be used for network-latency estimation. Supported by a domain-knowledge-assisted graph formulation, our model shares a stable performance across different network sizes and configurations of routing networks, while at the same time being able to extrapolate towards unseen sizes, configurations, and user behavior. We show that our model outperforms most conventional deep-learning-based models, in terms of prediction accuracy, computational resources, inference speed, as well as ability to generalize towards open-world input.

翻译：精确的路由网络状态估计是软件定义网络中的关键组成部分。然而，现有的基于深度学习的网络路由建模方法无法外推到未见过的特征分布，也无法处理包含开放世界输入的测试集中缩放和漂移的网络属性。为应对这些挑战，我们提出了一种基于图神经网络的新型网络路由建模方法，该方法还可用于网络延迟估计。在领域知识辅助的图公式支持下，我们的模型在不同规模的网络和路由网络配置下均能保持稳定性能，同时能够外推到未见过的规模、配置和用户行为。我们证明，该模型在预测精度、计算资源、推理速度以及向开放世界输入泛化的能力方面，优于大多数传统的基于深度学习的模型。