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Accurate routing network status estimation is a key component in SoftwareDefined Networking. However, existing deep-learning-based methods for modelingnetwork routing are not able to extrapolate towards unseen featuredistributions. Nor are they able to handle scaled and drifted networkattributes in test sets that include open-world inputs. To deal with thesechallenges, we propose a novel approach for modeling network routing, usingGraph Neural Networks. Our method can also be used for network-latencyestimation. Supported by a domain-knowledge-assisted graph formulation, ourmodel shares a stable performance across different network sizes andconfigurations of routing networks, while at the same time being able toextrapolate towards unseen sizes, configurations, and user behavior. We showthat our model outperforms most conventional deep-learning-based models, interms of prediction accuracy, computational resources, inference speed, as wellas ability to generalize towards open-world input.

Open World Learning Graph Convolution for Latency Estimation in Routing Networks