Open World Learning Graph Convolution for Latency Estimation in Routing Networks

arXiv:2207.14643v2 Announce Type: replace-cross
Abstract: 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. …

abstract arxiv convolution cs.ai cs.lg cs.ni feature graph however key latency modeling network networking networks routing software test type world