Towards more realistic network models based on Graph Neural Networks
Document typeConference report
PublisherAssociation for Computing Machinery (ACM)
Rights accessOpen Access
Recently, a Graph Neural Network (GNN) model called RouteNet was proposed as an efficient method to estimate end-to-end network performance metrics such as delay or jitter, given the topology, routing, and traffic of the network. Despite its success in making accurate estimations and generalizing to unseen topologies, the model makes some simplifying assumptions about the network, and does not consider all the particularities of how real networks operate. In this work we extend the architecture of RouteNet to support different features on forwarding devices, specifically we focus on devices with variable queue sizes, and we experimentally evaluate the accuracy of the extended RouteNet architecture.
CitationBadia, A. [et al.]. Towards more realistic network models based on Graph Neural Networks. A: International Conference on Emerging Networking Experiments and Technologies. "CoNEXT’19 Companion: proceedings of the 15th International Conference on emerging Networking EXperiments and Technologies: December 9-12, 2019, Orlando, FL, USA". New York: Association for Computing Machinery (ACM), 2019, p. 14-16.
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