Improving Network Delay Predictions Using GNNs
dc.contributor.author
dc.date.accessioned
2023-10-26T07:18:07Z
dc.date.available
2023-10-26T07:18:07Z
dc.date.issued
2023-07-20
dc.identifier.issn
1064-7570
dc.identifier.uri
dc.description.abstract
Autonomous network management is crucial for Fifth Generation (5G) and Beyond 5G (B5G) networks, where a constantly changing environment is expected and network configuration must adapt accordingly. Modeling tools are required to predict the impact on performance (packet and delay loss) when new traffic demands arrives and when changes in routing paths are applied in the network. Mathematical analysis and network simulators are techniques for modeling networks but both have limitations, as the former provides low accuracy and the latter requires high execution times. To overcome these limitations, machine learning (ML) algorithms, and more specifically, graph neural networks (GNNs), are proposed for network modeling due to their ability to capture complex relationships from graph-like data while predicting network properties with high accuracy and low computational requirements. However, one of the main issues when using GNNs is their lack of generalization capability to larger networks, i.e., when trained in small networks (in number of nodes, paths length, links capacity), the accuracy of predictions on larger networks is poor. This paper addresses the GNN generalization problem by the use of fundamental networking concepts. Our solution is built from a baseline GNN model called RouteNet (developed by Barcelona Neural Networking Center-Universitat Politècnica de Catalunya (BNN-UPC)) that predicts the average delay in network paths, and through a number of simple additions significantly improves the prediction accuracy in larger networks. The improvement ratio compared to the baseline model is 101, from a 187.28% to a 1.828%, measured by the Mean Average Percentage Error (MAPE). In addition, we propose a closed-loop control context where the resulting GNN model could be potentially used in different use cases
dc.description.sponsorship
This project has received funding from the Generalitat de Catalunya through the Consolidated Research Group 2017-SGR-1318 and 2017-SGR-1552, the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya for the FI-SDUR fellowship funding 2020 FISDU00590 assigned to Miquel Farreras, the Scientific Research Flanders (FWO) under grant agreement no. G055619N, and the European Union’s Horizon 2020 research and innovation program under grant agreement no. 101017109 ’DAEMON’.
Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Springer
dc.relation.isformatof
Reproducció digital del document publicat a: https://doi.org/10.1007/s10922-023-09758-9
dc.relation.ispartof
Journal of Network and Systems Management, 2023, vol. 31, p. 65
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Articles publicats (D-IMA)
dc.rights
Attribution 4.0 International
dc.rights.uri
dc.title
Improving Network Delay Predictions Using GNNs
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.relation.projectID
info:eu-repo/grantAgreement/EC/H2020/101017109/EU/Network intelligence for aDAptive and sElf-Learning MObile Networks/DAEMON
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.contributor.funder
dc.type.peerreviewed
peer-reviewed
dc.relation.FundingProgramme
dc.relation.ProjectAcronym
dc.identifier.eissn
1573-7705