Predicting performance metrics in edge-cloud networks using Graph Neural Networks

This paper explores the application of Graph Neural Networks (GNNs) for predicting performance metrics in edge-cloud networks. By modeling the network as a graph, where nodes represent devices, and edges represent communication links, GNNs effectively capture the complex interdependencies and interactions within the network. We demonstrate that GNNs can accurately predict key performance metrics such as latency and jitter, using data from real network conditions. Our findings highlight the potential of GNNs to enhance performance monitoring and optimization in edge-cloud environments, paving the way for more efficient resource management and energy-efficiency.

Metadaten
Author:	Christian Maier, Nina Großegesse, Felix Strohmeier
URN:	urn:nbn:de:bvb:384-opus4-1252545
Frontdoor URL	https://opus.bibliothek.uni-augsburg.de/opus4/125254
Parent Title (English):	3rd Workshop on Machine Learning in Networking (MaLeNe), co-located with the 6th International Conference on Networked Systems (NetSys 2025), Ilmenau, Germany, September 1, 2025: proceedings
Publisher:	Universität Augsburg
Place of publication:	Augsburg
Editor:	Michael SeufertORCiD GND, Andreas Blenk, Björn Richerzhagen
Type:	Conference Proceeding
Language:	English
Date of Publication (online):	2025/09/15
Year of first Publication:	2025
Publishing Institution:	Universität Augsburg
Release Date:	2025/09/16
Institutes:	Fakultät für Angewandte Informatik
	Fakultät für Angewandte Informatik / Institut für Informatik
	Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Vernetzte Systeme und Kommunikationsnetze
Dewey Decimal Classification:	0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):	CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)

Open Access