On Efficient Topology Management in Service-Oriented 6G Networks: An Edge Video Distribution Case Study
Zied Ennaceur, Mounir Bensalem, Admela Jukan, Claus Keuker, and Huanzhuo Wu, Rastin Pries

TL;DR
This paper presents a machine learning-based topology change prediction system for 6G networks, focusing on edge video distribution, demonstrating high accuracy and cost efficiency compared to traditional methods.
Contribution
It introduces a novel ML algorithm that automatically selects the best model for predicting topology changes in 6G networks, validated through a practical edge video distribution case study.
Findings
ANN outperforms other models in no-change detection.
XGBoost is most efficient for mobility-related predictions.
ML approach is more cost-effective than traditional monitoring.
Abstract
An efficient topology management in future 6G networks is one of the fundamental challenges for a dynamic network creation based on location services, whereby each autonomous network entity, i.e., a sub-network, can be created for a specific application scenario. In this paper, we study the performance of a novel topology changes management system in a sample 6G network being dynamically organized in autonomous sub-networks. We propose and analyze an algorithm for intelligent prediction of topology changes and provide a comparative analysis with topology monitoring based approach. To this end, we present an industrially relevant case study on edge video distribution, as it is envisioned to be implemented in line with the 3GPP and ETSI MEC (Multi-access Edge Computing) standards. For changes prediction, we implement and analyze a novel topology change prediction algorithm, which can…
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Taxonomy
TopicsMultimedia Communication and Technology · Telecommunications and Broadcasting Technologies · Image and Video Quality Assessment
