On Efficient Topology Management in Service-Oriented 6G Networks: An Edge Video Distribution Case Study

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 automatically optimize, train and, finally, select the best of different machine learning models available, based on the specific scenario under study. For link change scenario, the results show that three selected ML models exhibit high accuracy in detecting changes in link delay and bandwidth using measured throughput and RTT. ANN demonstrates the best performance in identifying cases with no changes, slightly outperforming random forest and XGBoost. For user mobility scenario, XGBoost is more efficient in learning patterns for topology change prediction while delivering much faster results compared to the more computationally demanding deep learning models, such as LSTM and CNN. In terms of cost efficiency, our ML-based approach represents a significantly cost-effective alternative to traditional monitoring approaches.