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

TL;DR

This work addresses dynamic topology management in service-oriented 6G networks by proposing a predictive topology-change framework that complements reactive monitoring. It introduces a 6G Topology Management System with a Topology API (TAPI) and an ML-driven topology detection/prediction pipeline employing ANN, CNN, LSTM, XGBoost, and RF, plus a Top-N voting mechanism. The approach is evaluated on an edge video distribution case using Mininet-wifi, showing ANN excels at detecting link changes while XGBoost best captures mobility-induced changes, with the ML-based method offering cost advantages over traditional monitoring as the system scales. The study demonstrates a practical, standards-aligned path to proactive, QoS-aware topology reconfiguration in dynamic 6G MEC-enabled edge networks.

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.

Figures (8)

• Figure 1: A reference architecture for service-oriented 6G networks.
• Figure 2: Edge video distribution application in 6G sub-networks.
• Figure 3: Topology changes exemplified.
• Figure 4: ML-based change detection architecture.
• Figure 5: Topology emulated in Scenario A.