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

2024-10-14

arXiv.org

AI Summary

To address the high management overhead and delayed response caused by frequent dynamic subnet topology changes in edge video distribution scenarios for 6G service-based networks, this paper proposes a machine learning–based predictive topology management method. Our approach introduces a novel adaptive model selection mechanism that automatically deploys the optimal lightweight model—ANN, XGBoost, LSTM, or CNN—based on contextual triggers such as link fluctuations or user mobility. By fusing real-time throughput and RTT measurements, the method enables low-latency topology prediction at the MEC edge. This work marks the first paradigm shift from reactive to predictive subnet-level topology management: ANN achieves highest accuracy for link change prediction; XGBoost delivers superior trade-offs between accuracy and inference speed—orders of magnitude faster than LSTM or CNN under user mobility scenarios. Overall, the solution significantly reduces operational costs and resource consumption.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• 6G network
• edge region video transmission
• network structure management

Innovation

• 6G Network Prediction
• Adaptive Machine Learning Models
• Cost-effective ML Approach