Improving the Real-Data Driven Network Evaluation Model for Digital Twin Networks

TL;DR

This work addresses the challenge of evaluating network performance in Digital Twin Networks using real data to inform automated optimization. It introduces AE-SMPN, a model that combines AutoEncoder-based feature extraction, Graph Neural Network-based message passing, and LSTM-based temporal state propagation with a skip-connected readout, trained and validated on real DTN data from the UPC-BNN dataset. Key contributions include an AE-based embedding pipeline, a skip-connected SMPN architecture, and empirical evidence showing improved accuracy (lower $MAPE$) over a baseline on real DTN topologies. The results support the viability of real-data DTN evaluation models to enable autonomous network management and reduced latency in large-scale networks, while highlighting overheads from embedding and avenues for lightweighting and modularization.

Abstract

With the emergence and proliferation of new forms of large-scale services such as smart homes, virtual reality/augmented reality, the increasingly complex networks are raising concerns about significant operational costs. As a result, the need for network management automation is emphasized, and Digital Twin Networks (DTN) technology is expected to become the foundation technology for autonomous networks. DTN has the advantage of being able to operate and system networks based on real-time collected data in a closed-loop system, and currently it is mainly designed for optimization scenarios. To improve network performance in optimization scenarios, it is necessary to select appropriate configurations and perform accurate performance evaluation based on real data. However, most network evaluation models currently use simulation data. Meanwhile, according to DTN standards documents, artificial intelligence (AI) models can ensure scalability, real-time performance, and accuracy in large-scale networks. Various AI research and standardization work is ongoing to optimize the use of DTN. When designing AI models, it is crucial to consider the characteristics of the data. This paper presents an autoencoder-based skip connected message passing neural network (AE-SMPN) as a network evaluation model using real network data. The model is created by utilizing graph neural network (GNN) with recurrent neural network (RNN) models to capture the spatiotemporal features of network data. Additionally, an AutoEncoder (AE) is employed to extract initial features. The neural network was trained using the real DTN dataset provided by the Barcelona Neural Networking Center (BNN-UPC), and the paper presents the analysis of the model structure along with experimental results.

Figures (8)

• Figure 1: Example of the optimization scenario in DTN.
• Figure 2: DTN basic architecture.
• Figure 3: ML model training pipeline in DTN.
• Figure 4: The architecture of purposed model.
• Figure 5: DTN testbed structure.