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

TL;DR

This paper introduces an AE-SMPN model combining GNN, RNN, and AutoEncoder techniques to evaluate network performance using real data, enhancing accuracy and scalability for Digital Twin Networks.

Contribution

The paper proposes a novel autoencoder-based skip connected message passing neural network for real-data network evaluation in Digital Twin Networks, integrating GNN and RNN for spatiotemporal analysis.

Findings

Model effectively captures spatiotemporal features of network data.

Experimental results demonstrate improved evaluation accuracy.

Model structure analysis provides insights for further optimization.

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.