  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.
