vChainNet: Accurate and Scalable End-to-End Slice Modeling for 5G and Beyond Networks

The need for accurate and scalable modeling of 5G and beyond networks has recently emerged, driven by the reliance on virtual network functions (VNFs) and network slicing. Unfortunately, traditional network modeling approaches fail to capture 5G network behavior since they disregard the domainspecific challenges of modeling 5G networks. We propose vChainNet, the first end-to-end network modeling framework that provides accurate, scalable, and generalizable per-VNF and slice-level modeling for 5G and beyond networks. vChainNet introduces a modular, sequence-to-sequence deep learning architecture that models each VNF independently and composes the per-VNF models for end-to-end slice delay prediction. Our system design overcomes key domain-specific challenges in modeling 5G networks, including the functional variability of VNFs, VNFs' stochastic behavior, and the scale introduced by network densification. To address these challenges, vChainNet tunes a lightweight model composed of over$\mathbf{9 5 \%}$fewer parameters than state-of-the-art models, fuses domain-specific manually-engineered features with automatic feature extraction, and optimizes a distribution-based loss function during model training. Our results show that vChainNet achieves an accuracy improvement up to 10.86 % compared to state-of-the-art traditional network models, while providing a speedup of up to 53.33 times over common packet-level simulators. Furthermore, vChainNet's reliance on tuning a lightweight model allows it to generalize to unseen VNF types without extra hyperparameter tuning efforts. These results demonstrate the accuracy, scalability, and generalization ability of vChainNet for modeling 5G and beyond networks.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.