TY - JOUR
T1 - Attention to Virtualization
T2 - Making Network Digital Twins Aware of Network Slicing
AU - Calvillo-Fernandez, Alejandro
AU - Dimitrovski, Toni
AU - Groshev, Milan
AU - Ganesh, Aditya
AU - Ayimba, Constantine
AU - de la Oliva, Antonio
N1 - Publisher Copyright:
© 1986-2012 IEEE.
PY - 2025
Y1 - 2025
N2 - In order to accommodate myriad disparate services, from legacy voice and data, to niche network applications for industry verticals, 6G networks are expected to heavily exploit the concept of network slicing introduced in 5G. However, the increased complexity of sliced networks amplifies the risk of configuration errors, necessitating the expanded use of Network Digital Twins (NDTs) for proactive service assurance. Legacy NDTs, are ill-suited to highly virtualized environments, as they fail to model the impact of virtualization on physical infrastructure and overlook long-term dependency effects. In this work, we highlight the performance impact of virtualization and introduce an attention-enhanced Graph Neural Networks (GNN)-based NDT to address these challenges. Our simulation results demonstrate that the proposed NDT framework significantly outperforms state-of-the-art models in accurately predicting service Key Performance Indicators (KPIs) across disparate use cases.
AB - In order to accommodate myriad disparate services, from legacy voice and data, to niche network applications for industry verticals, 6G networks are expected to heavily exploit the concept of network slicing introduced in 5G. However, the increased complexity of sliced networks amplifies the risk of configuration errors, necessitating the expanded use of Network Digital Twins (NDTs) for proactive service assurance. Legacy NDTs, are ill-suited to highly virtualized environments, as they fail to model the impact of virtualization on physical infrastructure and overlook long-term dependency effects. In this work, we highlight the performance impact of virtualization and introduce an attention-enhanced Graph Neural Networks (GNN)-based NDT to address these challenges. Our simulation results demonstrate that the proposed NDT framework significantly outperforms state-of-the-art models in accurately predicting service Key Performance Indicators (KPIs) across disparate use cases.
KW - 2025 OA procedure
KW - Graph neural networks
KW - Network digital twin
KW - Slicing
KW - Virtualization
KW - Attention
UR - https://www.scopus.com/pages/publications/105000523699
U2 - 10.1109/MNET.2025.3552137
DO - 10.1109/MNET.2025.3552137
M3 - Article
AN - SCOPUS:105000523699
SN - 0890-8044
VL - 39
SP - 134
EP - 139
JO - IEEE network
JF - IEEE network
IS - 3
ER -