A Graph Neural Network-Based Digital Twin for Network Slicing Management

• Published in: IEEE transactions on industrial informatics Vol. 18; no. 2; pp. 1367 - 1376
• Main Authors: Wang, Haozhe, Wu, Yulei, Min, Geyong, Miao, Wang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; Digital twins; Digital twins (DT); end-to-end (E2E) modeling; Graph neural networks; graph neural networks (GNN); Graphical representations; Monitoring; Network latency; Network slicing; Neural networks; Quality of service; Resource management; Substrates; Topology; Virtual networks
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2020.3047843

Network slicing has emerged as a promising networking paradigm to provide resources tailored for Industry 4.0 and diverse services in 5G networks. However, the increased network complexity poses a huge challenge in network management due to virtualized infrastructure and stringent quality-of-service requirements. Digital twin (DT) technology paves a way for achieving cost-efficient and performance-optimal management, through creating a virtual representation of slicing-enabled networks digitally to simulate its behaviors and predict the time-varying performance. In this article, a scalable DT of network slicing is developed, aiming to capture the intertwined relationships among slices and monitor the end-to-end (E2E) metrics of slices under diverse network environments. The proposed DT exploits the novel graph neural network model that can learn insights directly from slicing-enabled networks represented by non-Euclidean graph structures. Experimental results show that the DT can accurately mirror the network behaviour and predict E2E latency under various topologies and unseen environments.