A Graph Neural Network-based Digital Twin for Network Slicing Management
Wang, H; Wu, Y; Min, G; et al.Miao, W
Date: 29 December 2020
Journal
IEEE Transactions on Industrial Informatics
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publisher DOI
Abstract
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 virtualised infrastructure and stringent Quality-of-Service (QoS) requirements. Digital twin ...
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 virtualised infrastructure and stringent Quality-of-Service (QoS) 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 behaviours and predict the time-varying performance. In this paper, 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 slice-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.
Computer Science
Faculty of Environment, Science and Economy
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