A Graph Neural Network-Based Digital Twin for Network Slicing Management
A Graph Neural Network-Based Digital Twin for Network Slicing Management
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DOI：
10.1109/tii.2020.3047843
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发表时间：
2022-02
影响因子：
12.3
通讯作者：
Haozhe Wang;Yulei Wu;G. Min;W. Miao
中科院分区：
文献类型：
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作者：
Haozhe Wang;Yulei Wu;G. Min;W. Miao
网络切片已经成为一种有前途的网络模式，可以为工业4.0和5G网络中的各种服务提供量身定制的资源。然而，由于虚拟化基础设施和严格的服务质量要求，网络复杂性的增加给网络管理带来了巨大的挑战。数字孪生（DT）技术为实现经济高效和性能优化的管理铺平了道路，通过数字化创建切片网络的虚拟表示来模拟其行为并预测随时间变化的性能。在本文中，一个可扩展的DT网络切片的开发，旨在捕捉切片之间的交织关系，并监测不同的网络环境下的切片的端到端（E2E）的指标。所提出的DT利用了新的图神经网络模型，该模型可以直接从由非欧几里德图结构表示的切片网络中学习见解。实验结果表明，DT可以准确地反映网络行为，并预测各种拓扑结构和未知环境下的E2E延迟。
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.