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Title
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AI-empowered management and orchestration of vehicular systems in the beyond 5G era
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Author
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Abstract
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| The complexity of orchestrating Beyond 5G services, such as vehicular, demands novel approaches to remove limitations of existing techniques, as these might cause a large delay in orchestration operations, and thus, negatively impact the service performance. For instance, the human-in-the-loop approach is slow and prone to errors, and closed loop control using rule-based algorithms is difficult to design, as an abundant number of parameters need to be configured. Applying Artificial Intelligence (Al)/Machine Learning (ML), in combination with Network Function Virtualization (NFV) and Software Defined Networking (SDN), seems a promising solution for enabling automation and intelligence that will optimize orchestration operations. In this article, we study the challenges in current ETSI NFV orchestration solutions for B5G C-V2X edge services; propose an Al/ML-based closed-loop orchestration framework; propose how and which Al/ML techniques can alleviate the identified challenges and what are the implications resulting from applying certain Al/ML techniques; and discuss A//ML-based system enablers for B5G C-V2X services. |
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Language
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English
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Source (journal)
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IEEE network. - New York, N.Y.
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Publication
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New York, N.Y.
:
2023
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ISSN
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0890-8044
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DOI
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10.1109/MNET.008.2300024
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Volume/pages
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37
:4
(2023)
, p. 305-313
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ISI
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001175082600063
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Full text (Publisher's DOI)
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Full text (open access)
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Full text (publisher's version - intranet only)
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