Large Language Models meet Network Slicing Management and Orchestration
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914721599324160 |
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| author | Dandoush, Abdulhalim Kumarskandpriya, Viswanath Uddin, Mueen Khalil, Usman |
| author_facet | Dandoush, Abdulhalim Kumarskandpriya, Viswanath Uddin, Mueen Khalil, Usman |
| contents | Network slicing, a cornerstone technology for future networks, enables the creation of customized virtual networks on a shared physical infrastructure. This fosters innovation and agility by providing dedicated resources tailored to specific applications. However, current orchestration and management approaches face limitations in handling the complexity of new service demands within multi-administrative domain environments. This paper proposes a future vision for network slicing powered by Large Language Models (LLMs) and multi-agent systems, offering a framework that can be integrated with existing Management and Orchestration (MANO) frameworks. This framework leverages LLMs to translate user intent into technical requirements, map network functions to infrastructure, and manage the entire slice lifecycle, while multi-agent systems facilitate collaboration across different administrative domains. We also discuss the challenges associated with implementing this framework and potential solutions to mitigate them. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13721 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Models meet Network Slicing Management and Orchestration Dandoush, Abdulhalim Kumarskandpriya, Viswanath Uddin, Mueen Khalil, Usman Networking and Internet Architecture Artificial Intelligence Network slicing, a cornerstone technology for future networks, enables the creation of customized virtual networks on a shared physical infrastructure. This fosters innovation and agility by providing dedicated resources tailored to specific applications. However, current orchestration and management approaches face limitations in handling the complexity of new service demands within multi-administrative domain environments. This paper proposes a future vision for network slicing powered by Large Language Models (LLMs) and multi-agent systems, offering a framework that can be integrated with existing Management and Orchestration (MANO) frameworks. This framework leverages LLMs to translate user intent into technical requirements, map network functions to infrastructure, and manage the entire slice lifecycle, while multi-agent systems facilitate collaboration across different administrative domains. We also discuss the challenges associated with implementing this framework and potential solutions to mitigate them. |
| title | Large Language Models meet Network Slicing Management and Orchestration |
| topic | Networking and Internet Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2403.13721 |