LLM-enabled Intent-driven Service Configuration for Next Generation Networks

Mekrache, Abdelkader; Ksentini, Adlen

doi:10.1109/netsoft60951.2024.10588881

2024 IEEE 10th International Conference on Network Softwarization (NetSoft) 2024

DOI: 10.1109/netsoft60951.2024.10588881

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LLM-enabled Intent-driven Service Configuration for Next Generation Networks

Abdelkader Mekrache,

Adlen Ksentini

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Large Language Models Meet Next-Generation Networking Technologies: A Review

Hang,

Yu,

Morabito

et al. 2024

Future Internet

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The evolution of network technologies has significantly transformed global communication, information sharing, and connectivity. Traditional networks, relying on static configurations and manual interventions, face substantial challenges such as complex management, inefficiency, and susceptibility to human error. The rise of artificial intelligence (AI) has begun to address these issues by automating tasks like network configuration, traffic optimization, and security enhancements. Despite their potential, integrating AI models in network engineering encounters practical obstacles including complex configurations, heterogeneous infrastructure, unstructured data, and dynamic environments. Generative AI, particularly large language models (LLMs), represents a promising advancement in AI, with capabilities extending to natural language processing tasks like translation, summarization, and sentiment analysis. This paper aims to provide a comprehensive review exploring the transformative role of LLMs in modern network engineering. In particular, it addresses gaps in the existing literature by focusing on LLM applications in network design and planning, implementation, analytics, and management. It also discusses current research efforts, challenges, and future opportunities, aiming to provide a comprehensive guide for networking professionals and researchers. The main goal is to facilitate the adoption and advancement of AI and LLMs in networking, promoting more efficient, resilient, and intelligent network systems.

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Large Language Models Meet Next-Generation Networking Technologies: A Review

Hang,

Yu,

Morabito

et al. 2024

Future Internet

View full text Add to dashboard Cite

show abstract

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LLM-enabled Intent-driven Service Configuration for Next Generation Networks

Cited by 1 publication

References 12 publications

Large Language Models Meet Next-Generation Networking Technologies: A Review

Large Language Models Meet Next-Generation Networking Technologies: A Review

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