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

Author

Listed:
  • Ching-Nam Hang

    (Yam Pak Charitable Foundation School of Computing and Information Sciences, Saint Francis University, Hong Kong, China)

  • Pei-Duo Yu

    (Department of Applied Mathematics, Chung Yuan Christian University, Taoyuan City 320314, Taiwan)

  • Roberto Morabito

    (Communication Systems Department, EURECOM, 06140 Biot, France)

  • Chee-Wei Tan

    (College of Computing and Data Science, Nanyang Technological University, Singapore 639798, Singapore)

Abstract

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.

Suggested Citation

  • Ching-Nam Hang & Pei-Duo Yu & Roberto Morabito & Chee-Wei Tan, 2024. "Large Language Models Meet Next-Generation Networking Technologies: A Review," Future Internet, MDPI, vol. 16(10), pages 1-29, October.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:10:p:365-:d:1493535
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    References listed on IDEAS

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    4. Karan Singhal & Shekoofeh Azizi & Tao Tu & S. Sara Mahdavi & Jason Wei & Hyung Won Chung & Nathan Scales & Ajay Tanwani & Heather Cole-Lewis & Stephen Pfohl & Perry Payne & Martin Seneviratne & Paul G, 2023. "Large language models encode clinical knowledge," Nature, Nature, vol. 620(7972), pages 172-180, August.
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