Large Language Models for Networking: Workflow, Advances, and Challenges

Chang Liu, Xiaohui Xie, Xinggong Zhang, Yong Cui

Abstract: The networking domain is characterized by its high complexity and rapid iteration, requiring extensive expertise to accomplish network tasks, ranging from network design, configuration, diagnosis and security. The inherent complexity of these tasks, coupled with the ever-changing landscape of networking technologies and protocols, poses significant hurdles for traditional methods. However, the recent emergence of large language models (LLMs) has sparked a new wave of possibilities in addressing these challenges. LLMs have demonstrated remarkable capabilities in natural language understanding, generation, and reasoning. These models, trained on extensive data, can benefit the networking domain. Some efforts have already explored the application of LLMs in the networking domain and revealed promising results. To describe the fundamental process involved in applying LLM for networking, we first propose a unified workflow that encompasses a majority of the previous work. We then introduce the highlights of existing works by category and explain in detail how they operate at different stages of the workflow. Furthermore, we delve into the challenges encountered, discuss potential solutions, and outline future research prospects. We hope that this survey will provide insight for researchers and practitioners, promoting the development of this interdisciplinary research field.

External IDs:dblp:journals/network/LiuXZC25