Many networking tasks now employ deep learning (DL) to solve complex prediction and system optimization problems. However, current design philosophy of DL-based algorithms entails intensive engineering overhead due to the manual design of deep neural networks (DNNs) for different networking tasks. Besides, DNNs tend to achieve poor generalization performance on unseen data distributions/environments. Motivated by the recent success of large language models (LLMs), for the first time, this work studies the LLM adaptation for networking to explore a more sustainable design philosophy. With the massive pre-trained knowledge and powerful inference ability, LLM can serve as the foundation model, and is expected to achieve "one model for all" with even better performance and stronger generalization for various tasks. In this paper, we present NetLLM, the first LLM adaptation framework that efficiently adapts LLMs to solve networking problems. NetLLM addresses many practical challenges in LLM adaptation, from how to process task-specific information with LLMs, to how to improve the efficiency of answer generation and acquiring domain knowledge for networking. Across three networking-related use cases - viewport prediction (VP), adaptive bitrate streaming (ABR) and cluster job scheduling (CJS), we showcase the effectiveness of NetLLM in LLM adaptation for networking. Results show that the adapted LLM surpasses state-of-the-art algorithms by 10.1-36.6% for VP, 14.5-36.6% for ABR, 6.8-41.3% for CJS, and also achieves superior generalization performance.

翻译：当前众多网络任务采用深度学习技术解决复杂预测与系统优化问题。然而，现有基于深度学习的算法设计范式因需针对不同网络任务手动设计深度神经网络，导致高昂的工程开销。此外，深度神经网络在未见数据分布或环境下的泛化性能往往较差。受大语言模型近期成功应用的启发，本文首次探索将大语言模型适配至网络领域的研究，旨在构建更具可持续性的设计范式。凭借海量预训练知识与强大的推理能力，大语言模型可作为基础模型，有望实现"一模型通用于所有任务"，并在各类任务中展现出更优性能与更强的泛化能力。本文提出NetLLM——首个高效适配大语言模型以解决网络问题的框架。NetLLM克服了LLM适配中的多项实际挑战，涵盖如何利用LLM处理任务特定信息、提升答案生成效率以及获取网络领域知识等关键问题。通过视口预测、自适应视频流传输与集群作业调度三个典型网络应用场景的验证，NetLLM在LLM网络适配中展现出卓越效果。实验表明，适配后的LLM在视口预测任务中性能超越现有最优算法10.1%-36.6%，自适应视频流传输提升14.5%-36.6%，集群作业调度提升6.8%-41.3%，同时实现了更优的泛化性能。