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 demonstrate the effectiveness of NetLLM in LLM adaptation for networking, and showcase that the adapted LLM significantly outperforms state-of-the-art algorithms.

翻译：众多网络任务如今采用深度学习（DL）来解决复杂的预测及系统优化问题。然而，当前基于DL的算法设计理念要求为不同网络任务手动设计深度神经网络（DNN），导致巨大的工程开销。此外，DNN在面对未见过的数据分布/环境时往往泛化性能较差。受大型语言模型（LLM）近期成功的启发，本文首次研究将LLM适配于网络领域，以探索更具可持续性的设计理念。凭借大规模预训练知识与强大的推理能力，LLM可作为基础模型，有望实现“一个模型应对所有任务”，并在各类任务中展现更优性能与更强泛化能力。本文提出NetLLM——首个将LLM高效适配以解决网络问题的框架。NetLLM解决了LLM适配中的诸多实际挑战，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。在三个网络相关用例——视口预测（VP）、自适应比特率流媒体（ABR）与集群任务调度（CJS）中，我们验证了NetLLM在LLM网络适配中的有效性，并展示适配后的LLM显著优于当前最先进算法。