  Many networking tasks now employ deep learning (DL) to solve complex
prediction and 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), this work
studies the LLM adaptation for networking to explore a more sustainable design
philosophy. With the powerful pre-trained knowledge, the LLM is promising to
serve as the foundation model to achieve "one model for all tasks" with even
better performance and stronger generalization. In pursuit of this vision, we
present NetLLM, the first framework that provides a coherent design to harness
the powerful capabilities of LLMs with low efforts to solve networking
problems. Specifically, NetLLM empowers the LLM to effectively process
multimodal data in networking and efficiently generate task-specific answers.
Besides, NetLLM drastically reduces the costs of fine-tuning the LLM to acquire
domain knowledge for networking. Across three networking-related use cases -
viewport prediction, adaptive bitrate streaming and cluster job scheduling, we
showcase that the NetLLM-adapted LLM significantly outperforms state-of-the-art
algorithms.
