In this paper, we conduct an emulation-guided study to systematically investigate the feasibility of Large language model (LLM)-driven congestion control. The exploration is structured into two phases. The first phase derisks the whole capability where we isolate the role of LLM on a single yet crucial congestion avoidance phase so that we can safely examine when to invoke the LLM, what information to provide, and how to formulate LLM instructions. Based on the gained insights, we extend LLM's role to multiple congestion control phase and propose a more generic LLM-based congestion control policy. Our evaluation on both static and dynamic network traces demonstrates that the LLM-based solution can reduce latency by up to 50\% with only marginal throughput sacrifice (e.g., less than 0.3\%) compared to traditional CCAs. Overall, our exploration study confirms the potential of LLMs for adaptive and general congestion control, demonstrating that when granted appropriate control freedom and paired with an effective triggering mechanism, LLM-based policies achieve significant performance gains, particularly under highly dynamic network conditions.

翻译：本文通过仿真引导式研究，系统探究大语言模型（LLM）驱动拥塞控制的可行性。探索过程分为两个阶段：第一阶段旨在降低整体能力风险，我们仅将LLM角色限定在单个但关键的拥塞避免阶段，从而安全地检验何时调用LLM、提供何种信息以及如何构建LLM指令。基于所得见解，我们将LLM角色扩展至多个拥塞控制阶段，并提出更通用的基于LLM的拥塞控制策略。基于静态与动态网络轨迹的评估表明：与传统CCA相比，基于LLM的解决方案可实现高达50%的延迟降低，同时仅牺牲不到0.3%的吞吐量。总体而言，本探索性研究证实了LLM在自适应通用拥塞控制中的潜力——当赋予适当的控制自由度并配备有效触发机制时，基于LLM的策略能实现显著的性能提升，尤其是在高度动态的网络条件下。