Congestion is a critical and challenging problem in communication networks. Congestion control protocols allow network applications to tune their sending rate in a way that optimizes their performance and the network utilization. In the common distributed setting, the applications cannot collaborate with each other directly but instead obtain similar estimations about the state of the network using latency and loss measurements. These measurements can be fed into analytical functions, referred to by utility functions, whose gradients help each and all distributed senders to converge to a desired state. The above process becomes extremely complicated when each application has different optimization goals and requirements. Crafting these utilization functions has been a research subject for over a decade, with small incremental changes requiring rigorous mathematical analysis as well as real-world experiments. In this work, we present GenCC, a framework leveraging the code generation capabilities of large language models (LLMs) coupled with realistic network testbed, to design congestion control utility functions. Using GenCC, we analyze the impact of different guidance strategies on the performance of the generated protocols, considering application-specific requirements and network capacity. Our results show that LLMs, guided by either a generative code evolution strategy or mathematical chain-of-thought (CoT), can obtain close to optimal results, improving state-of-the-art congestion control protocols by 37%-142%, depending on the scenario.

翻译：拥塞是通信网络中一个关键且具有挑战性的问题。拥塞控制协议使网络应用能够调整其发送速率，从而优化其性能和网络利用率。在常见的分布式场景中，各应用无法直接相互协作，而是通过延迟和丢包测量获得对网络状态的相似估计。这些测量值可输入至解析函数（即效用函数），其梯度有助于每个分布式发送方共同收敛至期望状态。当每个应用具有不同的优化目标与需求时，上述过程变得极为复杂。效用函数的构建已成为持续十余年的研究课题，即使是微小的增量改进也需要严格的数学分析和实际网络实验。本研究提出GenCC框架，该框架结合大语言模型（LLMs）的代码生成能力与真实网络测试平台，以设计拥塞控制效用函数。通过GenCC，我们分析了不同引导策略对生成协议性能的影响，同时考虑了应用特定需求和网络容量。实验结果表明：在生成式代码演化策略或数学思维链（CoT）引导下，LLMs能够获得接近最优的结果，将最先进的拥塞控制协议性能提升37%-142%（具体取决于场景）。