General-purpose congestion control algorithms (CCAs) are designed to achieve general congestion control goals, but they may not meet the specific requirements of certain users. Customized CCAs can meet certain users' specific requirements; however, non-expert users often lack the expertise to implement them. In this paper, we present an exploratory non-expert customized CCA framework, named NECC, which enables non-expert users to easily model, implement, and deploy their customized CCAs by leveraging Large Language Models and the Berkeley Packet Filter (BPF) interface. To the best of our knowledge, we are the first to address the customized CCA implementation problem. Our evaluations using real-world CCAs show that the performance of NECC is very promising, and we discuss the insights that we find and possible future research directions.

翻译：通用拥塞控制算法旨在实现普遍的拥塞控制目标，但可能无法满足特定用户的个性化需求。定制化拥塞控制算法能够满足部分用户的特殊需求，然而非专业用户通常缺乏实现此类算法的专业知识。本文提出一种探索性的非专家定制化拥塞控制框架NECC，该框架通过结合大型语言模型与伯克利数据包过滤器接口，使非专业用户能够便捷地建模、实现并部署其定制化拥塞控制算法。据我们所知，本研究首次系统性地解决了定制化拥塞控制算法的实现难题。基于真实场景拥塞控制算法的评估表明，NECC框架展现出极具前景的性能表现，文中同时探讨了研究发现的内在机理及潜在的未来研究方向。