让非专家用大模型轻松定制网络拥塞控制算法
Toward Non-Expert Customized Congestion Control: Large Language Model-Assisted CCA Code Generation with eBPF Deployment
- 用大模型+eBPF实现非专家可操作的拥塞控制算法生成
- 实测性能接近专业算法，支持真实场景部署
- 适合网络初学者或快速验证新算法的研究者
通用拥塞控制算法（CCAs）虽具普适性，但难以满足特定用户需求。定制化CCAs虽能精准匹配需求，却对非专家而言实现门槛过高。本文提出首个面向非专家的定制化CCA框架NECC，结合大语言模型与eBPF技术，使用户可通过自然语言描述快速建模、生成并部署专属拥塞控制算法。评估表明，NECC生成的算法在真实场景下表现优异，展现出良好实用性。研究还揭示了若干设计洞见，并指明未来方向。
原文摘要 · Abstract (English)
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.
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