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.
Cite
@article{arxiv.2601.22461,
title = {Toward Non-Expert Customized Congestion Control},
author = {Mingrui Zhang and Hamid Bagheri and Lisong Xu},
journal= {arXiv preprint arXiv:2601.22461},
year = {2026}
}
Comments
Accepted manuscript (AAM) of IEEE ICC 2025 paper. DOI: 10.1109/ICC52391.2025.11160790