Toward Non-Expert Customized Congestion Control
This addresses the challenge for non-expert users in networking who need tailored CCAs but lack implementation expertise, representing a novel approach to customization.
The paper tackles the problem of enabling non-expert users to implement customized congestion control algorithms (CCAs) by introducing NECC, a framework that uses Large Language Models and BPF, with evaluations showing promising performance.
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.