Toward Non-Expert Customized Congestion Control

TL;DR

This paper introduces NECC, a framework that allows non-expert users to easily create and deploy customized congestion control algorithms using Large Language Models and BPF, addressing a gap in user-specific network management.

Contribution

The paper presents the first framework enabling non-experts to implement customized CCAs with LLMs and BPF, demonstrating promising performance and opening new research avenues.

Findings

NECC effectively enables non-experts to model and deploy CCAs.

Performance evaluations show NECC's promising results.

Discussion of insights and future research directions.

Abstract

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.