Distilling Large Language Models for Network Active Queue Management

TL;DR

This paper introduces AQM-LLM, a novel approach that distills large language models to improve active queue management in networks, achieving better congestion control and lower latency with minimal manual tuning.

Contribution

It presents a new LLM-based AQM method that leverages few-shot learning and pattern recognition, specifically tailored for L4S congestion control, with an open-source implementation and extensive evaluation.

Findings

L4S-LLM improves queue management and congestion prevention.

Reduces network latency and enhances throughput.

Demonstrates adaptability of LLMs in network traffic control.

Abstract

The growing complexity of network traffic and demand for ultra-low latency communication require smarter packet traffic management. Existing Deep Learning-based queuing approaches struggle with dynamic network scenarios and demand high engineering effort. We propose AQM-LLM, distilling Large Language Models (LLMs) with few-shot learning, contextual understanding, and pattern recognition to improve Active Queue Management (AQM) [RFC 9330] with minimal manual effort. We consider a specific case where AQM is Low Latency, Low Loss, and Scalable Throughput (L4S) and our design of AQM-LLM builds on speculative decoding and reinforcement-based distilling of LLM by tackling congestion prevention in the L4S architecture using Explicit Congestion Notification (ECN) [RFC 9331] and periodic packet dropping. We develop a new open-source experimental platform by executing L4S-AQM on FreeBSD-14, providing interoperable modules to support LLM integration and facilitate IETF recognition through wider testing. Our extensive evaluations show L4S-LLM enhances queue management, prevents congestion, reduces latency, and boosts network performance, showcasing LLMs' adaptability and efficiency in uplifting AQM systems.