Distilling Large Language Models for Network Active Queue Management

2025-01-28

AI Summary

Achieving ultra-low latency and high-throughput congestion control in L4S networks remains challenging for conventional active queue management (AQM) schemes. Method: This paper pioneers the integration of large language model (LLM) distillation into AQM, proposing a lightweight LLM-AQM controller that combines few-shot learning, context-awareness, and speculative decoding, with reinforcement learning optimizing the distillation process. Contribution/Results: We introduce the first open-source, kernel-level FreeBSD-14 L4S-AQM experimental platform supporting LLM integration—designed to advance IETF standardization efforts. Extensive experiments demonstrate that our approach reduces end-to-end latency by 37% on average, significantly mitigates queue buildup and packet loss, and enhances throughput stability. These results validate the efficacy, low computational overhead, and strong generalization capability of LLM distillation for real-time network control.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Agent Communication
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Large Language Models
• Network Packet Management
• Ultra-Low Latency Communication

Innovation

• Large Language Models
• Network Traffic Management
• Low Latency Low Loss High Throughput