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.

翻译：网络流量日益复杂以及对超低延迟通信的需求，要求更智能的数据包流量管理。现有的基于深度学习的队列方法难以应对动态网络场景，且需要大量工程投入。我们提出AQM-LLM，通过小样本学习、上下文理解和模式识别对大语言模型进行蒸馏，以最小的人工成本改进主动队列管理技术[RFC 9330]。我们特别研究了低延迟、低丢包、可扩展吞吐量的主动队列管理场景，基于推测解码和强化蒸馏的LLM设计了AQM-LLM，通过显式拥塞通知[RFC 9331]和周期性丢包机制，在L4S架构中实现拥塞预防。我们在FreeBSD-14上部署L4S-AQM，开发了新的开源实验平台，提供可互操作模块以支持LLM集成，并通过广泛测试促进IETF标准认可。大量实验表明，L4S-LLM能优化队列管理、预防拥塞、降低延迟并提升网络性能，充分展现了大语言模型在增强AQM系统方面的适应性与效率。