用大模型提升网络队列管理，降低延迟并预防拥塞
Distilling Large Language Models for Network Active Queue Management
- 将大语言模型通过少样本学习和上下文理解，蒸馏为智能队列控制器
- 在L4S架构下实现低延迟、低丢包，减少90%以上拥塞发生
- 开源实验平台支持模型集成，适合网络优化与AI系统研究者
日益复杂的网络流量和对超低延迟通信的需求，要求更智能的包调度管理。现有基于深度学习的队列管理方法在动态场景中表现不佳且工程成本高。我们提出AQMLLM，通过少样本学习、上下文理解和模式识别，将大语言模型（LLMs）蒸馏为轻量级主动队列管理（AQM）系统，显著降低人工干预。针对低延迟、低丢包、可扩展吞吐量（L4S）场景，设计结合推测解码与强化学习蒸馏的机制，利用显式拥塞通知（ECN）和周期性丢包策略预防拥塞。我们构建了基于FreeBSD-14的开源实验平台，提供可互操作模块以支持LLM集成，并推动IETF标准认可。大量测试表明，L4S-LLM有效提升队列管理能力，预防拥塞，降低延迟，增强网络性能，展现大模型在升级AQM系统中的适应性与高效性。
原文摘要 · Abstract (English)
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.
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