用大模型提升网络流量估算精度，内存占用更低。
LLM-Sketch: Enhancing Network Sketches with LLM
- 分层记录大小流量，结合微调大模型预测流量大小
- 在三项任务中实现7.5倍精度提升，内存开销小
- 适合需要高精度流分析的实时网络系统
网络流挖掘对多种网络运维至关重要。数据结构中的Sketch以低内存开销和有界精度成为主流方案。现有基于机器学习的方法存在难以适应动态网络、训练成本高等问题。本文提出LLM-Sketch，基于非报文头流ID信息也可辅助推断流大小的洞察，采用双层结构分别记录大流与小流，提升精度同时最小化内存使用。进一步利用微调的大语言模型（LLMs）可靠估计流大小。我们在三个代表性任务上评估了LLM-Sketch，结果表明其相比现有最优方法实现7.5倍精度提升。
原文摘要 · Abstract (English)
Network stream mining is fundamental to many network operations. Sketches, as compact data structures that offer low memory overhead with bounded accuracy, have emerged as a promising solution for network stream mining. Recent studies attempt to optimize sketches using machine learning; however, these approaches face the challenges of lacking adaptivity to dynamic networks and incurring high training costs. In this paper, we propose LLM-Sketch, based on the insight that fields beyond the flow IDs in packet headers can also help infer flow sizes. By using a two-tier data structure and separately recording large and small flows, LLM-Sketch improves accuracy while minimizing memory usage. Furthermore, it leverages fine-tuned large language models (LLMs) to reliably estimate flow sizes. We evaluate LLM-Sketch on three representative tasks, and the results demonstrate that LLM-Sketch outperforms state-of-the-art methods by achieving a $7.5\times$ accuracy improvement.
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