LLM-Sketch: Enhancing Network Sketches with LLM

• URL: http://arxiv.org/abs/2502.07495v1
• Date: Tue, 11 Feb 2025 11:54:56 GMT
• Title: LLM-Sketch: Enhancing Network Sketches with LLM
• Authors: Yuanpeng Li, Zhen Xu, Zongwei Lv, Yannan Hu, Yong Cui, Tong Yang,
• Abstract summary: Sketches are compact data structures that offer low memory overhead with bounded accuracy.<n>Recent studies attempt to optimize sketches using machine learning.<n>We propose LLM-Sketch, based on the insight that fields beyond the flow IDs in packet headers can also help infer flow sizes.
• Score: 10.886932940560477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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