边缘侧检索增强生成系统高效缓存算法研究

詹慧悠; 倪宏秋; 谈海生; 王天竹; 李向阳

doi:10.12263/DZXB.20250461

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边缘侧检索增强生成系统高效缓存算法研究

学术论文 | 更新时间：2026-04-24

- 边缘侧检索增强生成系统高效缓存算法研究
- Efficient Caching Algorithm for Retrieval Augmented Generation Systems on Edge Devices
- 电子学报 2025年53卷第12期页码：4444-4459
- 作者机构：
  
  中国科学技术大学计算机科学与技术学院，安徽合肥 230027
- 作者简介：
  
  [ "詹慧悠女，1998年1月生，广东韶关人.现为中国科学技术大学计算机科学与技术学院博士研究生.主要研究方向为边缘智能、在线算法等.E-mail: zhanhuiyou@foxmail.com" ]
  [ "倪宏秋女，2000年生，安徽合肥人.中国科学技术大学计算机科学与技术专业博士研究生.主要研究方向为大模型推理调度.E-mail: nhq0806@mail.ustc.edu.cn" ]
  [ "谈海生男，1981年生.中国科学技术大学人工智能与数据科学学院，教授，博士生导师，副院长.主要研究方向为边缘计算、工业互联网、AI+Edge、网络算法设计与系统实现.E-mail: hstan@ustc.edu.cn" ]
  [ "王天竹男，2003年生，河北唐山人.中国科学技术大学数据科学专业硕士研究生.主要研究方向为边缘智能与大语言模型等.E-mail: wtz2333@mail.ustc.edu.cn" ]
  [ "李向阳男，1971年10月生，江苏泰兴人.中国科学技术大学计算机科学与技术学院教授、博士生导师.现任中国科大信息与智能学部执行部长，计算机科学与技术学院执行院长，中国科学院无线光电通信重点实验室主任.主要研究方向为智能物联网、边缘计算、数据共享计算、物联网安全和数据安全隐私等.中国电子学会会员编号：E190072308M.E-mail: xiangyangli@ustc.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62132009)
- DOI：10.12263/DZXB.20250461
  中图分类号： TP303;TP393
- 收稿：2025-05-31，
  
  录用：2025-10-28，
  
  纸质出版：2025-12-25
- 稿件说明：
移动端阅览
詹慧悠, 倪宏秋, 谈海生, 等. 边缘侧检索增强生成系统高效缓存算法研究[J]. 电子学报, 2025, 53(12): 4444-4459.

ZHAN Hui-you, NI Hong-qiu, TAN Hai-sheng, et al. Efficient Caching Algorithm for Retrieval Augmented Generation Systems on Edge Devices[J]. Acta Electronica Sinica, 2025, 53(12): 4444-4459.
詹慧悠, 倪宏秋, 谈海生, 等. 边缘侧检索增强生成系统高效缓存算法研究[J]. 电子学报, 2025, 53(12): 4444-4459. DOI：10.12263/DZXB.20250461

ZHAN Hui-you, NI Hong-qiu, TAN Hai-sheng, et al. Efficient Caching Algorithm for Retrieval Augmented Generation Systems on Edge Devices[J]. Acta Electronica Sinica, 2025, 53(12): 4444-4459. DOI：10.12263/DZXB.20250461

摘要

随着大语言模型在边缘智能场景中的部署需求日益增长，检索增强生成（Retrieval-Augmented Generation， RAG）因其可降低模型依赖、提升领域知识覆盖与隐私保障能力而成为边缘侧部署的重要范式.然而，RAG系统在资源受限的边缘设备上仍面临显著挑战：大规模高维嵌入向量索引难以全量载入内存，频繁的缓存置换与低速存储访问导致检索延迟显著上升.当前嵌入向量获取主要依赖磁盘加载、在线生成和内存缓存三种路径，三者在延迟、计算开销与资源占用方面差异显著，缺乏统一高效的调度机制.针对上述挑战，本文提出一种专为资源受限边缘RAG系统定制的高效在线缓存算法——BP-Cache.该算法的核心创新在于引入了多路径访问代价建模与动态分级缓存管理机制，旨在通过细粒度的资源调度解决边缘侧的性能冲突.首先，本文通过分析揭示了边缘环境下嵌入向量访问的两个关键特性：一是向量生成代价呈现显著的长尾分布，大尺寸向量簇的在线计算延迟远超磁盘加载；二是访问模式存在极强的局部性与稀疏性，超过60%的向量在生命周期内仅被单次访问.基于此观测，BP-Cache引入轻量级准入过滤器机制，利用小缓存区作为“观察窗”暂存新到达向量，实现对低价值访问请求的快速旁路，有效遏制了缓存污染问题.同时，算法构建了代价-大小联合评分模型，将向量簇的在线生成计算代价、磁盘I/O读取延迟及其内存占用大小纳入统一评价体系，在缓存空间不足时，动态优先保留单位内存效益最高的向量簇，从而在无需预知未来访问序列的前提下逼近最优离线策略的性能.本文在基于NVIDIA Jetson AGX Orin的真实边缘平台上部署了该系统，并基于BEIR基准中的多个数据集开展了广泛的对比实验.实验结果表明，与EdgeRAG等当前最先进的边缘RAG方案相比，BP-Cache在多组数据集上平均降低检索延迟约29%，提升缓存命中率约21%，并显著优化了系统的长尾延迟表现.进一步的参数敏感性实验证实，该算法在不同缓存容量配置、小缓存配比及向量簇规模下均表现出优异的鲁棒性与适应性.

Abstract

With the growing demand for deploying large language models (LLMs) in edge intelligence scenarios

retrieval-augmented generation (RAG) has emerged as a pivotal paradigm for edge-side deployment due to its ability to reduce dependency on large models while enhancing domain-specific knowledge coverage and privacy protection. However

RAG systems still face significant challenges on resource-constrained edge devices: large-scale

high-dimensional embedding vector indexes cannot be fully loaded into memory

and frequent cache evictions coupled with slow storage accesses lead to substantially increased retrieval latency. Currently

embedding vectors are primarily acquired through three approaches—disk loading

online generation

and in-memory caching—which differ significantly in terms of latency

computational overhead

and resource consumption

and lack a unified

efficient scheduling mechanism. To address these challenges

this paper proposes BP-Cache

an efficient online caching algorithm specifically tailored for resource-constrained edge RAG systems. The core innovation of BP-Cache lies in its multi-path access cost modeling and dynamic hierarchical cache management mechanism

designed to resolve performance conflicts at the edge through fine-grained resource scheduling. First

we uncover two key characteristics of embedding vector access patterns in edge environments through empirical analysis: (1) the cost of online vector generation exhibits a pronounced long-tail distribution

where the computational latency for large vector clusters far exceeds that of disk loading; and (2) access patterns show strong locality and sparsity

with over 60% of vectors accessed only once throughout their lifetime. Based on these observations

BP-Cache introduces a lightweight admission filtering mechanism that uses a small buffer cache as an “observation window” to temporarily hold newly arriving vectors

enabling rapid bypass of low-value access requests and effectively mitigating cache pollution. Simultaneously

the algorithm constructs a joint cost–size scoring model that integrates the online generation cost

disk I/O latency

and memory footprint of each vector cluster into a unified evaluation framework. When cache capacity is insufficient

BP-Cache dynamically prioritizes retaining vector clusters that deliver the highest utility per unit of memory

thereby approaching the performance of an optimal offline strategy—without requiring any knowledge of future access sequences. We implement and evaluate our system on a real-world edge platform based on the NVIDIA Jetson AGX Orin

conducting extensive experiments across multiple datasets from the BEIR benchmark suite. Results show that

compared to state-of-the-art edge RAG solutions such as EdgeRAG

BP-Cache reduces average retrieval latency by approximately 29% and improves cache hit rate by about 21% across multiple datasets

while significantly optimizing tail latency performance. Further sensitivity analyses confirm that the algorithm demonstrates excellent robustness and adaptability under varying cache capacities

small-buffer ratios

and vector cluster granularities.

关键词

Keywords

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