一种面向大规模资源发现的分布式局部聚类方法  

A Distributed Local Clustering Method for Large-Scale Resource Discovery

作　　者：孟新宇 潘文宇 马艺宁 MENG Xinyu;PAN Wenyu;MA Yining(Department of Forensic Science,Jiangsu Police Institute,Nanjing 210031,China;Key Laboratory of Impression Evidence Examination and Identification Technology(National Police University of China),Shenyang 110854,China)

机构地区：[1]江苏警官学院刑事科学技术系,南京210031 [2]痕迹检验鉴定技术公安部重点实验室(中国刑事警察学院),沈阳110854

出　　处：《数据采集与处理》2024年第1期215-222,共8页Journal of Data Acquisition and Processing

基　　金：痕迹检验鉴定技术公安部重点实验室(中国刑事警察学院)资助项目(HJKF201906);江苏警官学院大创项目(2023)。

摘　　要：在大规模资源环境下,传统的资源索引机制导致Peer结点数量急剧增加和负载均衡性能下降,影响查询效率和系统稳定性。本文提出了一种质心模型的局部资源聚类方法,通过将相近资源聚类于单一结点并选出代表性键,有效减少了P2P(Peer-to-peer)网络中的Peer结点规模。此外,局部聚类机制集中处理距离相近的键,避免了资源覆盖的过度膨胀。实验结果显示,基于质心模型的Skip Graph算法不仅降低了查询复杂度,提高了负载均衡性能,而且在网络规模、数据量及查询复杂度方面展现出优秀的扩展性,更好地适应大规模资源发现的需求。In large-scale resource environments,traditional resource indexing mechanisms lead to a rapid increase in the number of Peer nodes and a decrease in load balancing performance,affecting query efficiency and system stability.This paper introduces a centroid model-based local resource clustering method,which clusters similar resources at a single node and selects a representative key value,effectively reducing the scale of Peer nodes in the peer‑to‑peer(P2P)network.Additionally,the local clustering mechanism focuses on processing closely related key values,thus preventing excessive expansion of resource coverage.Experimental results demonstrate that the Skip Graph algorithm based on the centroid model not only reduces query complexity and improves load balancing performance,but also exhibits excellent scalability in terms of network size,data volume,and query complexity,better adapting to the needs of large-scale resource discovery.

关 键 词：局部聚类 资源发现 P2P网络 质心模型 

分 类 号：TP302[自动化与计算机技术—计算机系统结构]