A Gossip-Based Approach for Resource Discovery in Structured Peer-to-Peer Networks

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A Gossip-Based Approach for Resource Discovery in Structured Peer-to-Peer Networks

更新时间：2025-07-08

- A Gossip-Based Approach for Resource Discovery in Structured Peer-to-Peer Networks
- Acta Electronica Sinica Vol. 38, Issue 11, Pages: 2510-2517(2010)
- 作者机构：
  
  1. 中国地质大学(武汉)计算机学院,湖北,武汉,430074
  2. 华中科技大学计算机科学与技术学院,湖北,武汉,430074
  3. 中国地质大学(武汉)计算机学院湖北武汉,430074
  4. 华中科技大学计算机科学与技术学院湖北武汉,430074
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP393
- Published：2010
- 稿件说明：
移动端阅览
A Gossip-Based Approach for Resource Discovery in Structured Peer-to-Peer Networks[J]. Acta Electronica Sinica, 2010, 38(11): 2510-2517.
DOI：

A Gossip-Based Approach for Resource Discovery in Structured Peer-to-Peer Networks[J]. Acta Electronica Sinica, 2010, 38(11): 2510-2517. DOI：

摘要

结构化P2P网络下的多属性资源发现一直是一个公开问题.本文针对当前一种新颖的、优于传统方法的多属性资源发现方法－PIRD

深入分析了其在网络动态变化时可能出现的低查询效率问题

并提出一种解决方法:基于闲谈的PIRD(Gossip-based PIRD

G-PIRD).G-PIRD通过闲谈算法估计网络规模

动态调整资源索引的发布以保证高的查询效率.同时针对G-PIRD可能导致的负载不均衡问题

提出一种基于有界LSH(Bounded LSH

B-LSH )的负载均衡策略.试验证明:G-PIRD能动态适应网络变化

保证高效率的多属性资源发现;以及G-PIRD的负载均衡策略在保证高查询效率的同时

大大地降低了节点的索引负载.

Abstract

Multi-attribute resource discovery in structured Peer-to-Peer (P2P) networks is still an open problem.Recently

a novel and more efficient multi-attribute resource discovery approach than traditional ways — P2P-based Intelligent Resource Discovery (PIRD) has been proposed.However

PIRD may be inefficient under network churns.To address this issue

in this paper

a gossip-based PIRD (G-PIRD) is proposed.G-PIRD employs a gossip algorithm to learn the estimated value of network size and dynamically publishes resource indexes to keep a great efficiency of resource discovery.Meanwhile

a load balancing scheme based on bounded LSH (B-LSH) is proposed to deal with the problem of potential load imbalancing of G-PIRD.Extensive experiments show that G-PIRD can adapt well to the changes of network size to maintain high query efficiency and the proposed load balancing scheme can greatly reduce the maximum number of indexes per node with the slight loss of query efficiency.

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