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Data resource discovery model based on hybrid architecture in data grid environment

机译:数据网格环境中基于混合架构的数据资源发现模型

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摘要

Today, the management of massive data collections draws much attention as data grids have been developedrnto deal with large computational problems and provide the opportunity for sharing geographically distributedrnresources for large-scale data-intensive applications. Therefore, finding an effective approach to discoverrndata resources in order to promote better interactions between application communities or virtual organizationsrnbecomes a critical challenge. Traditional grid resource discovery models are mostly based on centralrnand hierarchical architecture that can lead to bottlenecking with the expansion of the grid scale. Although thernPeer-to-Peer (P2P) technique is integrated into the grid in order to improve the performance in recent years,rneach P2P structure still has drawbacks that require several compensatory strategies. In this paper, based onrnthe unstructured super-node-based architecture from the P2P system, we design a structured logic resourcerntree in each domain in order to effectively alleviate the load on the super-node, and we propose a queryrnrecording learning algorithm based on this hybrid architecture to reduce traffic in the network and greatlyrnshorten the response time. The model and algorithm are validated by simulations and compared with therntraditional super-peer model and the flooding-based approach.
机译:如今,由于开发了数据网格以处理大型计算问题并为大型数据密集型应用程序共享地理分布的资源提供了机会,因此海量数据集合的管理引起了人们的极大关注。因此,寻找一种有效的方法来发现数据资源以促进应用程序社区或虚拟组织之间的更好的交互成为一个严峻的挑战。传统的网格资源发现模型主要基于中央和分层体系结构,随着网格规模的扩大,可能会导致瓶颈。尽管近年来对等(P2P)技术已集成到网格中以提高性能,但每个P2P结构仍具有需要几种补偿策略的缺点。本文基于P2P系统中基于非结构化超节点的体系结构,在每个域中设计了一个结构化逻辑资源树,以有效减轻超节点的负载,并提出了基于此的查询记录学习算法混合架构可减少网络流量并大大缩短响应时间。通过仿真验证了该模型和算法,并与传统的超级对等模型和基于泛洪的方法进行了比较。

著录项

  • 来源
    《Concurrency, practice and experience》 |2015年第3期|507-525|共19页
  • 作者单位

    Jiangsu Engineering Centre of Network Monitoring, Nanjing University of Information Science & Technology Jiangsu,Nanjing 210-044, China School of Computer & Software, Nanjing University of Information Science & Technology, Jiangsu,Nanjing 210-044, China;

    School of Computer & Software, Nanjing University of Information Science & Technology, Jiangsu,Nanjing 210-044, China;

    School of Computer & Software, Nanjing University of Information Science & Technology, Jiangsu,Nanjing 210-044, China;

    School of Computer & Software, Nanjing University of Information Science & Technology, Jiangsu,Nanjing 210-044, China;

    Huafeng Meteorological Media Group, Beijing 100-086, China;

    Department of Computer Engineering, Kyung Hee University, Suwon, 446701, Korea;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    data grid; resource discovery; P2P; super-node; logic resource tree;

    机译:数据网格;资源发现;P2P;超节点逻辑资源树;

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