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长持续时间数据流的并行检测算法

周爱平,程 光,郭晓军,朱琛刚   

  1. 1. 东南大学 计算机科学与工程学院,江苏 南京 210096;2. 泰州学院 计算机科学与技术学院,江苏 泰州 225300; 3. 东南大学 计算机网络和信息集成教育部重点实验室,江苏 南京 210096
  • 出版日期:2015-11-27 发布日期:2015-11-27
  • 基金资助:
    国家高技术研究发展计划(“863”计划)基金资助项目(2015AA015603);江苏省未来网络创新研究院未来网络前瞻性研究基金资助项目(BY2013095-5-03);江苏省“六大人才高峰”高层次人才基金资助项目(2011-DZ024)

Parallel detection algorithm on long duration data streaming

  • Online:2015-11-27 Published:2015-11-27

摘要: 针对现有长持续时间数据流检测算法的实时性差、检测精度与估计精度低的问题,提出长持续时间数据流的并行检测算法。基于共享数据结构的长持续时间数据流的并行检测算法中不同线程访问共享数据结构,线程之间的同步开销过大。在此基础上,基于独立数据结构的长持续时间数据流的并行检测算法中不同线程具有本地数据结构,线程之间不需要同步,产生较少的开销。理论分析与实验结果表明,基于独立数据结构的长持续时间数据流的并行检测算法具有良好的时间效率、较高的检测精度和流持续时间估计精度。

关键词: 流量测量;流持续时间;数据流;并行算法

Abstract: Parallel data streaming algorithm was proposed according to the weak real-time performance, low detection precision and estimation accuracy for detection of long duration flow. The different threads access the shared data structure in the parallel algorithm of long duration flow detection based on shared data structure, but it generates excessive synchronous overhead. On the basis of the analytical result on the parallel algorithm of long duration flow detection with shared data structure, the different threads own local data structure in the parallel algorithm of long duration flow detection based on independent data structure, where it doesn’t need synchronization and generates minor overhead. Theoretical analysis and experimental results show that the parallel algorithm of long duration flow detection based on independent data structure has good time efficiency, high detection precision and estimation accuracy of long duration flow.

Key words: traffic measurement; flow duration; data streaming; parallel algorithm

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