电信科学 ›› 2013, Vol. 29 ›› Issue (10): 49-57.doi: 10.3969/j.issn.1000-0801.2013.10.010

• 研究与开发 • 上一篇    下一篇

面向数据流处理的元组跟踪方法

杜华明1,3,张鹏2,3,徐克付2,3,谭建龙2,3,李焱4   

  1. 1 中国科学技术大学软件学院 合肥 230051
    2 中国科学院信息工程研究所 北京 100093
    3 信息内容安全技术国家工程实验室 北京 100093
    4 国家计算机网络应急技术处理协调中心 北京 100029
  • 出版日期:2013-10-15 发布日期:2017-06-19
  • 基金资助:
    国家高技术研究发展计划(“863”计划)基金资助项目;国家自然科学基金资助项目;中国科学院战略性先导专项基金资助项目

TTDSP:A Cost-Effective Approach to Tracking Tuple in Data Stream Processing

Huaming Du1,3,Peng Zhang2,3,Kefu Xu2,3,Jianlong Tan2,3,Yan Li4   

  1. 1 School of Software Engineering,University of Science and Technology of China,Hefei 230051,China
    2 Institute of Information Engineering,Chinese Academy of Sciences,Beijing 100093,China
    3 National Engineering Laboratory for Information Security Technologies,Beijing 100093,China
    4 National Computer Network Emergency Response and Coordination Center,Beijing 100029,China
  • Online:2013-10-15 Published:2017-06-19

摘要:

为了保证数据流中的每个元组得到可靠处理,传统的方法需要在内存中保存每个元组,直到它们被数据流处理系统正常处理,因此会带来很大的内存开销。为此提出了一种既能够保证元组得到可靠处理,又能够节省内存开销的元组跟踪方法。该方法包括内存分配策略、元组跟踪单元选择策略和校验值更新策略,这3个策略使得元组跟踪单元只保留元组标识符的异或校验值而不是元组减少内存开销,同时通过改进一致性散列变换实现元组跟踪单元的负载均衡。内存开销和负载均衡的相关实验表明,该方法能够有效实现对元组的跟踪和可靠处理。

关键词: 数据流处理, 可靠性, 数据流, 负载均衡

Abstract:

The traditional data stream processing systems will keep all the tuples in the memory until they have been processed in order to provide reliable tuple processing.Unfortunately,the strategy will take up much memory.To address this issue,a cost-effective approach to tracking tuples-TTDSP was proposed.The approach includes three strategies,namely memory allocation strategy,tuple acker selection strategy and checksum updating strategy,which make tuple acker to keep only the XOR checksum not the tuple in memory.Moreover,the tuple acker are load balancing through the improved consistent Hash.The experiments on memory overhead and load balancing show that this approach is able to track and process tuples effectively and reliably.

Key words: data stream processing, reliability,data stream, load balancing

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