计算机应用 ›› 2012, Vol. 32 ›› Issue (03): 614-616.DOI: 10.3724/SP.J.1087.2012.00614

• 先进计算 • 上一篇    下一篇

自适应累加型失效检测模型研究

石磊1,陈文远1,陶永才1,卫琳2   

  1. 1.郑州大学 信息工程学院, 郑州450001;
    2.郑州大学 软件技术学院, 郑州 450002
  • 收稿日期:2011-08-29 修回日期:2011-11-15 发布日期:2012-03-01 出版日期:2012-03-01
  • 通讯作者: 石磊
  • 作者简介:石磊(1967-),男,河南郑州人,教授,博士,CCF会员,主要研究方向:高性能计算、Web挖掘;陈文远(1986-),男,河南开封人,硕士研究生,主要研究方向:网络信息;陶永才(1976-),男,河南武陟人,讲师,博士,主要研究方向:网格计算;卫琳(1968-),女,河南郑州人,副教授,主要研究方向:Web挖掘。
  • 基金资助:

    河南省自然科学基金资助项目(2011B520035)。

 Adaptive accrual failure detection model

SHI Lei1, CHEN Wen-yuan1, TAO Yong-cai1, WEI Lin2   

  1. 1.School of Information Engineering, Zhengzhou University, Zhengzhou Henan 450001, China;
    2.School of Software Technology, Zhengzhou University, Zhengzhou Henan 450002, China
  • Received:2011-08-29 Revised:2011-11-15 Online:2012-03-01 Published:2012-03-01
  • Contact: SHI Lei

摘要: 传统失效检测输出二值信息分别代表信任或怀疑,然而该机制存在灵活性不足的问题。针对这种问题,累加型失效检测以怀疑级别为输出,能适应同时运行的不同进程的QoS需求。在分析和研究已有失效检测模型和累加型失效检测算法的基础上,提出一种新的累加型失效检测模型——EXP-ACC-FD。该模型利用幂律计算出心跳间隔的加权平均值,将该均值和距上次心跳到达的时间代入指数分布函数,从而计算出被监测进程的怀疑级别。实验分析表明,在相同的检测时间内,EXP-ACC-FD准确性高于NFD-E失效检测模型和PHI失效检测模型。

关键词: 失效检测, 自适应, 怀疑级别, 指数分布, 幂律

Abstract: The output binary information of traditional failure detection represents trust or suspicion respectively. However, the mechanism is lack of flexibility. For this problem, cumulative-type failure detection, taking the level of suspicion as output, can adapt to the QoS requirements of different processes running simultaneously. An accrual failure detection model named EXP-ACC-FD was proposed on the basis of analyzing and researching the existing failure detection models and accrual failure detection algorithms. It calculates the weighted mean of heartbeat inter-arrivals with power law and substitutes the weighted mean and the time between last heartbeat coming and current time into exponential distribution to get suspicion level of monitored process. The simulation analyses show that the accuracy of EXP-ACC-FD is higher than NFD-E and PHI within the same detection time.

Key words: failure detection, adaptive, suspicion level, exponential distribution, power law

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