电信科学 ›› 2011, Vol. 27 ›› Issue (10): 51-56.doi: 10.3639/j.issn.1000-0810.2011.10.011

• 专题：云计算技术与应用 • 上一篇    下一篇

一种基于Volterra级数的云平台自适应失效检测机制

侯金轩,林荣恒,邹华,杨放春   

  1. 北京邮电大学网络与交换技术国家重点实验室 北京100876
  • 出版日期:2011-10-15 发布日期:2011-10-15
  • 基金资助:
    核高基科技重大专项基金资助项目;国家“863”计划基金资助项目;国家“973”计划基金资助项目;中兴基金项目资助课题

An Efficient Adaptive Failure Detection Mechanism for Cloud Computing Platform Based on Volterra Series

Jinxuan, Hou,Rongheng Lin,Hua Zou,Fangchun Yang   

  1. State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunication,Beijing 100876,China
    State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunication,Beijing 100876,China
    State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunication,Beijing 100876,China
  • Online:2011-10-15 Published:2011-10-15

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5

摘要：

失效检测是分布式系统特别是云平台容错的关键组成部分。然而由于网络状态的不断变化，要实现对失效的快速准确的检测变得比较困难。为应对这种情况，本文提出了一种基于 Volterra 级数的高效的自适应失效检测机制。该机制基于Volterra 滤波器实现，能够只利用很少的训练样本就可以对时间序列做出很好的预测，且可根据预测误差来自适应地调整以使得预测结果更准确。为了适应云平台中各功能模块对失效检测的不同QoS 需求，本文提出的失效检测机制在统计反馈部分引入调节因子a，可以方便地满足不同应用的QoS 需求。

关键词: 失效检测, Volterra级数, 自适应, 云平台

Abstract:

Failure detector is one of the key components of distributed system especially for cloud computing platform.However,it becomes much more difficult to achieve rapid and accurate detection due to the changing network status.To deal with this situation,we proposed an adaptive and efficient mechanism for failure detection based on volterra series.The mechanism is based on volterra filter,can make a good time series prediction by using only a few training samples,and can be self-adapted according to the prediction error to make the predictions more accurate.Moreover,the failure detection mechanism proposed can easily meet the QoS requirements of different users by in introducing of the feedback regulatory factor a in the statistical part.

Key words: failure detector, volterra series, self-adaptive, cloud computing platform

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