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A Weibull distribution accrual failure detector for cloud computing

Author

Listed:
  • Jiaxi Liu
  • Zhibo Wu
  • Jin Wu
  • Jian Dong
  • Yao Zhao
  • Dongxin Wen

Abstract

Failure detectors are used to build high availability distributed systems as the fundamental component. To meet the requirement of a complicated large-scale distributed system, accrual failure detectors that can adapt to multiple applications have been studied extensively. However, several implementations of accrual failure detectors do not adapt well to the cloud service environment. To solve this problem, a new accrual failure detector based on Weibull Distribution, called the Weibull Distribution Failure Detector, has been proposed specifically for cloud computing. It can adapt to the dynamic and unexpected network conditions in cloud computing. The performance of the Weibull Distribution Failure Detector is evaluated and compared based on public classical experiment data and cloud computing experiment data. The results show that the Weibull Distribution Failure Detector has better performance in terms of speed and accuracy in unstable scenarios, especially in cloud computing.

Suggested Citation

  • Jiaxi Liu & Zhibo Wu & Jin Wu & Jian Dong & Yao Zhao & Dongxin Wen, 2017. "A Weibull distribution accrual failure detector for cloud computing," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-16, March.
    • Handle: RePEc:plo:pone00:0173666
    DOI: 10.1371/journal.pone.0173666
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    Cited by:

    1. A. M. Sakura R. H. Attanayake & R. M. Chandima Ratnayake, 2023. "Digitalization of Distribution Transformer Failure Probability Using Weibull Approach towards Digital Transformation of Power Distribution Systems," Future Internet, MDPI, vol. 15(2), pages 1-17, January.
    2. Er-Rahmadi, Btissam & Ma, Tiejun, 2022. "Data-driven mixed-Integer linear programming-based optimisation for efficient failure detection in large-scale distributed systems," European Journal of Operational Research, Elsevier, vol. 303(1), pages 337-353.
    3. Ahtasham Gul & Muhammad Mohsin & Muhammad Adil & Mansoor Ali, 2021. "A modified truncated distribution for modeling the heavy tail, engineering and environmental sciences data," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-24, April.

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