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Reliability Analysis of Cloud Service-Based Applications Through SRGM and NMSPN

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Abstract

Cloud service-based applications are subject to reliability critical problem, as the reliability of the application relies on both the failed states and the probabilities of the failures. Classically, reliability analysis approaches are lack of estimating unknown failure rate and non-exponentially distributed failure times. We propose a new framework for analyzing the reliability. The method is mainly decomposed in four successive steps: a non-Makovian stochastic Petri net (NMSPN) model which describes the failure behavior of underlying applications, a software reliability growth model (SRGM) which estimates the failure data of each basic service, a reachability graph which discoveries all the failure sequences, and a computation procedure which computes the occurrences of non-exponential failures. We assess and validate our method by conducting experiment on an actual application. The results demonstrate that the method is competitive compared to the existing approaches for reliability analysis, while providing a better reliability. This result is helpful to the managers in optimizing the overall quality of the cloud service-based application.

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Authors and Affiliations

  1. Key Laboratory of Cultivated Land Use, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Engineering Planning & Design, Beijing, 100125, China

    Jiajun Xu  (许家俊), Zhiyuan Pei  (裴志远), Lin Guo  (郭 琳), Ruxia Zhang  (张儒侠), Hualang Hu  (胡华浪) & Fei Wang  (王 飞)

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  1. Jiajun Xu  (许家俊)
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  2. Zhiyuan Pei  (裴志远)
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  3. Lin Guo  (郭 琳)
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  4. Ruxia Zhang  (张儒侠)
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  5. Hualang Hu  (胡华浪)
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  6. Fei Wang  (王 飞)
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Corresponding author

Correspondence to Zhiyuan Pei  (裴志远).

Additional information

Foundation item: The Special Fund of Major Information Platform Construction and Maintenance of the Ministry of Agriculture and Rural Affairs of China (No. 2130104)

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Xu, J., Pei, Z., Guo, L. et al. Reliability Analysis of Cloud Service-Based Applications Through SRGM and NMSPN. J. Shanghai Jiaotong Univ. (Sci.) 25, 57–64 (2020). https://doi.org/10.1007/s12204-019-2151-x

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  • DOI: https://doi.org/10.1007/s12204-019-2151-x

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