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

doi:10.1007/s12204-019-2151-x

Journal of Shanghai Jiao Tong University (Science) ›› 2020, Vol. 25 ›› Issue (1): 57-64.doi: 10.1007/s12204-019-2151-x

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

XU Jiajun (许家俊), PEI Zhiyuan* (裴志远), GUO Lin (郭琳), ZHANG Ruxia (张儒侠), HU Hualang (胡华浪), WANG Fei (王飞)

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

出版日期:2020-01-15 发布日期:2020-01-12
通讯作者: PEI Zhiyuan (裴志远) E-mail:peizhiyuan@agri.gov.cn

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

XU Jiajun (许家俊), PEI Zhiyuan* (裴志远), GUO Lin (郭琳), ZHANG Ruxia (张儒侠), HU Hualang (胡华浪), WANG Fei (王飞)

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

Online:2020-01-15 Published:2020-01-12
Contact: PEI Zhiyuan (裴志远) E-mail:peizhiyuan@agri.gov.cn

摘要/Abstract

摘要： 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.

关键词: reliability analysis, cloud service-based application, SRGM, NMSPN

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.

Key words: reliability analysis, cloud service-based application, SRGM, NMSPN

中图分类号:

TP 31

XU Jiajun (许家俊), PEI Zhiyuan (裴志远), GUO Lin (郭琳), ZHANG Ruxia (张儒侠), HU Hualang. Reliability Analysis of Cloud Service-Based Applications Through SRGM and NMSPN[J]. Journal of Shanghai Jiao Tong University (Science), 2020, 25(1): 57-64.

XU Jiajun (许家俊), PEI Zhiyuan (裴志远), GUO Lin (郭琳), ZHANG Ruxia (张儒侠), HU Hualang (胡华浪), WANG Fei (王飞). Reliability Analysis of Cloud Service-Based Applications Through SRGM and NMSPN[J]. Journal of Shanghai Jiao Tong University (Science), 2020, 25(1): 57-64.

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