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

doi:10.1007/s12204-019-2151-x

Abstract

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

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

CLC Number:

TP 31

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.

References 14

[1]	MELL P M, GRANCE T. The NIST definition of cloud computing [EB/OL]. (2011-09-28).https://csrc.nist.gov/publications/detail/sp/800-145/ˉnal.
[2]	BOZKURT M, HARMAN M, HASSOUN Y. Testing and verification in service-oriented architecture: A survey [J]. Software Testing, Veriˉcation and Reliability,2013, 23(4): 261-313.
[3]	BRAGLIA M, FROSOLINI M, MONTANARI R.Fuzzy criticality assessment model for failure modes and effects analysis [J]. International Journal of Quality & Reliability Management, 2003, 20(4): 503-524.
[4]	LONGO F, SCARPA M, PULIAFITO A. WebSPN: A flexible tool for the analysis of non-Markovian stochastic Petri nets [M]//FIONDELLA L, PULIAFITO A.Principles of performance and reliability modeling and evaluation. Cham, Switzerland: Springer, 2016: 255-285.
[5]	CHECHINA N, MACKENZIE K, THOMPSON S, et al. Evaluating scalable distributed erlang for scalability and reliability [J]. IEEE Transactions on Parallel and Distributed Systems, 2017, 28(8): 2244-2257.
[6]	INOUE S, YAMADA S. Markovian software reliability modeling with change-point [J]. International Journal of Reliability, Quality and Safety Engineering, 2018,25(2): 1850009.
[7]	FANG C C, YEH C W. E?ective conˉdence interval estimation of fault-detection process of software reliability growth models [J]. International Journal of Systems Science, 2016, 47(12): 2878-2892.
[8]	KAPUR P K, PHAM H, GUPTA A, et al. Software reliability assessment with OR applications [M]. London:Springer, 2011: 283-312.
[9]	XU J J, YAO S Z, YANG S K, et al. Software reliability growth model with temporal correlation in a network environment [J]. International Journal for Uncertainty Quantiˉcation, 2016, 6(2): 141-156.
[10]	KUMAR N, BANERJEE S. Measuring software reliability: A trend using machine learning techniques[M]//ZHU Q M, AZAR A T. Complex system modelling and control through intelligent soft computations. Cham, Switzerland: Springer, 2014: 807-829.
[11]	PACER M D, GRIFFITHS T l. Upsetting the contingency table: Causal induction over sequences of point events [C]//37th Annual Conference of the Cognitive Science Society. Austin, TX, USA: Cognitive Science Society, 2015.
[12]	WANG P, TARTAKOVSKY D M, JARMAN K D, etal. CDF solutions of Buckley-Leverett equation with uncertain parameters [J]. SIAM Journal on Multiscale Modeling and Simulation, 2013, 11(1): 118-133.
[13]	XU J J, YAO S Z. Characterizing uncertainty of software reliability growth model [J]. Ruan Jian Xue Bao/Journal of Software, 2017, 28(7): 1746-1758 (in Chinese).
[14]	MURATA T. Petri nets: Properties, analysis and applications [J]. Proceedings of the IEEE, 1989, 77(4):541-580.