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System resiliency quantification using non-state-space and state-space analytic models

Author

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  • Ghosh, Rahul
  • Kim, DongSeong
  • Trivedi, Kishor S.

Abstract

Resiliency is becoming an important service attribute for large scale distributed systems and networks. Key problems in resiliency quantification are lack of consensus on the definition of resiliency and systematic approach to quantify system resiliency. In general, resiliency is defined as the ability of (system/person/organization) to recover/defy/resist from any shock, insult, or disturbance [1]. Many researchers interpret resiliency as a synonym for fault-tolerance and reliability/availability. However, effect of failure/repair on systems is already covered by reliability/availability measures and that of on individual jobs is well covered under the umbrella of performability [2] and task completion time analysis [3]. We use Laprie [4] and Simoncini [5]'s definition in which resiliency is the persistence of service delivery that can justifiably be trusted, when facing changes. The changes we are referring to here are beyond the envelope of system configurations already considered during system design, that is, beyond fault tolerance. In this paper, we outline a general approach for system resiliency quantification. Using examples of non-state-space and state-space stochastic models, we analytically–numerically quantify the resiliency of system performance, reliability, availability and performability measures w.r.t. structural and parametric changes.

Suggested Citation

  • Ghosh, Rahul & Kim, DongSeong & Trivedi, Kishor S., 2013. "System resiliency quantification using non-state-space and state-space analytic models," Reliability Engineering and System Safety, Elsevier, vol. 116(C), pages 109-125.
    • Handle: RePEc:eee:reensy:v:116:y:2013:i:c:p:109-125
    DOI: 10.1016/j.ress.2012.12.023
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    Cited by:

    1. Gandoman, Foad H. & Ahmadi, Abdollah & Bossche, Peter Van den & Van Mierlo, Joeri & Omar, Noshin & Nezhad, Ali Esmaeel & Mavalizadeh, Hani & Mayet, Clément, 2019. "Status and future perspectives of reliability assessment for electric vehicles," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 1-16.
    2. Di Giandomenico, F. & Itria, M.L. & Masci, P. & Nostro, N., 2014. "Automated synthesis of dependable mediators for heterogeneous interoperable systems," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 220-232.
    3. Chiacchio, F. & D’Urso, D. & Manno, G. & Compagno, L., 2016. "Stochastic hybrid automaton model of a multi-state system with aging: Reliability assessment and design consequences," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 1-13.
    4. Dhople, S.V. & DeVille, L. & Domínguez-García, A.D., 2014. "A Stochastic Hybrid Systems framework for analysis of Markov reward models," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 158-170.
    5. Å nipas, Mindaugas & Radziukynas, Virginijus & ValakeviÄ ius, Eimutis, 2018. "Numerical solution of reliability models described by stochastic automata networks," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 570-578.

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