Abstract
Resiliency is often considered as a synonym for fault-tolerance and reliability/availability. We start from a different definition of resiliency as the ability to deliver services when encountering unexpected changes. Semantics of change is of extreme importance in order to accurately capture the real behavior of a system. We propose a resiliency analysis technique based on stochastic reward nets that allows the modeler: (1) to reuse an already existing dependability or performance model for a specific system with minimal modifications, and (2) to adapt the given model for specific change semantics. To automate the model analysis an algorithm is designed and the modeler is provided with a formalism that corresponds to the semantics. Our algorithm and approach is implemented to demonstrate the proposed resiliency quantification approach. Finally, we discuss the differences between our approach and an alternative technique based on deterministic and stochastic Petri nets and highlight the advantages of the proposed approach in terms of semantics specification.
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References
DeBardeleben, N., et al.: High-end computing resilience: Analysis of issues facing the hec community and path-forward for research and development, White paper, January 2010
Laprie, J.C.: From dependability to resilience. In: DSN (2008)
Simoncini, L.: Resilient computing: an engineering discipline. In: IPDPS (2009)
Sterbenz, J.P., et al.: Resilience and survivability in communication networks: Strategies, principles, and survey of disciplines. Elsevier Computer Networks, June 2010
Ciardo, G., et al.: Automated generation and analysis of markov reward models using stochastic reward nets. In: Linear Algebra, Markov Chains and Queuing Models. Springer (1993)
Hirel, C., Tuffin, B., Trivedi, K.S.: SPNP: stochastic petri nets. version 6.0. In: Haverkort, B.R., Bohnenkamp, H.C., Smith, C.U. (eds.) TOOLS 2000. LNCS, vol. 1786, pp. 354–357. Springer, Heidelberg (2000)
Mural, I., Bondavalli, A., Zang, X., Trivedi, K.: Dependability modeling and evaluation of phased mission systems: a dspn approach. In: Dependable Computing for Critical Applications 7, pp. 319–337, January 1999
Wang, J., Ip, W., Muddada, R., Huang, J., Zhang, W.: On petri net implementation of proactive resilient holistic supply chain networks. International Journal of Advanced Manufacturing Technology 69(1–4), 427–437 (2013)
Tavana, M., Busch, T., Davis, E.: Modeling operational robustness and resiliency with high-level petri nets. International Journal of Knowledge-Based Organizations 1(2), 17–38 (2011)
Liu, M., Hutchison, D.: Towards resilient networks using situation awareness. In: PGNET (2011)
Rodriguez, R., Merseguer, J., Bernardi, S.: Modelling and analysing resilience as a security issue within uml. In: SERENE (2010)
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Bruneo, D., Longo, F., Scarpa, M., Puliafito, A., Ghosh, R., Trivedi, K.S. (2015). An SRN-Based Resiliency Quantification Approach. In: Devillers, R., Valmari, A. (eds) Application and Theory of Petri Nets and Concurrency. PETRI NETS 2015. Lecture Notes in Computer Science(), vol 9115. Springer, Cham. https://doi.org/10.1007/978-3-319-19488-2_5
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DOI: https://doi.org/10.1007/978-3-319-19488-2_5
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