An SRN-based resiliency quantification approach
Title | An SRN-based resiliency quantification approach |
Publication Type | Conference Proceedings |
Year of Conference | 2015 |
Authors | Bruneo, D., F. Longo, M. Scarpa, A. Puliafito, R. Ghosh, and K. S. Trivedi |
Conference Name | Proceedings of the 36th International Conference on Application and Theory of Petri Nets and Concurrency (Petri Nets) |
Volume | 9115 Lecture Notes in Computer Science |
Pagination | 98-116 |
Publisher | Springer Verlag |
Conference Location | Brussels, Belgium, 21-26 June 2015 |
ISBN Number | 9783319194875 |
ISBN | 03029743 |
Keywords | Analysis techniques, Deterministic and stochastic Petri nets, Model analysis, Performance Model, Petri nets, Resiliency, Semantics, Stochastic models, Stochastic reward nets, Stochastic systems |
Abstract | Resiliency is often considered as a synonym for faulttolerance 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. © Springer International Publishing Switzerland 2015. |
URL | http://www.scopus.com/inward/record.url?eid=2-s2.0-84937428490&partnerID=40&md5=7d0bbe99afba1a79df65b4e35e86a02c |
DOI | 10.1007/978-3-319-19488-2_5 |