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An SRN-based resiliency quantification approach

Publication ,  Conference
Bruneo, D; Longo, F; Scarpa, M; Puliafito, A; Ghosh, R; Trivedi, KS
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2015

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.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783319194875

Publication Date

January 1, 2015

Volume

9115

Start / End Page

98 / 116

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9115, pp. 98–116). https://doi.org/10.1007/978-3-319-19488-2_5
Bruneo, D., F. Longo, M. Scarpa, A. Puliafito, R. Ghosh, and K. S. Trivedi. “An SRN-based resiliency quantification approach.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9115:98–116, 2015. https://doi.org/10.1007/978-3-319-19488-2_5.
Bruneo D, Longo F, Scarpa M, Puliafito A, Ghosh R, Trivedi KS. An SRN-based resiliency quantification approach. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015. p. 98–116.
Bruneo, D., et al. “An SRN-based resiliency quantification approach.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9115, 2015, pp. 98–116. Scopus, doi:10.1007/978-3-319-19488-2_5.
Bruneo D, Longo F, Scarpa M, Puliafito A, Ghosh R, Trivedi KS. An SRN-based resiliency quantification approach. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015. p. 98–116.
Journal cover image

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783319194875

Publication Date

January 1, 2015

Volume

9115

Start / End Page

98 / 116

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences