@proceedings {Bruneo201598,
	title = {An SRN-based resiliency quantification approach},
	journal = {Proceedings of the  36th International Conference on Application and Theory of Petri Nets and Concurrency (Petri Nets)},
	volume = {9115 Lecture Notes in Computer Science},
	year = {2015},
	note = {cited By 0; Conference of 36th International Conference on Application and Theory of Petri Nets and Concurrency, Petri Nets 2015 ; Conference Date: 21 June 2015 Through 26 June 2015; Conference Code:119609},
	pages = {98-116},
	publisher = {Springer Verlag},
	address = {Brussels, Belgium, 21-26 June 2015},
	abstract = {<p>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. {\textcopyright} Springer International Publishing Switzerland 2015.</p>
},
	keywords = {Analysis techniques, Deterministic and stochastic Petri nets, Model analysis, Performance Model, Petri nets, Resiliency, Semantics, Stochastic models, Stochastic reward nets, Stochastic systems},
	isbn = {9783319194875},
	issn = {03029743},
	doi = {10.1007/978-3-319-19488-2_5},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84937428490\&partnerID=40\&md5=7d0bbe99afba1a79df65b4e35e86a02c},
	author = {Dario Bruneo and Francesco Longo and Marco Scarpa and Antonio Puliafito and Rahul Ghosh and Kishor S. Trivedi}
}
@proceedings {Longo2011335,
	title = {A scalable availability model for Infrastructure-as-a-Service cloud},
	journal = {Proceedings of the 41st IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)},
	year = {2011},
	note = {cited By 33; Conference of 2011 IEEE/IFIP 41st International Conference on Dependable Systems and Networks, DSN 2011 ; Conference Date: 27 June 2011 Through 30 June 2011; Conference Code:86090},
	pages = {335-346},
	publisher = {IEEE Computer Society},
	address = {Hong Kong, Hong Kong, 27-30 June 2011},
	abstract = {<p>High availability is one of the key characteristics of Infrastructure-as-a- Service (IaaS) cloud. In this paper, we show a scalable method for availability analysis of large scale IaaS cloud using analytic models. To reduce the complexity of analysis and the solution time, we use an interacting Markov chain based approach. The construction and the solution of the Markov chains is facilitated by the use of a high-level Petri net based paradigm known as stochastic reward net (SRN). Overall solution is composed by iteration over individual SRN sub-model solutions. Dependencies among the sub-models are resolved using fixed-point iteration, for which existence of a solution is proved. We compare the solution obtained from the interacting sub-models with a monolithic model and show that errors introduced by decomposition are insignificant. Additionally, we provide closed form solutions of the sub-models and show that our approach can handle very large size IaaS clouds. {\textcopyright} 2011 IEEE.</p>
},
	keywords = {Analytic models, availability analysis, Closed form solutions, Fixed-point iterations, High availability, Key characteristics, Large sizes, Markov Chain, Markov model, Markov processes, Petri nets, Scalability, Scalable methods, Solution time, Stochastic reward nets, Submodels},
	isbn = {9781424492336},
	doi = {10.1109/DSN.2011.5958247},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-80051928903\&partnerID=40\&md5=37f3360476d39837acf5098ca20408c7},
	author = {Francesco Longo and Rahul Ghosh and Vijay K. Naik and Kishor S. Trivedi}
}
