As literature witnesses, multiformalism and multisolution have proven their effectiveness in the field of systems performance evaluation. Multiformalism modeling faces complexity of systems by allowing different and coordinated models, written by different formalisms (formal description languages), to coexist. In this way the entire system can be described by using the most suitable formalism for all its components. Multisolution is a model solution approach combining different evaluation methods to: i) obtain the desired evaluation indices about the model, ii) exploit the different features of the considered formalisms, iii) fit different conditions of the model or iv) build complex indices requiring different steps for their computation. Choosing the best combination of formalisms and solvers for a specific need is also a part of the modeling effort, that can sensibly influence the results. In this paper we suggest several successful approaches by presenting different case studies. © 2015 Scrivener Publishing LLC.
Barbierato, E., Gribaudo, M., Iacono, M., Multiformalism and Multisolution Strategies for Systems Performance Evaluation, in Dario Bruneo (editor), S. D. (. (ed.), Quantitative Assessments of Distributed Systems: Methodologies and Techniques, Wiley, New Jersey 2015: Quantitative Assessments of Distributed Systems: Methodologies and Techniques 201- 222. 10.1002/9781119131151.ch8 [http://hdl.handle.net/10807/202858]
Multiformalism and Multisolution Strategies for Systems Performance Evaluation
Barbierato, EnricoSoftware
;
2015
Abstract
As literature witnesses, multiformalism and multisolution have proven their effectiveness in the field of systems performance evaluation. Multiformalism modeling faces complexity of systems by allowing different and coordinated models, written by different formalisms (formal description languages), to coexist. In this way the entire system can be described by using the most suitable formalism for all its components. Multisolution is a model solution approach combining different evaluation methods to: i) obtain the desired evaluation indices about the model, ii) exploit the different features of the considered formalisms, iii) fit different conditions of the model or iv) build complex indices requiring different steps for their computation. Choosing the best combination of formalisms and solvers for a specific need is also a part of the modeling effort, that can sensibly influence the results. In this paper we suggest several successful approaches by presenting different case studies. © 2015 Scrivener Publishing LLC.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.