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Great-Nsolve: A Tool Integration for (Markov Regenerative) Stochastic Petri Nets

  • Elvio Gilberto AmparoreEmail author
  • Peter BuchholzEmail author
  • Susanna DonatelliEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11785)

Abstract

This paper presents Great-Nsolve, the integration of GreatSPN (with its user-friendly graphical interface and its numerous possibilities of stochastic Petri net analysis) and Nsolve (with its very efficient numerical solution methods) aimed at solving large Markov Regenerative Stochastic Petri Nets (MRSPN). The support for general distribution is provided by the alphaFactory library.

References

  1. 1.
    Ajmone Marsan, M., Conte, G., Balbo, G.: A class of generalized stochastic Petri nets for the performance evaluation of multiprocessor systems. ACM Trans. Comput. Sys. 2, 93–122 (1984)CrossRefGoogle Scholar
  2. 2.
    Amparore, E.G., Donatelli, S.: DSPN-tool: a new DSPN and GSPN solver for GreatSPN. In: Proceedings of the 2010 Seventh International Conference on the Quantitative Evaluation of Systems, QEST 2010, Washington, DC, USA, pp. 79–80. IEEE Computer Society (2010). ISBN: 978-0-7695-4188-4,  https://doi.org/10.1109/QEST.2010.17
  3. 3.
    Amparore, E.G.: A new greatSPN GUI for GSPN editing and CSLTA model checking. In: Norman, G., Sanders, W. (eds.) QEST 2014. LNCS, vol. 8657, pp. 170–173. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10696-0_13CrossRefGoogle Scholar
  4. 4.
    Amparore, E.G., Balbo, G., Beccuti, M., Donatelli, S., Franceschinis, G.: 30 years of GreatSPN. In: Fiondella, L., Puliafito, A. (eds.) Principles of Performance and Reliability Modeling and Evaluation. SSRE, pp. 227–254. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-30599-8_9CrossRefGoogle Scholar
  5. 5.
    Amparore, E.G., Buchholz, P., Donatelli, S.: A structured solution approach for Markov regenerative processes. In: Norman, G., Sanders, W. (eds.) QEST 2014. LNCS, vol. 8657, pp. 9–24. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10696-0_3CrossRefGoogle Scholar
  6. 6.
    Amparore, E.G., Donatelli, S.: Revisiting the matrix-free solution of Markov regenerative processes. Numer. Linear Algebra Appl. 18, 1067–1083 (2011)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Amparore, E.G., Donatelli, S.: A component-based solution for reducible Markov regenerative processes. Perform. Eval. 70(6), 400–422 (2013)CrossRefGoogle Scholar
  8. 8.
    Amparore, E.G., Donatelli, S.: alphaFactory: a tool for generating the alpha factors of general distributions. In: Bertrand, N., Bortolussi, L. (eds.) QEST 2017. LNCS, vol. 10503, pp. 36–51. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66335-7_3CrossRefGoogle Scholar
  9. 9.
    Babar, J., Beccuti, M., Donatelli, S., Miner, A.: GreatSPN enhanced with decision diagram data structures. In: Lilius, J., Penczek, W. (eds.) PETRI NETS 2010. LNCS, vol. 6128, pp. 308–317. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-13675-7_19CrossRefGoogle Scholar
  10. 10.
    Bause, F., Buchholz, P., Kemper, P.: A toolbox for functional and quantitative analysis of DEDS. In: Puigjaner, R., Savino, N.N., Serra, B. (eds.) TOOLS 1998. LNCS, vol. 1469, pp. 356–359. Springer, Heidelberg (1998).  https://doi.org/10.1007/3-540-68061-6_32CrossRefGoogle Scholar
  11. 11.
    Buchholz, P., Ciardo, G., Donatelli, S., Kemper, P.: Complexity of memory-efficient Kronecker operations with applications to the solution of Markov models. INFORMS J. Comput. 12(3), 203–222 (2000)MathSciNetCrossRefGoogle Scholar
  12. 12.
  13. 13.
    Buchholz, P.: Hierarchical structuring of superposed GSPNs. IEEE Trans. Softw. Eng. 25(2), 166–181 (1999)CrossRefGoogle Scholar
  14. 14.
    Buchholz, P., Dayar, T., Kriege, J., Orhan, M.C.: On compact solution vectors in Kronecker-based Markovian analysis. Perform. Eval. 115, 132–149 (2017)CrossRefGoogle Scholar
  15. 15.
    Buchholz, P., Kemper, P.: Kronecker based matrix representations for large Markov models. In: Baier, C., Haverkort, B.R., Hermanns, H., Katoen, J.-P., Siegle, M. (eds.) Validation of Stochastic Systems. LNCS, vol. 2925, pp. 256–295. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-24611-4_8CrossRefGoogle Scholar
  16. 16.
    Choi, H., Kulkarni, V.G., Trivedi, K.S.: Markov regenerative stochastic Petri nets. Perform. Eval. 20(1–3), 337–357 (1994)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Donatelli, S., Haddad, S., Sproston, J.: Model checking timed and stochastic properties with CSL\(^\text{ TA }\). IEEE Trans. Software Eng. 35(2), 224–240 (2009)CrossRefGoogle Scholar
  18. 18.
    German, R.: Iterative analysis of Markov regenerative models. Perform. Eval. 44, 51–72 (2001)CrossRefGoogle Scholar
  19. 19.
    Kordon, F., & all: Complete results for the 2019 edition of the Model Checking Contest (2019). http://mcc.lip6.fr/2019/results.php
  20. 20.
    Plateau, B., Fourneau, J.M.: A methodology for solving Markov models of parallel systems. J. Parallel Distrib. Comput. 12(4), 370–387 (1991)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Dipartimento di InformaticaUniversità degli Studi di TorinoTorinoItaly
  2. 2.Department of Computer ScienceTU DortmundDortmundGermany

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