Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Quantitative Evaluation of Systems (QEST 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11785))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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)

    Article  Google Scholar 

  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. 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_13

    Chapter  Google Scholar 

  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_9

    Chapter  Google Scholar 

  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_3

    Chapter  Google Scholar 

  6. Amparore, E.G., Donatelli, S.: Revisiting the matrix-free solution of Markov regenerative processes. Numer. Linear Algebra Appl. 18, 1067–1083 (2011)

    Article  MathSciNet  Google Scholar 

  7. Amparore, E.G., Donatelli, S.: A component-based solution for reducible Markov regenerative processes. Perform. Eval. 70(6), 400–422 (2013)

    Article  Google Scholar 

  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_3

    Chapter  Google Scholar 

  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_19

    Chapter  Google Scholar 

  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_32

    Chapter  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  12. Buchholz, P.: Markov matrix market. http://ls4-www.cs.tu-dortmund.de/download/buchholz/struct-matrix-market.html

  13. Buchholz, P.: Hierarchical structuring of superposed GSPNs. IEEE Trans. Softw. Eng. 25(2), 166–181 (1999)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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_8

    Chapter  Google Scholar 

  16. Choi, H., Kulkarni, V.G., Trivedi, K.S.: Markov regenerative stochastic Petri nets. Perform. Eval. 20(1–3), 337–357 (1994)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  18. German, R.: Iterative analysis of Markov regenerative models. Perform. Eval. 44, 51–72 (2001)

    Article  Google Scholar 

  19. Kordon, F., & all: Complete results for the 2019 edition of the Model Checking Contest (2019). http://mcc.lip6.fr/2019/results.php

  20. Plateau, B., Fourneau, J.M.: A methodology for solving Markov models of parallel systems. J. Parallel Distrib. Comput. 12(4), 370–387 (1991)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Torino, Torino, Italy

    Elvio Gilberto Amparore & Susanna Donatelli

  2. Department of Computer Science, TU Dortmund, Dortmund, Germany

    Peter Buchholz

Authors
  1. Elvio Gilberto Amparore
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter Buchholz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Susanna Donatelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Elvio Gilberto Amparore , Peter Buchholz or Susanna Donatelli .

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    David Parker

  2. Saarland University, Saarbrücken, Germany

    Verena Wolf

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Amparore, E.G., Buchholz, P., Donatelli, S. (2019). Great-Nsolve: A Tool Integration for (Markov Regenerative) Stochastic Petri Nets. In: Parker, D., Wolf, V. (eds) Quantitative Evaluation of Systems. QEST 2019. Lecture Notes in Computer Science(), vol 11785. Springer, Cham. https://doi.org/10.1007/978-3-030-30281-8_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-30281-8_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30280-1

  • Online ISBN: 978-3-030-30281-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation