Advertisement

Implementing a Stochastic Process Algebra within the Möbius Modeling Framework

  • Graham Clark
  • William H. Sanders
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2165)

Abstract

Many formalisms and solution methods exist for performance and dependability modeling. However, different formalisms have different advantages and strengths, and no one formalism is universally used. The Möbius tool was built to provide multi-formalism multi-solution modeling, and allows the modeler to develop models in any supported formalism. A formalism can be implemented in Möbius if a mapping can be provided to the Möbius Abstract Functional Interface, which includes a notion of state and a notion of how state changes over time. We describe a way to map PEPA, a stochastic process algebra, to the abstract functional interface. This gives Mobius users the opportunity to make use of stochastic process algebra models in their performance and dependability models.

Keywords

Process Algebra Equivalence Sharing Sequential Component Large State Space Partner Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J. M. Doyle, “Abstract model specification using the Möbius modeling tool,” M.S. thesis, University of Illinois at Urbana-Champaign, 2000.Google Scholar
  2. 2.
    G. Clark, T. Courtney, D. Daly, D. Deavours, S. Derisavi, J. M. Doyle, W. H. Sanders, and P. Webster, “ The Mobius modeling tool,” in Proc. of PNPM’01: 10th International Workshop on Petri Nets and Performance Models, Aachen, Germany (to appear), September 2001.Google Scholar
  3. 3.
    J. F. Meyer, A. Movaghar, and W. H. Sanders, “Stochastic activity networks: Structure, behavior and applications,” Proc. International Workshop on Timed Petri Nets, pp. 106–115, 1985.Google Scholar
  4. 4.
    J. Hillston, A Compositional Approach to Performance Modelling, Cambridge University Press, 1996.Google Scholar
  5. 5.
    W. H. Sanders, “Integrated frameworks for multi-level and multi-formalism modeling,” in Proc. PNPM’99: 8th International Workshop on Petri Nets and Performance Models, Zaragoza, Spain, September 1999, pp. 2–9.Google Scholar
  6. 6.
    D. Deavours and W. H. Sanders, “Mobius: Framework and atomic models,” in Proc. PNPM’01: 10th International Workshop on Petri Nets and Performance Models, Aachen, Germany (to appear), September 2001.Google Scholar
  7. 7.
    W. H. Sanders, W. D. Obal II, M. A. Qureshi, and F. K. Widjanarko, “The ultrasan modeling environment,” Performance Evaluation, vol. 24, No. 1, pp. 89–115, October–November 1995.zbMATHCrossRefGoogle Scholar
  8. 8.
    W. H. Sanders and J. F. Meyer, “Reduced base model construction methods for stochastic activity networks, ” IEEE Journal on Selected Areas in Communications, vol. 9, No. 1, pp. 25–36, Jan. 1991.CrossRefGoogle Scholar
  9. 9.
    D. Montgomery, Design and Analysis of Experiments, John Wiley & Sons, Inc., 5th edition, 2001.Google Scholar
  10. 10.
    R. Milner, Communication and Concurrency, International Series in Computer Science. Prentice Hall, 2nd edition, 1989.Google Scholar
  11. 11.
    T. Bolognesi and E. Brinksma, “Introduction to the ISO specification language LOTOS,” Computer Networks and ISDN Systems, vol. 14, pp. 25–59, 1987.CrossRefGoogle Scholar
  12. 12.
    H. Hermanns and M. Rettelbach, “Toward a superset of basic LOTOS for performance prediction,” Proc. of 4th Workshop on Process Algebras for Performance Modelling (PAPM), pp. 77–94, 1996.Google Scholar
  13. 13.
    M. Bernardo, Theory and Application of Extended Markovian Process Algebra, Ph.D. thesis, University of Bologna, Italy, 1999.Google Scholar
  14. 14.
    S. Gilmore, J. Hillston, and M. Ribaudo, “An efficient algorithm for aggregating PEPA models,” IEEE Transactions on Software Engineering, 2001.Google Scholar
  15. 15.
    J. Hillston, “The nature of synchronisation,” Proc. of 2nd Workshop on Process Algebras for Performance Modelling (PAPM), pp. 51–70, 1994.Google Scholar
  16. 16.
    S. Donatelli, “Superposed generalized stochastic Petri nets: Definition and efficient solution,” in Application and Theory of Petri Nets 1994, Lecture Notes in Computer Science 815 (Proc. 15th International Conference on Application and Theory of Petri Nets, Zaragoza, Spain), R. Valette, Ed., pp. 258–277. Springer-Verlag, June 1994.Google Scholar
  17. 17.
    I. Rojas, Compositional Construction of SWN Models, Ph.D. thesis, The University of Edinburgh, 1997.Google Scholar
  18. 18.
    E. Best, R. Devillers, and M. Koutny, Petri Net Algebra, Monographs in Theoretical Computer Science. An EATCS Series. Springer-Verlag, 2000.Google Scholar
  19. 19.
    W. D. Obal II, Measure-Adaptive State-Space Construction Methods, Ph.D. thesis, The University of Arizona, 1998.Google Scholar
  20. 20.
    W. J. Stewart, Introduction to the Numerical Solution of Markov Chains, Princeton University Press, 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Graham Clark
    • 1
  • William H. Sanders
    • 1
  1. 1.Dept. of Electrical and Computer Engineering and Coordinated Science LaboratoryUniversity of Illinois at Urbana-ChampaignUrbanaUSA

Personalised recommendations