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A Structured Solution Approach for Markov Regenerative Processes

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8657))

Abstract

Two different methods have been introduced in the past for the numerical analysis of Markov Regenerative Processes. The first one generates the embedded Markov chain explicitly and solves afterwards the often dense system of linear equations. The second method avoids computation of the embedded Markov chain by performing a transient analysis in each step. This method is called “matrix free” and it is often more efficient in memory and time. In this paper we go one step further by even avoiding the storage of the generator matrices required by the matrix-free method, thanks to the use of a Kronecker representation.

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References

  1. Ajmone Marsan, M., Chiola, G.: On Petri nets with deterministic and exponentially distributed firing times. In: Rozenberg, G. (ed.) APN 1987. LNCS, vol. 266, pp. 132–145. Springer, Heidelberg (1987)

    Chapter  Google Scholar 

  2. Amparore, E.G., Donatelli, S.: DSPN-Tool: a new DSPN and GSPN solver for GreatSPN. In: QEST 2010, pp. 79–80 (2010)

    Google Scholar 

  3. Amparore, E.G., Donatelli, S.: Revisiting matrix-free solution of Markov regenerative processes. Numerical Linear Algebra with Applications 18(6), 1067–1083 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  4. Balbo, G., Beccuti, M., De Pierro, M., Franceschinis, G.: First Passage Time Computation in Tagged GSPNs with Queue Places. The Computer Journal (2010)

    Google Scholar 

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

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

    Article  MathSciNet  Google Scholar 

  7. Buchholz, P., Ciardo, G., Donatelli, S., Kemper, P.: Complexity of memory-efficient Kronecker operations with applications to the solution of Markov models. INFORMS Journal on Computing 12(3), 203–222 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  8. Buchholz, P., Kemper, P.: Hierarchical reachability graph generation for Petri nets. Formal Methods in System Design 21(3), 281–315 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  9. Choi, H., Kulkarni, V.G., Trivedi, K.S.: Markov regenerative stochastic Petri nets. Performance Evaluation 20(1-3), 337–357 (1994)

    Article  MathSciNet  Google Scholar 

  10. Ciardo, G., Lindemann, C.: Analysis of Deterministic and Stochastic Petri Nets. In: PNPM 1993, pp. 160–169. IEEE Computer Society (1993)

    Google Scholar 

  11. Ciardo, G., Lüttgen, G., Siminiceanu, R.: Saturation: An efficient iteration strategy for symbolic state-space generation. In: Margaria, T., Yi, W. (eds.) TACAS 2001. LNCS, vol. 2031, pp. 328–342. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  12. German, R.: Iterative analysis of Markov regenerative models. Perform. Eval. 44(1-4), 51–72 (2001)

    Article  MATH  Google Scholar 

  13. 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 

  14. Saad, Y., Schultz, M.H.: GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems. SIAM Journal on Scientific and Statistical Computing 7(3), 856–869 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  15. Stewart, W.J.: Introduction to the numerical solution of Markov chains. Princeton University Press (1994)

    Google Scholar 

  16. Vicario, E., Sassoli, L., Carnevali, L.: Using stochastic state classes in quantitative evaluation of dense-time reactive systems. IEEE Transactions on Software Engineering 35(5), 703–719 (2009)

    Article  Google Scholar 

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Amparore, E.G., Buchholz, P., Donatelli, S. (2014). A Structured Solution Approach for Markov Regenerative Processes. In: Norman, G., Sanders, W. (eds) Quantitative Evaluation of Systems. QEST 2014. Lecture Notes in Computer Science, vol 8657. Springer, Cham. https://doi.org/10.1007/978-3-319-10696-0_3

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  • DOI: https://doi.org/10.1007/978-3-319-10696-0_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10695-3

  • Online ISBN: 978-3-319-10696-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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