Skip to main content

A Component-Based Solution Method for Non-ergodic Markov Regenerative Processes

  • Conference paper
Book cover Computer Performance Engineering (EPEW 2010)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 6342))

Included in the following conference series:

Abstract

This paper presents a new technique for the steady state solution of non-ergodic Markov Regenerative Processes (MRP), based on a structural decomposition of the MRP. Each component may either be a CTMC or a (smaller) MRP. Classical steady state solution methods of MRP are based either on the computation of the embedded Markov chain (EMC) defined over regenerative states, leading to high complexity in time and space (since the EMC is usually dense), or on an iterative scheme that does not require the construction of the EMC.

The technique presented is particularly suited for MRPs that exhibit a semi-sequential structure. In this paper we present the new algorithm, its asymptotic complexity, and its performance in comparison with classical MRP techniques. Results are very encouraging, even when the MRP only loosely exhibits the required semi-sequential structure.

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

Access this chapter

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Amparore, E., Donatelli, S.: DSPN-Tool: a new DSPN and GSPN solver for GreatSPN. In: Tool Demo Presentation Accepted at QEST 2010, Williamsburg, USA, September 15-18. IEEE-CS Press, Los Alamitos (2010)

    Google Scholar 

  2. Amparore, E., Donatelli, S.: MC4CSLTA: an efficient model checking tool for CSLTA. In: Tool Demo Presentation Accepted at QEST 2010, Williamsburg, USA, September 15-18, IEEE-CS Press, Los Alamitos (2010)

    Google Scholar 

  3. Amparore, E., Donatelli, S.: Revisiting the Iterative Solution of Markov Regenerative Processes. In: NSMC-2010, Williamsburg, USA (submitted 2010)

    Google Scholar 

  4. 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 

  5. Ciardo, G., Lindemann, C.: Analysis of Deterministic and Stochastic Petri Nets. In: Performance Evaluation, pp. 160–169. IEEE Computer Society, Los Alamitos (1993)

    Google Scholar 

  6. Cormen, T.H., Stein, C., Rivest, R.L., Leiserson, C.E.: Introduction to Algorithms. McGraw-Hill Higher Education, New York (2001)

    MATH  Google Scholar 

  7. Donatelli, S., Haddad, S., Sproston, J.: Model checking timed and stochastic properties with CSLTA. IEEE Trans. Softw. Eng. 35(2), 224–240 (2009)

    Article  Google Scholar 

  8. German, R.: Performance Analysis of Communication Systems with Non-Markovian Stochastic Petri Nets. John Wiley & Sons, Inc., New York (2000)

    MATH  Google Scholar 

  9. German, R.: Iterative analysis of Markov regenerative models. Perform. Eval. 44, 51–72 (2001), http://portal.acm.org/citation.cfm?id=371601.371606

    Article  MATH  Google Scholar 

  10. Lindemann, C.: Performance Modelling with Deterministic and Stochostic Petri Nets. John Wiley & Sons, Inc., New York (1998)

    MATH  Google Scholar 

  11. 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 

  12. Mura, I., Bondavalli, A., Zang, X., Trivedi, K.S.: Dependability modeling and evaluation of phased mission systems: a dspn approach. In: IEEE DCCA-7 - 7th IFIP Int. Conference on Dependable Computing for Critical Applications, pp. 299–318. IEEE Computer Society Press, Los Alamitos (1999)

    Google Scholar 

  13. Stewart, W.J.: Introduction to the Numerical Solution of Markov Chains. Princeton University Press, Princeton (1994)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Amparore, E.G., Donatelli, S. (2010). A Component-Based Solution Method for Non-ergodic Markov Regenerative Processes. In: Aldini, A., Bernardo, M., Bononi, L., Cortellessa, V. (eds) Computer Performance Engineering. EPEW 2010. Lecture Notes in Computer Science, vol 6342. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15784-4_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15784-4_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15783-7

  • Online ISBN: 978-3-642-15784-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics