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Transient calculations on process algebra derived Markov chains

Transient calculations on process algebra derived Markov chains

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The process of obtaining transient measures from a Markov chain as implemented in the software, ipclib is described. The software accepts models written in PEPA, Bio-PEPA or as a Petri net. In the case of the process algebras, a rich query specification language particularly well suited for the derivation of passage-time quantiles is provided. Such measurements are obtained from the derived Markov chain through a process known as uniformisation. The authors detail how the process algebra and query specification language allow one to ensure that the passage-time calculation is valid and then the entire process through to the final calculation of the cumulative distribution and probability density functions of the passage in question. The authors also show a more generic transient measure for which the full probability distributions at specific times are required.

References

    1. 1)
      • Clark, A.: `The ipclib PEPA Library', Proc. Fourth Int. Conf. Quantitative Evaluation of SysTems (QEST), 2007, p. 55–56.
    2. 2)
    3. 3)
      • F. Ciocchetta , J. Hillston . Bio-PEPA: a framework for the modelling and analysis of biological systems. Theoreti. Comput. Sci. , 3065 - 3084
    4. 4)
      • Bradley, J., Dingle, N., Gilmore, S., Knottenbelt, W.: `Extracting passage times from PEPA models with the HYDRA tool: a case study', Proc. 19th Annual UK Performance Engineering Workshop, 2003, University of Warwick, p. 79–90.
    5. 5)
      • J.D. Diener , W.J. Stewart . (1995) Empirical comparison of uniformization methods for continuous-time Markov chains, Computations with Markov chains.
    6. 6)
      • Clark, G., Courtney, T., Daly, D., Deavours, D., Derisavi, S., Doyle, J.M., Sanders, W.H., Webster, P.: `The Möbius modeling tool', PNPM’01: Proc. Ninth Int. Workshop on Petri Nets and Performance Models (PNPM’01), 2001, p. 241.
    7. 7)
      • J. Hillston . (1996) A compositional approach to performance modelling.
    8. 8)
      • Dingle, N.J.: `Parallel computation of response time densities and quantiles in large markov and semi-Markov models', 2004, PhD, University of London, Department of Computing, Imperial College London.
    9. 9)
      • Clark, A., Gilmore, S.: `State-aware performance analysis with eXtended Stochastic Probes', Proc. Fifth European Performance Engineering Workshop (EPEW 2008), 2008, p. 125–140, (LNCS, 5261).
    10. 10)
    11. 11)
      • Calder, M., Gilmore, S., Hillston, J.: `Automatically deriving ODEs from process algebra models of signalling pathways', Proc. Computational Methods in Systems Biology (CMSB 2005), 2005, p. 204–215.
    12. 12)
      • Calder, M., Gilmore, S., Hillston, J.: `Modelling the influence of RKIP on the ERK signalling pathway using the stochastic process algebra PEPA', Transactions on Computational Systems Biology VII, 2006, (LNCS, 4230).
    13. 13)
      • Katoen, J.P., Zapreev, I.S.: `Safe on-the-fly steady-state detection for timebounded reachability', Quantitative Evaluation of Systems (QEST), 2006, p. 301–310.
    14. 14)
      • A. Aziz , K. Sanwal , V. Singhal , R. Brayton . Model-checking continuoustime Markov chains. ACM Trans. Comput. Logic , 1 , 162 - 170
    15. 15)
      • Kwiatkowska, M., Norman, G., Parker, D.: `PRISM 2.0: a tool for probabilistic model checking', Proc. First Int. Conf. on Quantitative Evaluation of Systems (QEST’04), 2004, p. 322–323.
    16. 16)
      • Baier, C., Haverkort, B.R., Hermanns, H., Katoen, J.P.: `Model checking continuous-time Markov chains by transient analysis', CAV’00: Proc. 12th Int. Conf. on Computer Aided Verification, 2000, p. 358–372.
    17. 17)
      • Argent-Katwala, A., Bradley, J., Clark, A., Gilmore, S.: `Location-aware quality of service measurements for service-level agreements', Proc. Third Int. Conf. on Trustworthy Global Computing (TGC’07), 2008, p. 222–239, (LNCS, 4912).
    18. 18)
      • Knottenbelt, W.J.: `Generalised Markovian analysis of timed transition systems', 1996, Master's, University of Cape Town, Department of Computer Science.
    19. 19)
      • Katoen, J.P., Khattri, M., Zapreev, I.S.: `A Markov reward model checker', Quantitative Evaluation of Systems (QEST), 2005, p. 243–244.
    20. 20)
      • Dingle, N.J., Knottenbelt, W.J., Harrison, P.G.: `HYDRA: HYpergraphbased Distributed Response-time Analyser', Proc. 2003 Int. Conf. on Parallel and Distributed Processing Techniques and Applications, 2003, 1, p. 215–219.
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