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Higher Moment Analysis of a Spatial Stochastic Process Algebra

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Computer Performance Engineering (EPEW 2011)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 6977))

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Abstract

We introduce a spatial stochastic process algebra called MASSPA, which provides a formal behavioural description of Markovian Agent Models, a spatial stochastic modelling framework. We provide a translation to a master equation which governs the underlying transition behaviour. This provides a means of simulation and thus comparison of numerical results with simulation that was previously not available. On the theoretical side, we develop a higher moment analysis to allow quantities such as variance to be produced for spatial stochastic models in performance analysis for the first time. We compare the simulation results against resulting ODEs for both mean and standard deviations of model component counts and finish by analysing a distributed wireless sensor network model.

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References

  1. Massink, M., Latella, D., Bracciali, A., Hillston, J.: Modelling Crowd Dynamics in Bio-PEPA. In: Proceedings of the 9th Workshop PASTA/Bio-PASTA, pp. 1–11 (2010)

    Google Scholar 

  2. Bracciali, A., Hillston, J., Latella, D., Massink, M.: Reconciling Population and Agent Models for Crowd Dynamics. In: 3rd International Workshop on Logics, Agents, and Mobility, LAM 2010 (2010)

    Google Scholar 

  3. Dieckmann, U., Law, R.: Relaxation projections and the method of moments. Cambridge studies in adaptive dynamics, ch. 21, pp. 412–455. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  4. Stefanek, A., Vigliotti, M., Bradley, J.T.: Spatial extension of stochastic pi calculus. In: 8th Workshop on Process Algebra and Stochastically Timed Activities, pp. 109–117 (2009)

    Google Scholar 

  5. Gribaudo, M., Cerotti, D., Bobbio, A.: Analysis of on-off policies in sensor networks using interacting markovian agents. In: 6th IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 300–305 (2008)

    Google Scholar 

  6. Cerotti, D., Gribaudo, M., Bobbio, A., Calafate, C.T., Manzoni, P.: A markovian agent model for fire propagation in outdoor environments. In: Aldini, A., Bernardo, M., Bononi, L., Cortellessa, V. (eds.) EPEW 2010. LNCS, vol. 6342, pp. 131–146. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  7. Cerotti, D., Gribaudo, M., Bobbio, A.: Presenting Dynamic Markovian Agents with a road tunnel application. In: IEEE International Symposium on Modeling Analysis Simulation of Computer and Telecommunication Systems MASCOTS, pp. 1–4. IEEE, Los Alamitos (2009)

    Google Scholar 

  8. Galpin, V.: Towards a spatial stochastic process algebra. In: Proceedings of the 7th Workshop on Process Algebra and Stochastically Timed Activities (PASTA), Edinburgh (2008)

    Google Scholar 

  9. Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. Journal of Physical Chemistry 81(25), 2340–2361 (1977)

    Article  Google Scholar 

  10. Hillston, J.: Fluid flow approximation of PEPA models. In: Second International Conference on the Quantitative Evaluation of Systems QEST 2005, pp. 33–42 (2005)

    Google Scholar 

  11. Hayden, R.A., Bradley, J.T.: A fluid analysis framework for a Markovian process algebra. Theoretical Computer Science 411(22-24), 2260–2297 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  12. Hayden, R.A., Stefanek, A., Bradley, J.T.: Fluid computation of passage time distributions in large Markov models. Theoretical Computer Science (submitted, 2010)

    Google Scholar 

  13. Engblom, S.: Computing the moments of high dimensional solutions of the master equation. Applied Mathematics and Computation 180(2), 498–515 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  14. Van Kampen, N.G.: Stochastic Processes in Physics and Chemistry. North-Holland personal library, vol. 11. North-Holland, Amsterdam (1992)

    MATH  Google Scholar 

  15. Bobbio, A., Gribaudo, M., Telek, M.: Mean Field Methods in Performance Analysis. In: Fifth International Conference on Quantitative Evaluation of Systems, QEST, 2008, pp. 215–224 (2008)

    Google Scholar 

  16. Hillston, J.: A Compositional Approach to Performance Modelling, p. 158. Cambridge University Press, Cambridge (1996)

    Book  Google Scholar 

  17. Le Boudec, J.-Y., McDonald, D., Mundinger, J.: A Generic Mean Field Convergence Result for Systems of Interacting Objects. In: Fourth International Conference on the Quantitative Evaluation of Systems QEST 2007, pp. 3–18 (2007)

    Google Scholar 

  18. Murrell, D.J., Dieckmann, U., Law, R.: On moment closures for population dynamics in continuous space. Journal of Theoretical Biology 229(3), 421–432 (2004)

    Article  Google Scholar 

  19. Cerotti, D.: Interacting Markovian Agents. PhD thesis, University of Torino (2010)

    Google Scholar 

  20. Bortolussi, L.: On the Approximation of Stochastic Concurrent Constraint Programming by Master Equation. Electronic Notes in Theoretical Computer Science 220(3), 163–180 (2008)

    Article  MATH  Google Scholar 

  21. Gillespie, C.S.: Moment-closure approximations for mass-action models. IET Systems Biology 3(1), 52–58 (2009)

    Article  MathSciNet  Google Scholar 

  22. Guenther, M.C., Bradley, J.T.: Higher moment analysis of a spatial stochastic process algebra. Tech. rep., Imperial College London (July 2011), http://pubs.doc.ic.ac.uk/masspa-higher-moments/

  23. Milner, R.: Communicating and Mobile Systems: the Pi-Calculus. Cambridge University Press, Cambridge (1999)

    MATH  Google Scholar 

  24. Galpin, V.: Modelling network performance with a spatial stochastic process algebra. In: Proceedings of the 23rd IEEE International Conference on Advanced Information Networking and Applications (AINA 2009), Bradford, pp. 41–49 (May 2009)

    Google Scholar 

  25. Ovaskainen, O., Cornell, S.J.: Space and stochasticity in population dynamics. Proceedings of the National Academy of Sciences of the United States of America 103(34), 12781–12786 (2006)

    Article  Google Scholar 

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Guenther, M.C., Bradley, J.T. (2011). Higher Moment Analysis of a Spatial Stochastic Process Algebra. In: Thomas, N. (eds) Computer Performance Engineering. EPEW 2011. Lecture Notes in Computer Science, vol 6977. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24749-1_8

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  • DOI: https://doi.org/10.1007/978-3-642-24749-1_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24748-4

  • Online ISBN: 978-3-642-24749-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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