European Workshop on Performance Engineering

EPEW 2015: Computer Performance Engineering pp 208-224 | Cite as

Computing Response Time Distributions Using Iterative Probabilistic Model Checking

  • Freek van den Berg
  • Jozef Hooman
  • Arnd Hartmanns
  • Boudewijn R. Haverkort
  • Anne Remke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9272)

Abstract

System designers need to have insight in the response times of service systems to see if they meet performance requirements. We present a high-level evaluation technique to obtain the distribution of services completion times. It is based on a high-level domain-specific language that hides the underlying technicalities from the system designer. Under the hood, probabilistic real-time model checking technology is used iteratively to obtain precise bounds and probabilities. This allows reasoning about nondeterministic, probabilistic and real-time aspects in a single evaluation. To reduce the state spaces for analysis, we use two sampling methods (for measurements) that simplify the system model: (i) applying an abstraction on time by increasing the length of a (discrete) model time unit, and (ii) computing only absolute bounds by replacing probabilistic choices with non-deterministic ones. We use an industrial case on image processing of an interventional X-ray system to illustrate our approach.

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References

  1. 1.
    Beilner, H., Mater, J., Weissenberg, N.: Towards a performance modelling environment: news on HIT. In: Modeling Techniques and Tools for Computer Performance Evaluation, pp. 57–75. Plenum Press (1989)Google Scholar
  2. 2.
    van den Berg, F., Remke, A., Haverkort, B.R.: A domain specific language for performance evaluation of medical imaging systems. In: 5th Workshop on Medical Cyber-Physical Systems, pp. 80–93. Schloss Dagstuhl (2014)Google Scholar
  3. 3.
    van den Berg, F., Remke, A., Haverkort, B.: iDSL: Automated performance prediction and analysis of medical imaging systems. In: Computer Performance Engineering, LNCS, vol. 9272. Springer (2015) (to appear)Google Scholar
  4. 4.
    van den Berg, F., Remke, A., Mooij, A., Haverkort, B.: Performance evaluation for collision prevention based on a domain specific language. In: Balsamo, M.S., Knottenbelt, W.J., Marin, A. (eds.) EPEW 2013. LNCS, vol. 8168, pp. 276–287. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  5. 5.
    Grottke, M., Apte, V., Trivedi, K., Woolet, S.: Response time distributions in networks of queues. In: Queueing Networks, pp. 587–641. Springer (2011)Google Scholar
  6. 6.
    Hahn, E., Hartmanns, A., Hermanns, H., Katoen, J.P.: A compositional modelling and analysis framework for stochastic hybrid systems. Formal Methods in System Design 43(2), 191–232 (2012)CrossRefMATHGoogle Scholar
  7. 7.
    Hartmanns, A., Hermanns, H.: The modest toolset: an integrated environment for quantitative modelling and verification. In: Ábrahám, E., Havelund, K. (eds.) TACAS 2014 (ETAPS). LNCS, vol. 8413, pp. 593–598. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  8. 8.
    Haveman, S., Bonnema, G., van den Berg, F.: Early insight in systems design through modeling and simulation. Procedia Computer Science 28, 171–178 (2014)CrossRefGoogle Scholar
  9. 9.
    Jain, R.: The Art of Computer Systems Performance Analysis. John Wiley & Sons (1991)Google Scholar
  10. 10.
    Johnson, J.: Designing with the Mind in Mind: Simple Guide to Understanding User Interface Design Rules. Morgan Kaufmann (2010)Google Scholar
  11. 11.
    Kienhuis, B., Deprettere, E.F., van der Wolf, P., Vissers, K.: A methodology to design programmable embedded systems. In: Deprettere, F., Teich, J., Vassiliadis, S. (eds.) SAMOS 2001. LNCS, vol. 2268, pp. 18–37. Springer, Heidelberg (2002) CrossRefGoogle Scholar
  12. 12.
    Kontogiannis, K., Lewis, G., Smith, D. and Litoiu, M., Muller, H., Schuster, S., Stroulia, E.: The landscape of service-oriented systems: a research perspective. In: Proceedings of the International Workshop on Systems Development in SOA Environments, p. 1. IEEE Computer Society (2007)Google Scholar
  13. 13.
    Kwiatkowska, M., Norman, G., Parker, D.: PRISM 4.0: verification of probabilistic real-time systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 585–591. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  14. 14.
    Kwiatkowska, M., Norman, G., Segala, R., Sproston, J.: Automatic verification of real-time systems with discrete probability distributions. Theor. Comput. Sci. 282(1), 101–150 (2002)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Philippou, A., Georghiou, C., Philippou, G.: A generalized geometric distribution and some of its properties. Statistics & Probability Letters 1(4), 171–175 (1983)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Puschner, P., Burns, A.: Guest editorial: A review of worst-case execution-time analysis. Real-Time Systems 18(2–3), 115–128 (2000)CrossRefGoogle Scholar
  17. 17.
    Wandeler, E., Thiele, L., Verhoef, M., Lieverse, P.: System architecture evaluation using modular performance analysis: a case study. International Journal on Software Tools for Technology Transfer 8(6), 649–667 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Freek van den Berg
    • 1
  • Jozef Hooman
    • 2
  • Arnd Hartmanns
    • 3
  • Boudewijn R. Haverkort
    • 1
  • Anne Remke
    • 4
  1. 1.University of TwenteEnschedeThe Netherlands
  2. 2.Radboud University, Nijmegen & TNO-ESIEindhovenThe Netherlands
  3. 3.Saarland UniversitySaarbrückenGermany
  4. 4.Westfälische Wihlhems-UniversitätMünsterGermany

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