Skip to main content
SpringerLink
Log in

Performance Modeling Notations

  • Chapter
Model-Based Software Performance Analysis

  • 1032 Accesses

Abstract

A major problem for stably embedding software performance modeling and analysis within the software lifecycle resides in the distance between notations for static and dynamic modeling of software (such as UML) and notations for modeling performance (such as Queueing Networks). In Chap. 2 we have introduced the major notations for software modeling, whereas in this chapter we introduce basic performance modeling notations. A question may arise at this point from readers that are not familiar with performance analysis: “If all the performance notations are able to provide the desired indices, then why using different notations for performance modeling?”. The software performance community is still far from unifying languages and notations, although some recent efforts have been spent in the direction of building a performance ontology as a shared vocabulary of the domain (see Chap. 7). The performance notations that we describe in this chapter are well described in the literature and many references can be found. Although more sophisticated notations have been introduced, most of them build up over the basic notations that are described in this chapter.

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

Access this chapter

Log in via an institution
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 EPUB and 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
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover 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

Notes

  1. 1.

    Several seminal books have been published on stochastic and Markov processes (see, for example, [49]). The description that follows, for sake of synthesis and readability, has been taken by Jane Hillston’s class notes [3].

  2. 2.

    This holds under the assumption of continuous time, whereas for discrete-time Markov processes the sojourn time is geometrically distributed.

  3. 3.

    Note that this is a prime example of structure that links (nowadays called) a Platform Independent Model to a Platform Specific Model.

  4. 4.

    We describe the remainder of this model by using the OP pattern, as the internal dynamics of the three use cases is structurally the same.

  5. 5.

    For more details on the domain model please refer to [85].

References

  1. C++SIM. http://cxxsim.ncl.ac.uk/

  2. CSIM-performance simulator. http://www.atl.imco.com/proj/csim

  3. Jane Hillston’s notes of lectures: Private communication

    Google Scholar 

  4. JavaSim. http://javasim.ncl.ac.uk/

  5. Ajmone, M., Balbo, G., Conte, G.: A class of generalised stochastic petri nets for the performance evaluation of multiprocessor systems. ACM Transactions on Computer Systems 2, 93–122 (1984)

    Article  Google Scholar 

  6. Ajmone, M., Balbo, G., Conte, G.: Performance Models of Multiprocessor Performance. The MIT Press, Cambridge (1986)

    Google Scholar 

  7. Baccelli, F., Balbo, G., Boucherie, R.J., Campos, J.J., Chiola, G.: Annotated bibliography on stochastic petri nets. In: Tract, C. (ed.) Performance Evaluation of Parallel and Distributed Systems-Solution Methods, Amsterdam, 1994, pp. 1–24 (1994)

    Google Scholar 

  8. Baeten, J.C.M., Weijland, W.P.: Process Algebra. Cambridge University Press, Cambridge (1990)

    Book  Google Scholar 

  9. Balsamo, S., Di Marco, A., Inverardi, P., Simeoni, M.: Model-based performance prediction in software development: A survey. IEEE Transactions of Software Engineering 30(5), 295–310 (2004)

    Article  Google Scholar 

  10. Banks, J., Carson, J.S., Nelson, B.L., Nicol, D.M.: Discrete-Event System Simulation. Pearson Prentice Hall, Upper Saddle River (2004)

    Google Scholar 

  11. Beilner, H., Matter, J., Wysocki, C.: The hierarchical evaluation tool HIT. In: Proceedings of the International Conference on Modelling Techniques and Tools for Computer Performance Evaluation, Wien, 1994

    Google Scholar 

  12. Bernardo, M., Bravetti, M.: Performance measurement sensitive congruences for Markovian process algebras. Theoretical Computer Science 290, 117–160 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  13. Brinksma, E., Hermanns, H., Katoen, J.-P. (eds.): Lectures on Formal Methods and Performance Analysis, First EEF/Euro Summer School on Trends in Computer Science, The Netherlands, 2001. Lecture Notes in Computer Science, vol. 2090. Springer, Berlin (2001)

    Google Scholar 

  14. Doob, J.L.: Stochastic Processes. John Wiley and Sons, New York (1953)

    MATH  Google Scholar 

  15. Ephraim, Y., Merhav, N.: Hidden Markov processes. IEEE Transactions on Information Theory 48(6), 1518–1569 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  16. Franks, G., Hubbard, A., Majumdar, S., Petriu, D.C., Rolia, J., Woodside, C.M.: A toolset for performance engineering and software design of client-server systems. Performance Evaluation 24(1–2), 117–135 (1995)

    Article  MATH  Google Scholar 

  17. Franks, R., Woodside, C.M.: Performance of multi-level client-server systems with parallel service operations. In: ACM Proceedings of the First Workshop on Software and Performance (WOSP98), Santa Fe, New Mexico, pp. 120–130 (1998)

    Chapter  Google Scholar 

  18. Hermanns, H., Herzog, U., Katoen, J.P.: Process algebra for performance evaluation. Theoretical Computer Science 274(1–2), 43–87 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  19. Hillston, J.: PEPA – performance enhanced process algebra. Technical report, Dept. of Computer Science, University of Edinburgh (1993)

    Google Scholar 

  20. Kant, K.: Introduction to Computer System Performance Evaluation. McGraw-Hill, New York (1992)

    Google Scholar 

  21. Kleinrock, L.: Queueing Systems, vol. 1: Theory. Wiley, New York (1975)

    Google Scholar 

  22. Lazowska, E.D., Kahorjan, J., Graham, G.S., Sevcik, K.C.: Quantitative System Performance: Computer System Analysis Using Queueing Network Models. Prentice-Hall, Englewood Cliffs (1984)

    Google Scholar 

  23. Object Management Group: UML profile, for schedulability, performance, and time. OMG document ptc/2002-03-02. http://www.omg.org/cgi-bin/doc?ptc/2002-03-02

  24. Pooley, R.J.: An Introduction to Programming in SIMULA. Blackwell Scientific Publications, Oxford (1987)

    MATH  Google Scholar 

  25. Reisig, W.: Petri Nets: An Introduction. EATCS Monographs on Theoretical Computer Science, vol. 4 (1985)

    Google Scholar 

  26. Rolia, J.A., Sevcik, K.C.: The method of layers. IEEE Transaction on Software Engineering 21(8), 622–688 (1995)

    Article  Google Scholar 

  27. Sauer, C.H., MacNair, E.A.: Queueing network software for systems modeling. In: Research Report RC-7143, IBM Thomas J. Watson Research Center, Yorktown Heights (1978)

    Google Scholar 

  28. Sauer, C.H., Reiser, M., MacNair, E.A.: RESQ – a package for solution of generalized queueing networks. In: Proceedings, National Computer Conference, Dallas, TX, 1977, pp. 977–986 (1977)

    Google Scholar 

  29. Smith, C.U., Williams, L.G.: Performance Solutions: A Practical Guide to Creating Responsive, Scalable Software. Addison-Wesley, Reading (2002)

    Google Scholar 

  30. Tijms, H.C.: Stochastic Models, An Algorithmic Approach. John Wiley and Sons Ltd, New York (1994)

    MATH  Google Scholar 

  31. Trivedi, K.S.: Probability and Statistics with Reliability, Queueing and Computer Science Applications. John Wiley and Sons, New York (2001)

    Google Scholar 

  32. Veran, M., Potier, D.: QNAP 2: a portable environment for queueing systems modelling. In: Rapport de recherche de l’INRIA-Rocquencourt. http://www.inria.fr/rrrt/rr-0314.html (1984)

    Google Scholar 

  33. Woodside, C.M., Neilson, J., Petriu, S., Mjumdar, S.: The stochastic rendezvous network model for performance of synchronous client-server-like distributed software. IEEE Transaction on Computer 44, 20–34 (1995)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Università di L’Aquila, Via Vetoio 1, Coppito, 67010, L’Aquila, Italy

    Vittorio Cortellessa, Antinisca Di Marco & Paola Inverardi

Authors
  1. Vittorio Cortellessa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antinisca Di Marco
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paola Inverardi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vittorio Cortellessa .

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Cortellessa, V., Di Marco, A., Inverardi, P. (2011). Performance Modeling Notations. In: Model-Based Software Performance Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13621-4_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13621-4_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13620-7

  • Online ISBN: 978-3-642-13621-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation