Skip to main content
SpringerLink
Log in
Book cover

International Conference on System Analysis and Modeling

SAM 2018: System Analysis and Modeling. Languages, Methods, and Tools for Systems Engineering pp 105–126Cite as

Enabling Performance Modeling for the Masses: Initial Experiences

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11150))

Abstract

Performance problems such as sluggish response time or low throughput are especially annoying, frustrating and noticeable to users. Fixing performance problems after they occur results in unplanned expenses and time. Our vision is an MDE-intensive software development paradigm for complex systems in which software designers can evaluate performance early in development, when the analysis can have the greatest impact. We seek to empower designers to do the analysis themselves by automating the creation of performance models out of standard design models. Such performance models can be automatically solved, providing results meaningful to them. In our vision, this automation can be enabled by using model-to-model transformations: First, designers create UML design models embellished with the Modeling and Analysis of Real Time and Embedded systems (MARTE) design specifications; and secondly, such models are transformed to automatically solvable performance models by using QVT. This paper reports on our first experiences when implementing these two initial activities.

This is a preview of subscription content, 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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    From this point on, we will use the generic term system designers to refer to any stakeholder taking advantage of our approach.

  2. 2.

    We have indicated with a gray background the activities that are different from those in Fig. 1.

  3. 3.

    Allocate can only be applied to Abstractions, which are a specific kind of UML Dependency.

  4. 4.

    In fact, the QVT specification defines three transformation languages: Core, Operational and Relations, being the main difference among them their declarative or imperative nature.

  5. 5.

    Sensor here represents the software element used to access hardware Sensors.

  6. 6.

    We obtained processing times and data/network transfer bytes specified in Figs. 4 and 5 from the analysis of benchmark data.

  7. 7.

    We based the encryption and decryption on an open source version of the Advanced Encryption Standard (AES) [8].

  8. 8.

    See http://issues.omg.org/issues/MARTE12-4.

References

  1. Balsamo, S., Di Marco, A., Inverardi, P., Simeoni, M.: Model-based performance prediction in software development: a survey. IEEE Trans. Softw. Eng. 30(5), 295–310 (2004)

    Article  Google Scholar 

  2. Becker, S., Koziolek, H., Reussner, R.: The palladio component model for model-driven performance prediction. J. Syst. Softw. 82(1), 3–22 (2009)

    Article  Google Scholar 

  3. Bernardi, S., et al.: A systematic approach for performance assessment using process mining. Empir. Softw. Eng. (2018). https://doi.org/10.1007/s10664-018-9606-9

  4. Bernardi, S., Merseguer, J., Petriu, D.C.: Dependability modeling and analysis of software systems specified with UML. ACM Comput. Surv. 45(1), 1–48 (2012)

    Article  Google Scholar 

  5. Celonis PI (2011). https://www.celonis.com. Accessed June 2018

  6. Consortium, D.: Getting started with DICE: developing data-intensive cloud applications with iterative quality enhancements (2018). http://www.dice-h2020.eu/getting-started/. Accessed June 2018

  7. Cortellessa, V., Marco, A.D., Inverardi, P.: Model-Based Software Performance Analysis, 1st edn. Springer Publishing Company, Incorporated (2011)

    Book  Google Scholar 

  8. Daemen, J., Rijmen, V.: The Design of Rijndael. Springer-Verlag New York Inc., Secaucus (2002)

    Book  Google Scholar 

  9. Demathieu, S.: MARTE tutorial: An OMG UML profile to develop Real-Time and Embedded systems. http://www.uml-sysml.org/documentation/marte-tutorial-713-ko/at_download/file. Accessed June 2018

  10. Di Ruscio, D., Paige, R.F., Pierantonio, A.: Guest editorial to the special issue on success stories in model driven engineering. Sci. Comput. Program. 89(PB), 69–70 (2014). https://doi.org/10.1016/j.scico.2013.12.006

    Article  Google Scholar 

  11. Diwan, A., Hauswirth, M., Mytkowicz, T., Sweeney, P.F.: TraceAnalyzer: a system for processing performance traces. Softw. Pract. Exp. 41(3), 267–282 (2011)

    Google Scholar 

  12. Günther, C.W., Rozinat, A.: Disco: discover your processes. BPM (Demos) 940, 40–44 (2012)

    Google Scholar 

  13. Huber, N., Brosig, F., Spinner, S., Kounev, S., Bähr, M.: Model-based self-aware performance and resource management using the descartes modeling language. IEEE Trans. Softw. Eng. 43(5), 432–452 (2017)

    Article  Google Scholar 

  14. JetBrains: Extensions-Kotlin Programming Language. https://kotlinlang.org/docs/reference/extensions.html. Accessed June 2018

  15. Kent, S.: Model driven engineering. In: Proceedings of the Third International Conference on Integrated Formal Methods, IFM 2002. pp. 286–298. Springer-Verlag, London, UK (2002)

    Google Scholar 

  16. Khaitan, S.K., McCalley, J.D.: Design techniques and applications of cyberphysical systems: a survey. IEEE Syst. J. 9(2), 350–365 (2015)

    Article  Google Scholar 

  17. Kounev, S., Huber, N., Brosig, F., Zhu, X.: A model-based approach to designing self-aware IT systems and infrastructures. IEEE Comput. 49(7), 53–61 (2016). https://doi.org/10.1109/MC.2016.198

    Article  Google Scholar 

  18. Leveson, N.G.: Safeware-System Safety and Computers: A Guide to Preventing Accidents and Losses Caused by Technology. Addison-Wesley (1995)

    Google Scholar 

  19. Lladó, C.M., Smith, C.U.: PMIF+: extensions to broaden the scope of supported models. In: Balsamo, M.S., Knottenbelt, W.J., Marin, A. (eds.) EPEW 2013. LNCS, vol. 8168, pp. 134–148. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40725-3_11

    Chapter  Google Scholar 

  20. L&S Computer Technology Inc: SPE-ED+. http://spe-ed.com/. Accessed June 2018

  21. Medina, J.: The UML profile for MARTE: modelling predictable real-time systems with UML. http://www.artist-embedded.org/docs/Events/2011/Models_for_SA/01-MARTE-SAM-Julio_Medina.pdf. Aaccessed June 2018

  22. Moreno, G.A., Smith, C.U.: Performance analysis of real-time component architectures: an enhanced model interchange approach. Perform. Eval. 67(8), 612–633 (2010). Special Issue on Software and Performance

    Article  Google Scholar 

  23. OMG: Meta Object Facility (MOF) 2.0 Query/View/Transformation Specification, Version 1.3. http://www.omg.org/spec/QVT/1.3/

  24. OMG: Meta Object Facility (MOF), Version 2.5.1. http://www.omg.org/spec/MOF/2.5.1/

  25. OMG: Modeling and Analysis of Real-time Embedded Systems (MARTE), Version 1.1. http://www.omg.org/spec/MARTE/1.1/

  26. OMG: UML Profile for Schedulability, Performance, & Time (SPTP), Version 1.1. http://www.omg.org/spec/SPTP/1.1/

  27. OMG: Unified Modeling Language (UML), Version 2.5. http://www.omg.org/spec/UML/2.5/

  28. Petriu, D.B., Woodside, M.: An intermediate metamodel with scenarios and resources for generating performance models from uml designs. Softw. Syst. Model. 6(2), 163–184 (2007). https://doi.org/10.1007/s10270-006-0026-8

    Article  Google Scholar 

  29. ProM Tools (2017). http://www.promtools.org/doku.php. Accessed June 2018

  30. QPR Process Analyzer (2011). https://www.qpr.com. Accessed June 2018

  31. Selic, B., Gérard, S.: Modeling and Analysis of Real-Time and Embedded Systems with UML and MARTE: Developing Cyber-Physical Systems, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco (2013)

    Google Scholar 

  32. Smith, C.U., Lladó, C.M., Puigjaner, R.: Model interchange format specifications for experiments, output and results. Comput. J. 54(5), 674–690 (2011). https://doi.org/10.1093/comjnl/bxq065

    Article  Google Scholar 

  33. Smith, C.U., Lladó, C.M.: SPE for the internet of things and other real-time embedded systems. In: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion, pp. 227–232. ACM, New York 2017). https://doi.org/10.1145/3053600.3053652

  34. Smith, C.U., Williams, L.G.: Performance Solutions: A Practical Guide to Creating Responsive, Scalable Software. Addison Wesley Longman Publishing Co., Inc. (2002)

    Google Scholar 

  35. Smith, C., Williams, L.: A performance model interchange format. J. Syst. Softw. 49(1), 63–80 (1999). https://doi.org/10.1016/S0164-1212(99)00067-9

    Article  Google Scholar 

  36. Wallin, P., Johnsson, S., Axelsson, J.: Issues related to development of E/E product line architectures in heavy vehicles. In: 42nd Hawaii International Conference on System Sciences (2009)

    Google Scholar 

  37. Williams, L.G., Smith, C.U.: Information requirements for software performance engineering. In: Beilner, H., Bause, F. (eds.) TOOLS 1995. LNCS, vol. 977, pp. 86–101. Springer, Heidelberg (1995). https://doi.org/10.1007/BFb0024309

    Chapter  Google Scholar 

  38. Woodside, M., Petriu, D.C., Merseguer, J., Petriu, D.B., Alhaj, M.: Transformation challenges: from software models to performance models. Softw. Syst. Model. 13(4), 1529–1552 (2014). https://doi.org/10.1007/s10270-013-0385-x

    Article  Google Scholar 

  39. Woodside, M., Petriu, D.C., Petriu, D.B., Shen, H., Israr, T., Merseguer, J.: Performance by unified model analysis (PUMA). In: Proceedings of the 5th International Workshop on Software and Performance, WOSP 2005. pp. 1–12. ACM, New York (2005). https://doi.org/10.1145/1071021.1071022

Download references

Author information

Authors and Affiliations

  1. Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya (UOC), Barcelona, Spain

    Abel Gómez & Jordi Cabot

  2. L&S Computer Technology, Inc, Austin, USA

    Connie U. Smith & Amy Spellmann

  3. ICREA, Barcelona, Spain

    Jordi Cabot

Authors
  1. Abel Gómez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Connie U. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amy Spellmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jordi Cabot
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abel Gómez .

Editor information

Editors and Affiliations

  1. Concordia University, Montreal, QC, Canada

    Ferhat Khendek

  2. University of Kaiserslautern, Kaiserslautern, Germany

    Reinhard Gotzhein

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gómez, A., Smith, C.U., Spellmann, A., Cabot, J. (2018). Enabling Performance Modeling for the Masses: Initial Experiences. In: Khendek, F., Gotzhein, R. (eds) System Analysis and Modeling. Languages, Methods, and Tools for Systems Engineering. SAM 2018. Lecture Notes in Computer Science(), vol 11150. Springer, Cham. https://doi.org/10.1007/978-3-030-01042-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-01042-3_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01041-6

  • Online ISBN: 978-3-030-01042-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation