Performance Engineering for Enterprise Applications

  • Marcel Seelig
  • Jan Schaffner
  • Gero Decker
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 6)

Performance is a key aspect for software systems. Bad responsiveness can result in a decreasing number of users. In contrast, exceptional performance can lead to a decisive competitive advantage, as the example of Google's search engine shows.

We validate our approach using a case study. We have taken an existing ABAP system, measured the performance of the system, and compared the measurements to simulation results obtained with our framework.

The remainder of this chapter is structured as follows. First, we discuss related work in Sect. 39.2. In Sect. 39.3 we introduce our methodology for simulation-based performance engineering. Section 39.4 discusses the implementation of our performance simulation framework. Afterwards, Sect. 39.5 presents the case study where our simulation-based approach has been applied. The results demonstrating the applicability of our approach are discussed in Sect. 39.6, also providing concluding remarks and an overview of our further research prospects.


Performance Prediction Performance Engineer Performance Behavior Object Management Group Enterprise Application 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Raj Jain. The Art of Computer Systems Performance Analysis. Wiley, New York, 1991.MATHGoogle Scholar
  2. 2.
    Robert F. Dugan Jr. Performance lies my professor told me: The case for teaching software performance engineering to undergraduates. In WOSP’04, Redwood City, CA, January 2004. ACM.Google Scholar
  3. 3.
    Donald E. Knuth. Structured programming with go to statements. Computing Surveys, 6(4): 261–301, December 1974.MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    L. Kleinrock. Queuing Systems, Theory, volume 1. Wiley, New York, 1975.Google Scholar
  5. 5.
    Giovanni Denaro, Andrea Polini, and Wolfgang Emmerich. Early performance testing of distributed software applications. In WOSP’ 04, pages 94–103, Redwood City, CA, January 14–16, 2004. ACM.CrossRefGoogle Scholar
  6. 6.
    Yan Liu, Ian Gorton, Anna Liu, Ning Jiang, and Shiping Chen. Designing a test suite for empirically-based middleware performance prediction. In James Noble and John Potter, editors, 40th International Conference on Technology of Object-Oriented Languages and Systems (TOOLS Pacific 2002), volume 10. Australian Computer Society Inc., 2002.Google Scholar
  7. 7.
    Simonetta Balsamo, Antinisca Di Marco, Paola Inverardi, and Marta Simeoni. Model-based performance prediction in software development: A survey. IEEE Transactions on Software Engineering, 30(5):295–310, May 2004.CrossRefGoogle Scholar
  8. 8.
    Rebecca Isaacs and Paul Barham. Performance analysis in loosely-coupled distributed systems. Technical Report, Microsoft, 2003.Google Scholar
  9. 9.
    K. London, J. Dongarra, S. Moore, P. Mucc, K. Seymour, and T. Spencer. End-user tools for application performance analysis using hardware counters. In International Conference on Parallel and Distributed Computing Systems, August 2001.Google Scholar
  10. 10.
    Elaine J. Weyuker and Filippos I. Vokolos. Experience with performance testing of software systems: Issues, an approach, and case study. IEEE Transactions on Software Engineering, 26(12):1147–1156, 2000.CrossRefGoogle Scholar
  11. 11.
    Connie U. Smith. Software performance engineering. In Proceedings of Computer Measurement Group International Conference XIII, pages 5–14. Computer Measurement Group, December 1981.Google Scholar
  12. 12.
    M. Woodside, D. Petriu, and D. Amyot. PUMA project. URL retrieved 20.11.2005.
  13. 13.
    Object Management Group. UML Profile for Schedulability, Performance and Time. OMG Full Specification, formal/03-09-01, 2003.Google Scholar
  14. 14.
    Xiuping Wu, David McMullan, and Murray Woodside. Component-based performance prediction. In Proceedings of Sixth ICSE Workshop on Component-Based Software, May 2003.Google Scholar
  15. 15.
    Andreas Knoepfel, Bernhard Groene, and Peter Tabeling. Fundamental Modeling Concepts: Effective Communication of IT Systems. Wiley, UK, 2006.Google Scholar
  16. 16.
    Peter Tabeling and Bernhard Grone. Integrative architecture elicitation for large computer based systems. In ECBS’05: Proceedings of the 12th IEEE International Conference and Workshops on the Engineering of Computer-Based Systems (ECBS’05), pages 51–61, Washington, DC, 2005. IEEE Computer Society.CrossRefGoogle Scholar
  17. 17.
    Sun Microsystems, Inc. Java pet store demo.
  18. 18.
    Daniel Brinkmann. Analyse des Java Pet Stores und Portierung auf den SAP Web ApplicationServer. Master’s thesis, Hasso-Plattner-Institut für Softwaresystemtechnik an der Universität Potsdam, 2005.Google Scholar
  19. 19.
    Object Management Group. Unified Modeling Language Specification v. 2.0. OMG UML 2.0 Superstructure Specification, formal/05-07-04, 2005.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Marcel Seelig
    • 1
  • Jan Schaffner
  • Gero Decker
    • 1
  1. 1.Hasso-Plattner-Institute for Software Systems EngineeringGermany

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