Transaction Profile Estimation of Queueing Network Models for IT Systems Using a Search-Based Technique

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8636)


The software and hardware systems required to deliver modern Web based services are becoming increasingly complex. Management and evolution of the systems requires periodic analysis of performance and capacity to maintain quality of service and maximise efficient use of resources. In this work we present a method that uses a repeated local search technique to improve the accuracy of modelling such systems while also reducing the complexity and time required to perform this task. The accuracy of the model derived from the search-based approach is validated by extrapolating the performance to multiple load levels which enables system capacity and performance to be planned and managed more efficiently.


Service Demand Capacity Management Enterprise Application Actual Processing Time Queueing Network Model 
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.
    Grinshpan, L.: Solving Enterprise Applications Performance Puzzles. John Wiley and Sons, Inc., Hoboken (2012)CrossRefGoogle Scholar
  2. 2.
    Casale, G., Cremonesi, P., Turrin, R.: Robust workload estimation in queueing network performance models. In: Proceedings of the 16th Euromicro Conference on Parallel, Distributed and Network-Based Processing, PDP 2008 (2008)Google Scholar
  3. 3.
    Kraft, S., Pacheco-Sanchez, S., Casale, G., Dawson, S.: Estimating service resource consumption from response time measurements. In: Proceedings of the International ICST Conference on Performance Evaluation Methodologies and Tools (2009)Google Scholar
  4. 4.
    Wynter, L., Liu, Z., Cathy, H.X., Zhang, F.: Parameter inference of queueing models for it systems using end-to-end measurements. Performance Evaluation 63(1), 36–60 (2006)CrossRefGoogle Scholar
  5. 5.
    Ghaith, S., Wang, M., Perry, P., Murphy, J.: Automatic, load-independent detection of performance regressions by transaction profiles. In: Proceedings of the 2013 International Workshop on Joining AcadeMiA and Industry Contributions to testing Automation, JAMAICA 2013, pp. 59–64. ACM, New York (2013)CrossRefGoogle Scholar
  6. 6.
    Ghaith, S., Wang, M., Perry, P., Murphy, J.: Profile-based, load-independent anomaly detection and analysis in performance regression testing of software systems. In: 17th European Conference on Software Maintenance and Reengineering (CSMR 2013), Genova, Italy (2013)Google Scholar
  7. 7.
    Harman, M., Hierons, R., Jones, B., Lumkin, M., Mitchell, B., Mancoridis, S., Rees, K., Roper, M., Clarke, J., Dolado, J.J., Shepperd, M.: Reformulating software engineering as a search problem. In: Software IEE Proceedings (June 2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Computer ScienceUniversity College DublinIreland

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