Software Tools

  • Peter Buchholz
  • Jan Kriege
  • Iryna Felko
Part of the SpringerBriefs in Mathematics book series (BRIEFSMATH)


Various of the approaches and algorithms presented in the previous sections are available in software tools. The majority of the tools has been designed for parameter estimation of PHDs and MAPs and will be presented in Sects. 7.1 and 7.2, respectively. Of course, the resulting PHDs or MAPs will usually serve as input model (e.g. characterizing inter-arrival or service times) to some larger model which should be analyzed, either by simulation or by applying numerical techniques. The last section of this chapter deals with software to analyze these models.


Software Tool Random Number Generation Joint Moment Process Description Moment Match 
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.


  1. 12.
    Bause, F., Buchholz, P., Kriege, J.: ProFiDo: the processes fitting toolkit Dortmund. In: Proceedings of the 7th International Conference on Quantitative Evaluation of Systems (QEST 2010), pp. 87–96. IEEE Computer Society, Williamsburg (2010)Google Scholar
  2. 13.
    Bause, F., Gerloff, P., Kriege, J.: ProFiDo: a toolkit for fitting input models. In: Müller-Clostermann, B., Echtle, K., Rathgeb, E.P. (eds.) Proceedings of the 15th International GI/ITG Conference on Measurement, Modelling, and Evaluation of Computing Systems and Dependability and Fault Tolerance. Lecture Notes in Computer Science, vol. 5987, pp. 311–314. Springer, Berlin (2010)Google Scholar
  3. 32.
    Buchholz, P., Kriege, J.: A heuristic approach for fitting MAPs to moments and joint moments. In: Proceedings of the 6th International Conference on the Quantitative Evaluation of Systems (QEST), pp. 53–62. IEEE Computer Society, Budapest (2009)Google Scholar
  4. 41.
    Casale, G., Zhang, E.Z., Smirni, E.: KPC-toolbox: simple yet effective trace fitting using Markovian arrival processes. In: Proceedings of the 5th International Conference on the Quantitative Evaluation of Systems (QEST), pp. 83–92. IEEE Computer Society, St. Malo (2008)Google Scholar
  5. 75.
    Horváth, A., Telek, M.: PhFit: a general purpose phase type fitting tool. In: Proceedings of the Performance Tools 2002. Lecture Notes in Computer Science, vol. 2324, pp. 82–91. Springer, Berlin (2002)Google Scholar
  6. 81.
    Horváth, G., Reinecke, P., Telek, M., Wolter, K.: Efficient generation of PH-distributed random variates. In: Al-Begain, K., Fiems, D., Vincent, J.M. (eds.) Proceedings of the Analytical and Stochastic Modeling Techniques and Applications (ASMTA). Lecture Notes in Computer Science, vol. 7314, pp. 271–285. Springer, Berlin (2012)CrossRefGoogle Scholar
  7. 99.
    Kriege, J., Buchholz, P.: Simulating stochastic processes with OMNeT++. In: Liu, J., Quaglia, F., Eidenbenz, S., Gilmore, S. (eds.) Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques (SimuTools’11), pp. 367–374. ICST/ACM, Brussels (2011)Google Scholar
  8. 134.
    Okamura, H., Dohi, T., Trivedi, K.S.: A refined EM algorithm for PH distributions. Perform. Eval. 68(10), 938–954 (2011)CrossRefGoogle Scholar
  9. 139.
    Panchenko, A., Thümmler, A.: Efficient phase-type fitting with aggregated traffic traces. Perform. Eval. 64(7–8), 629–645 (2007)CrossRefGoogle Scholar
  10. 143.
    Reinecke, P., Horváth, G.: Phase-type distributions for realistic modelling in discrete-event simulation. In: Proceedings of the 5th International ICST Conference on Simulation Tools and Techniques, SIMUTOOLS ’12, pp. 283–290. ICST, Brussels (2012)Google Scholar
  11. 144.
    Reinecke, P., Krauß, T., Wolter, K.: Cluster-based fitting of phase-type distributions to empirical data. Comput. Math. Appl. 64(12), 3840–3851 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
  12. 150.
    Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)CrossRefzbMATHGoogle Scholar
  13. 155.
    Telek, M., Horváth, G.: A minimal representation of Markov arrival processes and a moments matching method. Perform. Eval. 64(9–12), 1153–1168 (2007)CrossRefGoogle Scholar
  14. 156.
    Thümmler, A., Buchholz, P., Telek, M.: A novel approach for phase-type fitting with the EM algorithm. IEEE Trans. Dep. Sec. Comput. 3(3), 245–258 (2006)CrossRefGoogle Scholar

Copyright information

© Peter Buchholz, Jan Kriege, Iryna Felko 2014

Authors and Affiliations

  • Peter Buchholz
    • 1
  • Jan Kriege
    • 1
  • Iryna Felko
    • 1
  1. 1.Department of Computer ScienceTechnical University of DortmundDortmundGermany

Personalised recommendations