Performance Evaluation with Hidden Markov Models

  • E. de Souza e Silva
  • R. M. M. Leão
  • Richard R. Muntz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6821)


The use of hidden Markov models (HMMs) has found widespread use in many different areas. This chapter focuses on HMMs applied to the performance evaluation of computer systems and networks. After presenting a brief review of background material on HMMs, applications such as channel delay and loss characteristics, traffic modeling and workload generation are surveyed. The power of HMMs as predictors of performance metrics is also highlighted. We conclude by presenting a few features of the module of the Tangram-II performance evaluation tool that is targeted to HMMs.


hidden Markov models performance evaluation network applications 


  1. 1.
    Bernaille, L., Teixeira, R., Salamatian, K.: Early application identification. In: CoNEXT 2006, pp. 1–12. ACM (2006)Google Scholar
  2. 2.
    Cappé, O.: Ten years of HMMs (March 2001)Google Scholar
  3. 3.
    Carmo, R.M.L.R., de Carvalho, L.R., de Souza e Silva, E., Diniz, M.C., Muntz, R.R.: Performance/Availability Modeling with the TANGRAM-II Modeling Environment. Performance Evaluation 33(1), 45–65 (1998)CrossRefGoogle Scholar
  4. 4.
    de Vielmond, C.C.L.B., Leão, R.M.M., de Souza e Silva, E.: A hierarchical HMM for iterative users acessing a multimedia server. In: Brazilian Symposium on Computer Networks, pp. 469–482 (2007) (in Portuguese)Google Scholar
  5. 5.
    Dainotti, A., de Donato, W., Pescape, A., Salvo Rossi, P.: Classification of network traffic via packet-level Hidden Markov Models. In: IEEE GLOBECOM 2008, pp. 1–5 (2008)Google Scholar
  6. 6.
    Dainotti, A., Pescapé, A., Rossi, P.S., Palmieri, F., Ventre, G.: Internet traffic modeling by means of hidden markov models. Computer Networks 52(14), 2645–2662 (2008)CrossRefzbMATHGoogle Scholar
  7. 7.
    de Souza e Silva, E., Figueiredo, D.R., Leão, R.M.M.: The TANGRAM-II integrated modeling environment for computer systems and networks. Performance Evaluation Review 36(4), 64–69 (2009)CrossRefGoogle Scholar
  8. 8.
    de Souza e Silva, E., Leão, R.M.M., Santos, A.D., Azevedo, J.A., Machado Netto, B.C.: Multimedia Supporting Tools for the CEDERJ Distance Learning Initiative applied to the Computer Systems Course. In: 22nd ICDE World Conference on Distance Education, pp. 1–11 (2006)Google Scholar
  9. 9.
    de Souza e Silva, E., Leão, R.M.M., Trindade, M.B., da Silva, A.P.C., Ribeiro, B.F., Duarte, F.P., Azevedo, J.A.: A methodology for dimensioning IP networks with QoS using Hidden Markov Models. Technical report, UFRJ-COPPE-PESC (2005)Google Scholar
  10. 10.
    Filho, F.S., Watanabe, E.H., de Souza e Silva, E.: Adaptive forward error correction for interactive streaming over the Internet. In: IEEE Globecom 2006, pp. 1–6 (2006)Google Scholar
  11. 11.
    Fine, S., Singer, Y., Tishby, N.: The hierarchical Hidden Markov Model: Analysis and applications. Machine Learning 32, 41–62 (1998)CrossRefzbMATHGoogle Scholar
  12. 12.
    Kobayashi, H., Yu, S.-Z., Mark, B.L.: An integrated mobility and traffic model for resource allocation in wireless networks. In: 3rd ACM International Workshop on Wireless Mobile Multimedia (WOWMOM 2000), pp. 39–47. ACM (2000)Google Scholar
  13. 13.
    Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–285 (1989)CrossRefGoogle Scholar
  14. 14.
    Salamatian, K., Vaton, S.: Hidden markov modeling for network communication channels. Performance Evaluation Review 29(1), 92–101 (2001)CrossRefGoogle Scholar
  15. 15.
    Silveira, F., de Souza e Silva, E.: Predicting packet loss statistics with hidden Markov models. Performance Evaluation Review 35(3), 19–21 (2007), CrossRefGoogle Scholar
  16. 16.
    Tao, S., Guerin, R.: On-line estimation of internet path performance: An application perspective. In: IEEE Infocom (2004)Google Scholar
  17. 17.
    Wei, W., Wang, B., Towsley, D.: Continuous-time hidden markov models for network performance evaluation. Performance Evaluation 49(1-4), 129–146 (2002)CrossRefzbMATHGoogle Scholar
  18. 18.
    Welch, L.R.: Hidden markov models and the baum-welch algorithm. IEEE Information Theory Society Newsletter 53(4), 10–14 (2003)MathSciNetGoogle Scholar
  19. 19.
    Wright, C., Monrose, F., Masson, G.M.: Hmm profiles for network traffic classification. In: The 2004 ACM Workshop on Visualization and Data Mining for Computer Security (VizSEC/DMSEC 2004), pp. 9–15. ACM (2004)Google Scholar
  20. 20.
    The TANGRAM-II manual,
  21. 21.
    Xie, Y., Yu, S.-Z.: A large-scale hidden semi-markov model for anomaly detection on user browsing behaviors. IEEE/ACM Transactions on Networking 17(1), 54–65 (2009)CrossRefGoogle Scholar
  22. 22.
    Yajnik, M., Moon, S., Kurose, J., Towsley, D.: Measurement and modelling of the temporal dependence in packet loss. In: IEEE Infocom 2004 (1999)Google Scholar
  23. 23.
    Yu, S.-Z., Kobayashi, H.: An efficient forward-backward algorithm for an explicit-duration hidden markov model. IEEE Signal Processing Letters 10(1), 11–14 (2003)CrossRefzbMATHGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • E. de Souza e Silva
    • 1
  • R. M. M. Leão
    • 1
  • Richard R. Muntz
    • 2
  1. 1.Federal Univ. of Rio de Janeiro - COPPE/PESCBrazil
  2. 2.CS DepartamentUCLALAUSA

Personalised recommendations