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An unified VoIP model for workload generation

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Abstract

This paper presents a new model for VoIP workload generation. The novelty of our proposal consists in modeling the sessions by characterizing both the user behavior (session level) and the packet generation for an active call (intra-session level) with easily measured parameters and low computational complexity. This approach also facilitates systematic study of changes in user behavior and voice codec. The session level was modeled by analysis of call-holding time and time interval between successive calls. The model for call-holding time, characterizing the individual user behavior, uses the Pareto type 2 probability distribution. The time interval between calls is obtained from aggregate traffic and can be modeled by exponential probability distribution. Aggregate traffic is obtained by superposition of simultaneous sessions. The data used to characterize the session level were collected at the backbone of two Brazilian telecommunication carriers. The model for intra-session level comprises the characterization of the packet size and the packet inter-arrival time. The intra-session model was based on data generated in a laboratory environment, in order to properly characterize the codec influence on packet generation and to avoid the effects of delay, jitter and loss commonly present in an operational network. Models for constant bit rate and variable bit rate codecs were considered. A simulator was implemented and the results indicate that our model properly mimics the characteristics observed in real traffic and can be used for VoIP modeling and workload generation. Additionally, an application to automate the performance analysis was developed.

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References

  1. 12 ISG (2009) ITU-T test signals for telecommunication systems. Test vectors associated to recommendation ITU-T P.50 appendix I

  2. 16th Int Teletraffic Congr (ITC) (1999) A memory Markov chain model for VBR traffic with strong positive correlations, pp 827–836

  3. 3GPP (1999) 3GPP recommendation TR26.075: performance characterization of the AMR speech codec

  4. Abry P, Borgnat P, Ricciato F, Scherrer A, Veitch D (2009) Revisiting an old friend: on the observability of the relation between long range dependence and heavy tail. Telecommun Syst (Special issue on Traffic Modeling, its Computations and Applications) 43(3–4):147–165

    Google Scholar 

  5. Banks J, Carson J, Nelson BL, Nicol DM (2004) Discrete-event system simulation, 4th edn. Prentice Hall

  6. Barford P, Crovella M (1998) Generating representativeWeb workloads for network and server performance evaluation. SIGMETRICS Perform Eval Rev 26(1):151–160

    Article  Google Scholar 

  7. Bavarian archive for speech signals (BAS) verbmobil 6.1 (1996) http://www.phonetik.uni-muenchen.de/Bas/BasHomedeu.html. Accessed September 2011

  8. Beran J (1994) Statistics for long-memory processes. Chapman and Hall, New York

    MATH  Google Scholar 

  9. Bergstra JA, Middelburg CA (2003) ITU-T recommendation G.107: the e-model, a computational model for use in transmission planning

  10. Box GEP, Pierce DA (1970) Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J Am Stat Assoc 65(332):1509–1526

    Article  MATH  MathSciNet  Google Scholar 

  11. Box G, Jenkine G, Reineel G (1994) Time series analysis, 3th edn. Prentice-Hall, New York

    MATH  Google Scholar 

  12. Casilari E, Montes H, Sandoval F (2002) Modelling of voice traffic over IP networks. In: Proc of communication systems, networks and digital signal processsing (CSNDSP)

  13. Chandy KM, Misra J (1981) Asynchronous distributed simulation via a sequence of parallel computations. Commun ACM 24:198–206

    Article  MathSciNet  Google Scholar 

  14. Chen WE, Hung HN, Lin YB (2007) Modeling VoIP call holding times for telecommunications. IEEE Netw 21:22–28

    Article  Google Scholar 

  15. Crovella ME, Bestavros A (1997) Self-similarity in World Wide Web traffic: evidence and possible causes. IEEE/ACM Trans Netw 5(6):835–846

    Article  Google Scholar 

  16. de Mattos CI, Ribeiro EP, Pedroso CM (2010) A new model for VoIP traffic generation. In: International telecommunication symposium. Brazilian Telecommunication Society, Manaus, Brazil

    Google Scholar 

  17. Flood J (1997) Telecommunications networks, 2nd edn. The Institution of Eletrical Engineers

  18. Heffes H, Lucsmtoni DM (1986) Markov modulated characterization of packetized voice and data traffic and related statistical multiplexer peformance. IEEE J Sel Areas Commun SAC-4:856–868

    Article  Google Scholar 

  19. Huang TY, Huang P, Chen KT, Wang PJ (2010) Could Skype be more satisfying? A QoE-centric study of the FEC mechanism in an internet-scale VoIP system. IEEE Netw 24(2):42–48

    Article  Google Scholar 

  20. ITU-T (2000) Q.1901 bearer independent call control

  21. ITU-T (1996) Recommendation G.729. Coding of speech at 8kbit/s using Conjugate-Structure Algebraic-Code-Excited Linear Prediction (CS-ACELP)

  22. ITU-T (1988) Recommendation G.711. Pulse code modulation (PCM) of voice frequencies

  23. Jain R (1991) The art of computer system performance analysis: techniques for experimental design, measurement, simulation and modeling. Wiley, New York

    Google Scholar 

  24. Jain R, Routhier SA (1986) Packet trains: measurements and a new model for computer network traffic. IEEE J Sel Areas Commun 4:986–995

    Article  Google Scholar 

  25. Jiang W, Schulzrinne H (2000) Analysis of on-off patterns in VoIP and their effect on voice traffic aggregation. In: Proceedings of 9th IEEE international conference on computer communication networks

  26. Klugman SA, Panjer HH, Willmot GE (2004) Loss models from data to decisions, 2nd edn

  27. Leland WE, Taqqu, MS, Willinger W, Wilson DV (1994) On the self-similar nature of ethernet traffic (extended version). IEEE/ACM Trans Netw 2(1):1–15

    Article  Google Scholar 

  28. Lindblom J (2005) A sinusoidal voice over packet coder tailored for the frame-erasure channel. IEEE Trans Speech Audio Process 13(5–2):787–798

    Article  Google Scholar 

  29. Menascé DA, Almeida VA (1998) Capacity planning for Web performance. Prentice Hall

  30. Menth M, Binzenhöfer A, Mühleck S (2009) Source models for speech traffic revisited. IEEE/ACM Trans Netw 17(4):1042–1051

    Article  Google Scholar 

  31. Orebaugh A, Ramirez G, Burke J, Pesce L (2006) Wireshark & Ethereal network protocol analyzer toolkit (Jay Beale’s open source security). Syngress Publishing

  32. Rosenberg J, Schulzrinne H, Camarillo G, Johnston A, Peterson J, Sparks R, Handley M, Schooler E (2002) RFC 3261: session initiation protocol

  33. Schulzrinne H, Casner S, Frederick R, Jacobson V (1998) RTP: a transport protocol for real-time applications

  34. Team RDC (2009) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. Available at http://www.R-project.org. Accessed September 2011

  35. Vitez M (2011) Picophone. Available at http://www.vitez.it/picophone/. Accessed September 2011

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Acknowledgements

We wish to thank the Electrical Engineers Rafael Alesi and Willian Mattos for their dedication during the year of 2010 in implementation the application to automatize the analysis of simulation results, the Computer Engineer Jeferson Caldeira for programming the scripts to analyze the large amount of data, the Electrical Engineer M.Sc. Edgard Massahiro for providing of the data from Telecommunication Carrier 2 and to Electrical Engineer M.Sc. Mateus Cruz for providing the data from Telecommunication Carrier 1.

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Authors and Affiliations

  1. Department of Electrical Engineering, Federal University of Parana, Curitiba, Parana, Brazil

    Carlos Ignacio Mattos, Eduardo Parente Ribeiro, Evelio Martín García Fernandez & Carlos Marcelo Pedroso

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  1. Carlos Ignacio Mattos
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  2. Eduardo Parente Ribeiro
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  3. Evelio Martín García Fernandez
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  4. Carlos Marcelo Pedroso
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Corresponding author

Correspondence to Carlos Marcelo Pedroso.

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Mattos, C.I., Ribeiro, E.P., Fernandez, E.M.G. et al. An unified VoIP model for workload generation. Multimed Tools Appl 70, 2309–2329 (2014). https://doi.org/10.1007/s11042-012-1243-5

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