Performance Modelling and Traffic Characterisation of Optical Networks

  • Harry Mouchos
  • Athanasios Tsokanos
  • Demetres D. Kouvatsos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5233)

Abstract

A review is carried out on the traffic characteristics of an optical carrier’s OC-192 link, based on the IP packet size distribution, traffic burstiness and self-similarity. The generalised exponential (GE) distribution is employed to model the interarrival times of bursty traffic flows of IP packets whilst self-similar traffic is generated for each wavelength of each source node in the optical network. In the context of networks with optical burst switching (OBS), the dynamic offset control (DOC) allocation protocol is presented, based on the offset values of adapting source-destination pairs, using preferred wavelengths specific to each destination node. Simulation evaluation results are devised and relative comparisons are carried out between the DOC and Just-Enough-Time (JET) protocols. Moreover parallel generators of optical bursts are implemented and simulated using the Graphics Processing Unit (GPU) and the Compute Unified Device Architecture (CUDA) and favourable comparisons are made against simulations run on general-purpose CPUs.

Keywords

Wavelength division multiplexing (WDM) Synchronous Optical Networking (SONET) optical burst switching (OBS) protocol Just Enough Time (JET) protocol Generalised Exponential Distribution (GE) bursty traffic self-similar traffic Compute Unified Device Architecture (CUDA) Parallel Processing Wavelength Division Multiplexing (WDM) Dense-wavelength Division Multiplexing (DWDM) Optical Packet Switching (OPS) Optical Burst Switching (OBS) Self-Similarity Long-Range Dependence (LRD) Generalised Exponential (GE) Distribution Graphics Processing Unit (GPU) 

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References

  1. 1.
    Amstutz, S.R.: Burst Switching - an introduction. IEEE Communications Magazine, 36–42 (1983)Google Scholar
  2. 2.
    Listanti, M., Eramo, V., Sabella, R.: Architectural and Technological Issues for Future Optical Internet Networks. IEEE Communications Magazine 38(9), 82–92 (2000)CrossRefGoogle Scholar
  3. 3.
    Yoo, M., Qiao, C.: Just-Enough-Time (JET): a high speed protocol for bursty traffic in optical networks. Vertical-Cavity Lasers, Technologies for a Global Information Infrastructure, WDM Components Technology, Advanced Semiconductor Lasers and Applications, Gallium Nitride Materials, Processing and Devices 11(15), 26–27 (1997)Google Scholar
  4. 4.
    Dolzer, K., Gauger, C., Spath, J., Bodamer, S.: Evaluation of reservation mechanisms in optical burst switching networks. AEU Int. J. of Electronics and Communications 55(1) (2001)Google Scholar
  5. 5.
    Verma, S., Chaskar, H., Ravikanth, R.: Optical Burst Switching: A Viable Solution for Terabit IP Backbone. IEEE Network, 48–53 (2000)Google Scholar
  6. 6.
    Tan, K., Mohan, G., Chua, K.C.: Link Scheduling State Information Based Offset Management for Fairness Improvement in WDM Optical Burst Switching Networks. Computer Networks 45(6), 819–834 (2004)CrossRefMATHGoogle Scholar
  7. 7.
    Kouvatsos, D.D.: Entropy Maximization and Queueing Network Models. Annals of Operation Research 48, 63–126 (1994)CrossRefMATHGoogle Scholar
  8. 8.
    Zhang, Q., Vokkarane, V., Jue, J., Chen, B.: Absolute QoS Differentiation in Optical Burst-Switched Networks. IEEE Journal on Selected Areas in Communications 22(9) (2004)Google Scholar
  9. 9.
    Yu, X., Chen, Y., Qiao, C.: Study of traffic statistics of assembled burst traffic in optical burst switched networks. In: Proceedings, Optical Networking and Communication Conference (OptiComm), Boston, MA (2002)Google Scholar
  10. 10.
    Park, K., Willinger, W.: Self-Similar Network Traffic and Performance Evaluation. John Wiley and Sons, Chichester (2000)CrossRefGoogle Scholar
  11. 11.
    Willinger, W., Taqqu, M., Erramilli, A.: A Bibliographical Guide to Self-Similar Traffic and Performance Modeling for Modern High-Speed Networks. Stochastic Networks: Theory and applications. Oxford University Press, Oxford (1996)MATHGoogle Scholar
  12. 12.
    Jeong, H.-D.J., Pawlikowski, K., McNickle, D.C.: Generation of Self-Similar Processes for Simulation Studies of Telecommunication Networks. Mathematical and Computer Modelling 38, 1249–1257 (2003)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Jeong, H.-D.J., Pawlikowski, K., McNickle, D.C.: Generation of Self-Similar Processes for Simulation Studies of Telecommunication Networks. Mathematical and Computer Modelling 38, 1249–1257 (2003)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Karagiannis, T., Molle, M., Faloutsos, M., Broido, A.: A Nonstationary Poisson View of Internet Traffic. In: INFOCOM 2004, 23rd Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 3, pp. 1558–1569 (2004)Google Scholar
  15. 15.
    de Vega Rodrigo, M., Goetz, J.: An analytical study of optical burst switching aggregation strategies. In: Proceedings of the Third International Workshop on Optical Burst Switching, WOBS (2004)Google Scholar
  16. 16.
    Shannon, C., Aben, E., Claffy, K., Andersen, D.: The CAIDA Anonymized 2008 Internet Traces - 19/06/2008 12:59:08 - 19/06/2008 14:01:00 (2008), CAIDA, http://www.caida.org/passive/passive_2008_dataset.xml (retrieved December 10, 2008)
  17. 17.
    CAIDA. The Cooperative Association for Internet Data Analysis (2003), http://www.caida.org/research/traffic-analysis/AIX/ (Retrieved from CAIDA)
  18. 18.
    Kouvatsos, D.D.: Maximum Entropy and the G/G/1 Queue. Acta Informatica 23, 545–565 (1986)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Crovella, M., Bestavros, A.: Self-Similarity in World-Wide Web Traffic: Evidence and Possible Causes. In: Proc. ACM SIGMETRICS 1996 (1996)Google Scholar
  20. 20.
    Paxson, V., Floyd, S.: Wide Area Traffic: The Failure of Poisson Modeling. In: Proc. ACM SIGCOMM 1994 (1994)Google Scholar
  21. 21.
    Popescu, A.: Traffic Analysis and Control in Computer Communications Networks. Blekinge Institute of Technology, Stockholm (2007) (preprint)Google Scholar
  22. 22.
    Beran, J.: Statistics for Long-Memory Processes. Chapman and Hall, Boca Raton (1994)MATHGoogle Scholar
  23. 23.
    Abry, P., Veitch, D.: Wavelet Analysis of Long Range Dependent Traffic. IEEE Transactions on Information Theory (1998)Google Scholar
  24. 24.
    Stallings, W.: High-Speed Networks and Internets: Performance and Quality of Service, 2nd edn. Prentice-Hall, Englewood Cliffs (2002)Google Scholar
  25. 25.
    Taqqu, M., Teverovsky, V.: On Estimating the Intensity of Long-Range Dependence. Finite and Infinite Variance Time Series. Boston University USA, Boston (1996)MATHGoogle Scholar
  26. 26.
    Qiao, C., Yoo, M.: Choices, Features and Issues in Optical Burst Switching (OBS). Optical Networking Magazine 2 (1999)Google Scholar
  27. 27.
    Xu, L., Perros, H.G., Rouskas, G.N.: A Simulation Study of Access Protocols for Optical Burst-Switched Ring Networks. In: Proceedings of Networking (2002)Google Scholar
  28. 28.
    Aysegul, G., Biswanath, M.: Virtual-Topology Adaptation for WDM Mesh Networks Under Dynamic Traffic. IEEE/ACM Transactions on Networking 11, 236–247 (2003)CrossRefGoogle Scholar
  29. 29.
    NVIDIA. NVIDIA CUDA Compute Unified Device Architecture, Programming Guide. NVIDIA (2007), http://www.nvidia.com/object/cuda_develop.html
  30. 30.
    Dolzer, K., Gauger, C.: On burst assembly in optical burst switching networks - a performance evaluation of Just-Enough-Time. In: Proceedings of the 17th International Teletraffic Congress (ITC 17), Salvador (2001)Google Scholar
  31. 31.
    Barakat, N., Sargent, E.H.: Analytical Modeling of Offset-Induced Priority in Multiclass OBS Networks. IEEE Transactions on Communications 53(8), 1343–1352 (2005)CrossRefGoogle Scholar
  32. 32.
    Matsumoto, M., Nishimura, T.: Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudo-Random Number Generator. ACM Transactions on Modelling and Computer Simulation (TOMACS) 8(1), 3–30 (1998)CrossRefMATHGoogle Scholar
  33. 33.
    Hu, G., Dolzer, K., Gauger, C.: Does Burst Assembly Really Reduce the Self-Similarity. In: Optical Fiber Communications Conference, OFC 2003, vol. 1, pp. 124–126 (2003)Google Scholar
  34. 34.
    Triolet, D.: NVidia CUDA: Preview - BeHardware (2007), http://www.behardware.com/art/imprimer/659
  35. 35.
    Feldmann, A.: Characteristics of TCP Connection Arrivals. In: Self-Similar Network Traffic and Performance Evaluation, pp. 367–397. Wiley Interscience, Hoboken (2000)CrossRefGoogle Scholar
  36. 36.
    Khronos Group.: OpenCL - The open standard for parallel programming of heterogeneous systems, http://www.khronos.org/opencl/
  37. 37.
    Park, K., Willinger, W.: Self-Similar Network Traffic: An Overview. In: Park, K., Willinger, W. (eds.) Self-Similar Network Traffic and Performance Evaluation, pp. 1–38 (2000)Google Scholar
  38. 38.
    Mouchos, C., Tsokanos, A., Kouvatsos, D.D.: Dynamic OBS Offset Allocation in WDM Networks. Computer Communications (COMCOM) - The International Journal for the Computer and Telecommunications Industry, Special Issue on ’Heterogeneous Networks: Traffic Engineering and Performance Evaluation 31(suppl. 1) (to appear mid, 2010), (in Press Corrected Proof), ISSN 0140-3664, doi: 10.1016/j.comcom.2010.04.009, http://www.sciencedirect.com/science/article/B6TYP-4YVY769-1/2/e3903ceb381e6d5f30adf33d1824281a (available online April 16, 2010)
  39. 39.
    Mouchos, C., Kouvatsos, D.D.: Parallel Traffic Generation of a Decomposed Optical Edge Node on a GPU, in Traffic and Performance Engineering for Heterogeneous Networks. In: Kouvatsos, D.D. (ed.) Performance Modelling and Analysis of Heterogeneous Networks, ch. 20, Aalborg, Denmark. Series of Information Science 7 Technology, vol. 2, pp. 417–439. River Publishers (2009), ISBN 978-87-92329-18-9Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Harry Mouchos
    • 1
  • Athanasios Tsokanos
    • 1
  • Demetres D. Kouvatsos
    • 1
  1. 1.NetPen - Networks and Performance Engineering Research Unit, Informatics Research Institute (IRI)University of BradfordBradfordUK

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