Advertisement

Abstract

Estimation of traffic demand is a major requirement in telecommunication network operation and management. As traffic level typically varies with time, online applications such as dynamic routing and dynamic capacity allocation need to accurately estimate traffic in real time to optimize network operations. Traffic mean can be estimated using known filtering methods such as moving averages or exponential smoothing. In this paper, we analyze online traffic estimation based on exponential smoothing, with focus on response and stability. Novel approaches, based on traffic arrivals autocorrelation and cumulative distribution functions, are proposed to adapt estimation parameters to varying traffic trends. Performance of proposed approaches is compared to other adaptive exponential smoothing methods found in the literature. The results show that our approach based on autocorrelation function gives the best combined response-stability performance.

Keywords

Traffic measurement estimation adaptation trend detection exponential smoothing autocorrelation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Thompson, K., Miller, G.J., Wilder, R.: Wide-area Internet Traffic Patterns and Characteristics. IEEE Network 11(6), 10–23 (1997)CrossRefGoogle Scholar
  2. 2.
    Roberts, J.W.: Internet Traffic, QoS, and Pricing. Proceedings of the IEEE 92(9), 1389–1399 (2004)CrossRefGoogle Scholar
  3. 3.
    Krishnan, K.R.: Adaptive State-dependent Traffic Routing Using On-line Trunk-group Measurements. In: Proc. of the Thirteenth International Teletraffic Congress, Copenhagen, pp. 407–411 (1991)Google Scholar
  4. 4.
    Sultan, A., Girard, A.: Adaptive Implementation of the Revenue-maximization Optimal Routing Algorithm. In: Proc. of Telecommunication Systems, Modeling and Analysis Conference, Nashville, pp. 371–374 (1993)Google Scholar
  5. 5.
    Dziong, Z.: ATM Network Resource Management, ch. 5. McGraw-Hill, New York (1997)Google Scholar
  6. 6.
    Tu, M.: Estimation of Point-to-point Traffic Demand in the Public Switched Telephone Network. IEEE Transactions on Communications 42(2-4), 840–845 (1994)CrossRefGoogle Scholar
  7. 7.
    Duan, Z., Zhang, Z.-L., Hou, Y.T.: Service Overlay Networks: SLAs, QoS, and Bandwidth Provisioning. IEEE/ACM Transactions on Networking 11(6), 870–883 (2003)CrossRefGoogle Scholar
  8. 8.
    Krithikaivasan, B., Deka, K., Medhi, D.: Adaptive Bandwidth Provisioning Envelope Based on Discrete Temporal Network Measurements. In: Proc. of IEEE INFOCOM 2004 Conference on Computer Communications, Hong Kong, vol. 3, pp. 1786–1796 (2004)Google Scholar
  9. 9.
    Anjali, T., Bruni, C., Iacoviello, D., Koch, G., Scoglio, C.: Filtering and Forecasting Problems for Aggregate Traffic in Internet Links. Performance Evaluation 58(1), 25–42 (2004)CrossRefGoogle Scholar
  10. 10.
    Anjali, T., Scoglio, C., Uhl, G.: A New Scheme for Traffic Estimation and Resource Allocation for Bandwidth Brokers. Computer Networks 41(6), 761–777 (2003)CrossRefzbMATHGoogle Scholar
  11. 11.
    Tran, C., Dziong, Z.: Service Overlay Network Capacity Adaptation for Profit Maximization. IEEE Transactions on Network and Service Management 7(2), 72–82 (2010)CrossRefGoogle Scholar
  12. 12.
    Dasgupta, S., de Oliveira, J.C., Vasseur, J.-P.: Trend Based Bandwidth Provisioning: an Online Approach for Traffic Engineered Tunnels. In: Proc. of 4th EURO-NGI Conference on Next Generation Internet Networks, Krakow, pp. 53–60 (2008)Google Scholar
  13. 13.
    Gardner, E.S.: Exponential Smoothing: the State of the Art – Part II. International Journal of Forecasting 22(4), 637–666 (2006)CrossRefGoogle Scholar
  14. 14.
    Mentzer, J.T.: Forecasting with Adaptive Extended Exponential Smoothing. Journal of the Academy of Marketing Science 16(3-4), 62–70 (1988)CrossRefGoogle Scholar
  15. 15.
    Mentzer, J.T., Gomes, R.: Further Extensions of Adaptive Extended Exponential Smoothing and Comparison with the M-competition. Journal of the Academy of Marketing Science 22(4), 372–382 (1994)CrossRefGoogle Scholar
  16. 16.
    Ching, W.-K., Scholtes, S., Zhang, S.-Q.: Numerical Algorithms for Dynamic Traffic Demand Estimation Between Zones in a Network. Engineering Optimization 36(3), 379–400 (2004)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Paxson, V., Floyd, S.: Wide Area Traffic: the Failure of Poisson Modeling. IEEE/ACM Transactions on Networking 3(3), 226–244 (1995)CrossRefGoogle Scholar
  18. 18.
    WAND Network Research Group, WITS: Waikato Internet Traffic Storage, http://www.wand.net.nz/wits/
  19. 19.
    Makridakis, S., et al.: The Accuracy of Extrapolation (Time Series) Methods: Results of a Forecasting Competition. Journal of Forecasting 1(2), 111–153 (1982)CrossRefGoogle Scholar
  20. 20.
    Chatfield, C.: The Analysis of Time Series: An Introduction, 6th edn., section 2. Chapman & Hall/CRC, Boca Raton (2003)zbMATHGoogle Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2012

Authors and Affiliations

  • Con Tran
    • 1
  • Zbigniew Dziong
    • 1
  1. 1.Department of Electrical EngineeringEcole de Technologie SuperieureMontrealCanada

Personalised recommendations