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Reseach Article

An ARIMA Based Approach for Traffic Prediction

Published on August 2011 by Sunil Kumar P V
International Conference on Information Systems and Technology
Foundation of Computer Science USA
ICIST - Number 1
August 2011
Authors: Sunil Kumar P V
d22b57ea-3ad5-4f7e-98bc-022e11c1dc00

Sunil Kumar P V . An ARIMA Based Approach for Traffic Prediction. International Conference on Information Systems and Technology. ICIST, 1 (August 2011), 7-10.

@article{
author = { Sunil Kumar P V },
title = { An ARIMA Based Approach for Traffic Prediction },
journal = { International Conference on Information Systems and Technology },
issue_date = { August 2011 },
volume = { ICIST },
number = { 1 },
month = { August },
year = { 2011 },
issn = 0975-8887,
pages = { 7-10 },
numpages = 4,
url = { /proceedings/icist/number1/3261-icist016/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Information Systems and Technology
%A Sunil Kumar P V
%T An ARIMA Based Approach for Traffic Prediction
%J International Conference on Information Systems and Technology
%@ 0975-8887
%V ICIST
%N 1
%P 7-10
%D 2011
%I International Journal of Computer Applications
Abstract

Network traffic prediction plays a vital role in the optimal resource allocation and management in computer networks. This paper introduces an ARIMA based model for the real time prediction of VBR video traffic. The methodology presented here can successfully addresses the challenges in traffic prediction such as accuracy in prediction, resource management and utilization. ARIMA application on a VBR video trace results in a component wise representation of the trace which in turn used for prediction. A brief introduction of the classic prediction scheme of ALP along with a quantitative comparison of the ARIMA with ALP is also presented. Performance evaluation of the proposed method is carried out using RMSE. The prediction accuracy is improved by 23% and the error variance is reduced by 18%.

References
  1. S. Kang, S. Lee, Y. Won, B. Seong, “On-Line Prediction of Nonstationary Variable-Bit-Rate Video Traffic”, IEEE Transacions on Signal Processing, vol. 58, No. 3, March 2010, pp 1219 - 1237.
  2. H. Zaho, N. Ansari, Y.Q Shi, “A Fast Non-linear Adaptive Algorithm for Video Traffic Prediction”, Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC.02), 2002.
  3. N. Doulamis, A. Doulamis, and S. Kollias, “Modeling and adaptive prediction of VBR MPEG video sources,” in Proc. IEEE ThirdWorkshop on Multimedia Signal Process., Sep. 1999, pp. 27 - 32.
  4. N. Rozic and M. Vojnovic, “Source modeling of MPEG video,” in Proc.IEEE GLOBECOM ‘97, 1997, pp. 1429 - 1433.
  5. D. Heyman, “The GBAR source model for VBR videoconferences’ ’IEEE/ACM Trans. Netw., vol. 5, pp. 554 - 560, 1997.
  6. Y. Won and S. Ahn, “GOP ARIMA: Modeling the non-stationarity of VBR process,” ACM/Springer Multimedia Syst. J., vol. 10, no. 5, pp.359 - 378, Aug. 2005.
  7. M. Grossglauser, S. Keshav, and D. N. C. Tse, “RCBR: A simple and efficientservice for multiple time-scale traffic,” IEEE/ACM Trans. Netw., vol. 5, no. 6, pp. 741 - 755, Dec. 1997
  8. S.-J. Yoo, “Efficient traffic prediction scheme for real-time VBR MPEG video transmission over high-speed networks,” IEEE Trans.Broadcasting, vol. 48, no. 1, pp. 10 - 18, 2002.
  9. A. Adas, “Using adaptive linear prediction to support real-time VBR video under RCBR network service model,” IEEE/ACM Trans. Netw., vol. 6, pp. 635 - 644,Oct. 1998.
  10. A. Sang, S. Li, “A Predictability Analysis of Network Traffic”, IEEE/ACM Trans. Netw., vol. 10, no. 8, pp. 71 - 75, Dec. 1999.
  11. W. Xu and A. G. Qureshi, “Adaptive Linear Prediction of MPEG Video Traffic”, Fifth International Symposium on Signal Processing and its Applications, ISSP ‘99, Brisbane, Australia, 22-25 August, 1999
  12. S Haykin, “Adaptive Filter Theory”; Englewood Cliffs, N J Prentice Hall,1991.
  13. A. Sang and S. Li, "A Predictability Analysis of Network Traffic", in Proc. INFOCOM, 2000, pp.342-351.
  14. Traces at: http:// www.traces.eas.asu.edu/
Index Terms

Computer Science
Information Sciences

Keywords

Traffic prediction ARIMA ALP VBR Video