Traffic predictability based on ARIMA/GARCH model

The predictability of Internet traffic is a significant interest in many domains such as adaptive applications, congestion control, admission control, and network management. In this paper, we propose a new traffic prediction model called autoregressive integrated moving average with generalized autoregressive conditional heteroscedasticity (ARIMA/GARCH), which can capture traffic burstiness and exhibit self-similarity and long-range dependence (LRD). We discuss network traffic predictability related to different prediction applications and measure methods. We validate our prediction model by comparing with other models, includes non-model-based minimum mean square error (MMSE), pure self-similar fractional ARIMA (FARIMA). We use the real network traces to evaluate models. The results show that MMSE computation is simplest and fastest and can apply for online prediction applications. The results also show that FARIMA predictability relies on strong degree of self-similarity, our proposed ARIMA/GARCH model get the best adaptability and accuracy. Therefore ARIMA/GARCH model can be used for exact prediction applications

[1]  Christian Gouriéroux,et al.  Some Applications of Univariate ARCH Models , 1997 .

[2]  Richard G. Baraniuk,et al.  A Multifractal Wavelet Model with Application to Network Traffic , 1999, IEEE Trans. Inf. Theory.

[3]  Oliver W. W. Yang,et al.  Prediction-based admission control using FARIMA models , 2000, 2000 IEEE International Conference on Communications. ICC 2000. Global Convergence Through Communications. Conference Record.

[4]  Patrice Abry,et al.  Wavelet Analysis of Long-Range-Dependent Traffic , 1998, IEEE Trans. Inf. Theory.

[5]  Srinivasan Seshan,et al.  A network measurement architecture for adaptive applications , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[6]  Azer Bestavros,et al.  Self-similarity in World Wide Web traffic: evidence and possible causes , 1996, SIGMETRICS '96.

[7]  Kavitha Chandra,et al.  Time series models for Internet data traffic , 1999, Proceedings 24th Conference on Local Computer Networks. LCN'99.

[8]  Stephen F. Bush,et al.  Active virtual network management prediction: complexity as a framework for prediction, optimization, and assurance , 2002, Proceedings DARPA Active Networks Conference and Exposition.

[9]  Amarnath Mukherjee,et al.  Time series models for internet traffic , 1996, Proceedings of IEEE INFOCOM '96. Conference on Computer Communications.

[10]  San-qi Li,et al.  A predictability analysis of network traffic , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[11]  Hans-Werner Braun,et al.  The NLANR network analysis infrastructure , 2000, IEEE Commun. Mag..

[12]  Walter Willinger,et al.  On the self-similar nature of Ethernet traffic , 1993, SIGCOMM '93.

[13]  C. Gouriéroux.  ARCH Models and Financial Applications , 1997 .

[14]  Desmond P. Taylor,et al.  On the SelfSimilar Nature of Ethernet Traffic (Extended Version) , 2007 .

[15]  Walter Willinger,et al.  Self-similarity through high-variability: statistical analysis of Ethernet LAN traffic at the source level , 1997, TNET.

[16]  Van Jacobson,et al.  Congestion avoidance and control , 1988, SIGCOMM '88.

[17]  George C. Polyzos,et al.  A time series model of long-term NSFNET backbone traffic , 1994, Proceedings of ICC/SUPERCOMM'94 - 1994 International Conference on Communications.

[18]  R. Chou,et al.  ARCH modeling in finance: A review of the theory and empirical evidence , 1992 .

[19]  Richard A. Davis,et al.  Introduction to time series and forecasting , 1998 .

[20]  Stephen F. Bush,et al.  Active Virtual Network Management Prediction , 2001 .

[21]  Alireza Khotanzad,et al.  ATM dynamic bandwidth allocation using F-ARIMA prediction model , 2003, Proceedings. 12th International Conference on Computer Communications and Networks (IEEE Cat. No.03EX712).