GARCH model-based large-scale IP traffic matrix estimation

This letter proposes a novel method to estimate large-scale IP traffic matrix (TM). By using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) to model the Origin-Destination (OD) flows, we can easily get rid of the ill-posed problem of large-scale IP TM. Compared with previous m...

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Veröffentlicht in:	IEEE communications letters 2009-01, Vol.13 (1), p.52-54
Hauptverfasser:	Jiang, Dingde, Hu, Guangmin
Format:	Artikel
Sprache:	eng
Schlagworte:	Applied sciences Equations Errors Estimates Estimation error Exact sciences and technology GARCH Gravity High-speed networks Ill posed problems IP (Internet Protocol) IP networks Large-scale systems Mathematical models Noise Noise robustness nonstationary traffic Routing Systems, networks and services of telecommunications Telecommunication traffic Telecommunications Telecommunications and information theory Teletraffic Traffic control Traffic engineering Traffic flow Traffic matrix traffic matrix estimation
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Beschreibung
Zusammenfassung:	This letter proposes a novel method to estimate large-scale IP traffic matrix (TM). By using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) to model the Origin-Destination (OD) flows, we can easily get rid of the ill-posed problem of large-scale IP TM. Compared with previous methods, our method does not only hold the lower estimation errors but also is more robust to the noise.
ISSN:	1089-7798 1558-2558
DOI:	10.1109/LCOMM.2008.081271