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Article: Robust estimation of the self-similarity parameter in network traffic using wavelet transform

TitleRobust estimation of the self-similarity parameter in network traffic using wavelet transform
Authors
KeywordsNon-stationarity
Internet traffic
Hurst parameter
Long-range dependence
Issue Date2007
Citation
Signal Processing, 2007, v. 87, n. 9, p. 2111-2124 How to Cite?
AbstractThis article studies the problem of estimating the self-similarity parameter of network traffic traces. A robust wavelet-based procedure is proposed for this estimation task of deriving estimates that are less sensitive to some commonly encountered non-stationary traffic conditions, such as sudden level shifts and breaks. Two main ingredients of the proposed procedure are: (i) the application of a robust regression technique for estimating the parameter from the wavelet coefficients of the traces, and (ii) the proposal of an automatic level shift removal algorithm for removing sudden jumps in the traces. Simulation experiments are conducted to compare the proposed estimator with existing wavelet-based estimators. The proposed estimator is also applied to real traces obtained from the Abilene Backbone Network and a university campus network. Both results from simulated experiments and real trace applications suggest that the proposed estimator is superior. © 2007 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/219534
ISSN
2015 Impact Factor: 2.063
2015 SCImago Journal Rankings: 1.119

 

DC FieldValueLanguage
dc.contributor.authorShen, Haipeng-
dc.contributor.authorZhu, Zhengyuan-
dc.contributor.authorLee, Thomas C M-
dc.date.accessioned2015-09-23T02:57:19Z-
dc.date.available2015-09-23T02:57:19Z-
dc.date.issued2007-
dc.identifier.citationSignal Processing, 2007, v. 87, n. 9, p. 2111-2124-
dc.identifier.issn0165-1684-
dc.identifier.urihttp://hdl.handle.net/10722/219534-
dc.description.abstractThis article studies the problem of estimating the self-similarity parameter of network traffic traces. A robust wavelet-based procedure is proposed for this estimation task of deriving estimates that are less sensitive to some commonly encountered non-stationary traffic conditions, such as sudden level shifts and breaks. Two main ingredients of the proposed procedure are: (i) the application of a robust regression technique for estimating the parameter from the wavelet coefficients of the traces, and (ii) the proposal of an automatic level shift removal algorithm for removing sudden jumps in the traces. Simulation experiments are conducted to compare the proposed estimator with existing wavelet-based estimators. The proposed estimator is also applied to real traces obtained from the Abilene Backbone Network and a university campus network. Both results from simulated experiments and real trace applications suggest that the proposed estimator is superior. © 2007 Elsevier B.V. All rights reserved.-
dc.languageeng-
dc.relation.ispartofSignal Processing-
dc.subjectNon-stationarity-
dc.subjectInternet traffic-
dc.subjectHurst parameter-
dc.subjectLong-range dependence-
dc.titleRobust estimation of the self-similarity parameter in network traffic using wavelet transform-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.sigpro.2007.02.010-
dc.identifier.scopuseid_2-s2.0-34247624070-
dc.identifier.volume87-
dc.identifier.issue9-
dc.identifier.spage2111-
dc.identifier.epage2124-

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