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- Publisher Website: 10.1016/j.sigpro.2007.02.010
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Article: Robust estimation of the self-similarity parameter in network traffic using wavelet transform
Title | Robust estimation of the self-similarity parameter in network traffic using wavelet transform |
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Authors | |
Keywords | Non-stationarity Internet traffic Hurst parameter Long-range dependence |
Issue Date | 2007 |
Citation | Signal Processing, 2007, v. 87, n. 9, p. 2111-2124 How to Cite? |
Abstract | This 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 Identifier | http://hdl.handle.net/10722/219534 |
ISSN | 2023 Impact Factor: 3.4 2023 SCImago Journal Rankings: 1.065 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Shen, Haipeng | - |
dc.contributor.author | Zhu, Zhengyuan | - |
dc.contributor.author | Lee, Thomas C M | - |
dc.date.accessioned | 2015-09-23T02:57:19Z | - |
dc.date.available | 2015-09-23T02:57:19Z | - |
dc.date.issued | 2007 | - |
dc.identifier.citation | Signal Processing, 2007, v. 87, n. 9, p. 2111-2124 | - |
dc.identifier.issn | 0165-1684 | - |
dc.identifier.uri | http://hdl.handle.net/10722/219534 | - |
dc.description.abstract | This 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.language | eng | - |
dc.relation.ispartof | Signal Processing | - |
dc.subject | Non-stationarity | - |
dc.subject | Internet traffic | - |
dc.subject | Hurst parameter | - |
dc.subject | Long-range dependence | - |
dc.title | Robust estimation of the self-similarity parameter in network traffic using wavelet transform | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.sigpro.2007.02.010 | - |
dc.identifier.scopus | eid_2-s2.0-34247624070 | - |
dc.identifier.volume | 87 | - |
dc.identifier.issue | 9 | - |
dc.identifier.spage | 2111 | - |
dc.identifier.epage | 2124 | - |
dc.identifier.isi | WOS:000247090000010 | - |
dc.identifier.issnl | 0165-1684 | - |