Fitting World-Wide Web Request Traces with the EM-Algorithm

Rachid El Abdouni Khayari, R. Sadre, Boudewijn R.H.M. Haverkort

Research output: Contribution to conferencePaperAcademicpeer-review

10 Citations (Scopus)
10 Downloads (Pure)

Abstract

In recent years, several studies have shown that network traffic exhibits the property of self-similarity. Traditional (Poissonian) modelling approaches have been shown not to be able to describe this property and generally lead to the underestimation of interesting performance measures. Crovella and Bestavros have shown that network traffic that is due to World Wide Web transfers shows characteristics of self-similarity and they argue that this can be explained by the heavy-tailedness of many of the involved distributions. Considering these facts, developing methods which are able to handle self-similarity and heavy-tailedness is of great importance for network capacity planing purposes. In this paper we discuss two methods to fit hyper-exponential distributions to data sets which exhibit heavy-tails. One method is taken from the literature and shown to fall short. The other, new method, is shown to perform well in a number of case studies.
Original languageUndefined
Pages211-220
Number of pages10
DOIs
Publication statusPublished - 2001

Keywords

  • IR-63635
  • EWI-7908

Cite this

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title = "Fitting World-Wide Web Request Traces with the EM-Algorithm",
abstract = "In recent years, several studies have shown that network traffic exhibits the property of self-similarity. Traditional (Poissonian) modelling approaches have been shown not to be able to describe this property and generally lead to the underestimation of interesting performance measures. Crovella and Bestavros have shown that network traffic that is due to World Wide Web transfers shows characteristics of self-similarity and they argue that this can be explained by the heavy-tailedness of many of the involved distributions. Considering these facts, developing methods which are able to handle self-similarity and heavy-tailedness is of great importance for network capacity planing purposes. In this paper we discuss two methods to fit hyper-exponential distributions to data sets which exhibit heavy-tails. One method is taken from the literature and shown to fall short. The other, new method, is shown to perform well in a number of case studies.",
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author = "{El Abdouni Khayari}, Rachid and R. Sadre and Haverkort, {Boudewijn R.H.M.}",
note = "Imported from research group DACS (ID number 470)",
year = "2001",
doi = "10.1117/12.434316",
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}

Fitting World-Wide Web Request Traces with the EM-Algorithm. / El Abdouni Khayari, Rachid; Sadre, R.; Haverkort, Boudewijn R.H.M.

2001. 211-220.

Research output: Contribution to conferencePaperAcademicpeer-review

TY - CONF

T1 - Fitting World-Wide Web Request Traces with the EM-Algorithm

AU - El Abdouni Khayari, Rachid

AU - Sadre, R.

AU - Haverkort, Boudewijn R.H.M.

N1 - Imported from research group DACS (ID number 470)

PY - 2001

Y1 - 2001

N2 - In recent years, several studies have shown that network traffic exhibits the property of self-similarity. Traditional (Poissonian) modelling approaches have been shown not to be able to describe this property and generally lead to the underestimation of interesting performance measures. Crovella and Bestavros have shown that network traffic that is due to World Wide Web transfers shows characteristics of self-similarity and they argue that this can be explained by the heavy-tailedness of many of the involved distributions. Considering these facts, developing methods which are able to handle self-similarity and heavy-tailedness is of great importance for network capacity planing purposes. In this paper we discuss two methods to fit hyper-exponential distributions to data sets which exhibit heavy-tails. One method is taken from the literature and shown to fall short. The other, new method, is shown to perform well in a number of case studies.

AB - In recent years, several studies have shown that network traffic exhibits the property of self-similarity. Traditional (Poissonian) modelling approaches have been shown not to be able to describe this property and generally lead to the underestimation of interesting performance measures. Crovella and Bestavros have shown that network traffic that is due to World Wide Web transfers shows characteristics of self-similarity and they argue that this can be explained by the heavy-tailedness of many of the involved distributions. Considering these facts, developing methods which are able to handle self-similarity and heavy-tailedness is of great importance for network capacity planing purposes. In this paper we discuss two methods to fit hyper-exponential distributions to data sets which exhibit heavy-tails. One method is taken from the literature and shown to fall short. The other, new method, is shown to perform well in a number of case studies.

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KW - EWI-7908

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