Bivariate statistical analysis of TCP-flow sizes and durations

Natalia M. Markovich, Jorma Kilpi

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)

Abstract

We approximate the distribution of the TCP-flow rate by deriving it from the joint bivariate distribution of the flow sizes and flow durations of a given access network. The latter distribution is represented by a bivariate extreme value distribution using the Pickand’s dependence A-function. We estimate the A-function to measure the dependencies of random pairs: TCP-flow size and duration, the rate of TCP-flow and size, as well as the rate and duration. We provide a method to test that the achieved estimate of A-function is good and perform the analysis with one concrete data example.
Original languageEnglish
Pages (from-to)199-216
Number of pages18
JournalAnnals of Operations Research
Volume170
Issue number1
DOIs
Publication statusPublished - 2009
MoE publication typeA1 Journal article-refereed

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Statistical analysis
Extreme values
Network access

Keywords

  • TCP-flow
  • Extreme value distribution
  • Pickands function

Cite this

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Bivariate statistical analysis of TCP-flow sizes and durations. / Markovich, Natalia M.; Kilpi, Jorma.

In: Annals of Operations Research, Vol. 170, No. 1, 2009, p. 199-216.

Research output: Contribution to journalArticleScientificpeer-review

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T1 - Bivariate statistical analysis of TCP-flow sizes and durations

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AU - Kilpi, Jorma

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AB - We approximate the distribution of the TCP-flow rate by deriving it from the joint bivariate distribution of the flow sizes and flow durations of a given access network. The latter distribution is represented by a bivariate extreme value distribution using the Pickand’s dependence A-function. We estimate the A-function to measure the dependencies of random pairs: TCP-flow size and duration, the rate of TCP-flow and size, as well as the rate and duration. We provide a method to test that the achieved estimate of A-function is good and perform the analysis with one concrete data example.

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KW - Extreme value distribution

KW - Pickands function

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SP - 199

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