A shifted gamma distribution model for long-range dependent Internet traffic

Sunggon Kim, Ju Yong Lee, Dan Keun Sung

Research output: Contribution to journalArticlepeer-review

46 Scopus citations

Abstract

It is important to characterize the distributional property and the correlation structure of traffic arrival processes in modeling internet traffic. The conventional fractional Gaussian noise(fGn) model fails in characterizing the distributional property when the distribution of the input traffic rates is nongaussian. We propose a shifted gamma distribution model which can solve this problem. A linear-time generation algorithm is also given.

Original languageEnglish
Pages (from-to)124-126
Number of pages3
JournalIEEE Communications Letters
Volume7
Issue number3
DOIs
StatePublished - Mar 2003

Keywords

  • Fractional Gaussian noise
  • Internet traffic modeling
  • Long-range dependence
  • Shifted gamma distribution

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