A long range dependent internet traffic model using unbounded johnson distribution

Sunggon Kim, Seung Yeob Nam

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

It is important to characterize the distributional property and the long-range dependency of traffic arrival processes in modeling Internet traffic. To address this problem, we propose a long-range dependent traffic model using the unbounded Johnson distribution. Using the proposed model, a sequence of traffic rates with the desired four quantiles and Hurst parameter can be generated. Numerical studies show how well the sequence of traffic rates generated by the proposed model mimics that of the real traffic rates using a publicly available Internet traffic trace.

Original languageEnglish
Pages (from-to)301-304
Number of pages4
JournalIEICE Transactions on Communications
VolumeE96-B
Issue number1
DOIs
StatePublished - Jan 2013

Keywords

  • Fractional Gaussian noise
  • Internet traffic modeling
  • Long-range dependence
  • Unbounded Johnson distribution

Fingerprint

Dive into the research topics of 'A long range dependent internet traffic model using unbounded johnson distribution'. Together they form a unique fingerprint.

Cite this