Linking network usage patterns to traffic Gaussianity fit

R. de Oliveira Schmidt, R. Sadre, Nikolay Melnikov, Jürgen Schönwälder, Aiko Pras

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    11 Citations (Scopus)
    4 Downloads (Pure)


    Gaussian traffic models are widely used in the domain of network traffic modeling. The central assumption is that traffic aggregates are Gaussian distributed. Due to its importance, the Gaussian character of network traffic has been extensively assessed by researchers in the past years. In 2001, researchers showed that the property of Gaussianity can be disturbed by traffic bursts. However, assumptions on network infrastructure and traffic composition made by the authors back in 2001 are not consistent with those of today's networks. The goal of this paper is to study the impact of traffic bursts on the degree of Gaussianity of network traffic. We identify traffic bursts, uncover applications and hosts that generate them and, ultimately, relate these findings to the Gaussianity degree of the traffic expressed by a goodness-of-fit factor. In our analysis we use recent traffic captures from 2011 and 2012. Our results show that Gaussianity can be directly linked to the presence or absence of extreme traffic bursts. In addition, we also show that even in a more homogeneous network, where hosts have similar access speeds to the Internet, we can identify extreme traffic bursts that might compromise Gaussianity fit.
    Original languageEnglish
    Title of host publicationProceedings of the 13th IFIP Networking Conference 2014
    Place of PublicationUSA
    PublisherIEEE Computer Society
    Number of pages9
    ISBN (Print)978-3-901882-58-6
    Publication statusPublished - Jun 2014
    Event13th IFIP Networking Conference 2014 - Trondheim, Norway
    Duration: 2 Jun 20144 Jun 2014


    Conference13th IFIP Networking Conference 2014
    Internet address


    • Traffic measurements
    • traffic analysis
    • IR-91456
    • Gaussian modeling
    • METIS-305885
    • EWI-24751


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