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Large scale cross-correlations in Internet traffic.

Marc Barthélemy1, Bernard Gondran, Eric Guichard

  • 1CEA, Service de Physique de la Matière Condensée, Boîte Postale 12, Bruyères-le-Châtel, France.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|January 7, 2003
PubMed
Summary
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Researchers analyzed internet traffic correlations using random matrix theory. They discovered network-specific information in large eigenvalues, identifying active centers that influence traffic patterns and potentially explain web traffic self-similarity.

Area of Science:

  • Network Science
  • Complex Systems
  • Data Analysis

Background:

  • Internet congestion indicates crucial correlations between network connections.
  • Quantifying these correlations is essential for understanding network behavior.

Purpose of the Study:

  • To measure and quantify correlations in internet traffic flow.
  • To identify the role of these correlations in network dynamics.

Main Methods:

  • Applied random matrix theory (RMT) to analyze the cross-correlation matrix of information flow changes.
  • Examined 650 connections across 26 routers in the French scientific network 'Renater'.

Main Results:

  • The cross-correlation matrix exhibits universal properties of the Gaussian orthogonal ensemble (GOE) of random matrices.

Related Experiment Videos

  • Deviations in large eigenvalues reveal genuine, network-specific correlations.
  • Identified 'active centers' of routers exchanging information widely, inducing significant correlations.

    • Conclusions:

    • Internet traffic correlations align with RMT predictions, with notable deviations indicating specific network structures.
    • Active centers are key drivers of correlations, potentially explaining self-similarity in web traffic.