Skip to Content
[CAIDA - Center for Applied Internet Data Analysis logo]
Center for Applied Internet Data Analysis
A Nonstationary Poisson View of Internet Traffic
T. Karagiannis, M. Molle, M. Faloutsos, and A. Broido, "A Nonstationary Poisson View of Internet Traffic", in IEEE Conference on Computer Communications (INFOCOM), Mar 2004, pp. 1558--1569.
|   View full paper:    PDF    gzipped postscript    |  Citation:    BibTeX   |

A Nonstationary Poisson View of Internet Traffic

Thomas Karagiannis 2
Mart Molle 2
Michalis Faloutsos 2
Andre Broido 1

CAIDA, San Diego Supercomputer Center, University of California San Diego


Department of Computer Science & Engineering,
University of California, Riverside

Since the identification of long-range dependence in network traffic ten years ago, its consistent appearance across numerous measurement studies has largely discredited Poissonbased models. However, since that original data set was collected, both link speeds and the number of Internet-connected hosts have increased by more than three orders of magnitude. Thus, we now revisit the Poisson assumption, by studying a combination of historical traces and new measurements obtained from a major backbone link belonging to a Tier 1 ISP. We show that unlike the older data sets, current network traffic can be well represented by the Poisson model for sub-second time scales. At multi-second scales, we find a distinctive piecewise-linear non-stationarity, together with evidence of long-range dependence. Combining our observations across both time scales leads to a time-dependent Poisson characterization of network traffic that, when viewed across very long time scales, exhibits the observed long-range dependence. This traffic characterization reconciliates the seemingly contradicting observations of Poisson and long-memory traffic characteristics. It also seems to be in general agreement with recent theoretical models for large-scale traffic aggregation.

Keywords: passive data analysis
  Last Modified: Wed Oct-11-2017 17:03:50 PDT
  Page URL: