A pragmatic approach to dealing with high-variability in network measurements

The Internet is teeming with high variability phenomena, from measured IP flow sizes to aspects of inferred router-level connectivity, but there still exists considerable debate about how best to deal with this encountered high variability and model it. While one popular approach favors modeling highly variable event sizes with conventional, finite variance distributions such as lognormal or Weibull distributions, Mandelbrot has argued for the last 40 years that there are compelling mathematical, statistical, and practical reasons for why infinite variance distributions are natural candidates for capturing the essence behind high variability phenomena. In this paper, we elaborate on Mandelbrot’s arguments and present a methodology that often allows for a clear distinction between the two approaches. In particular, by requiring the resulting models to be resilient to ambiguities (i.e., robust to real-world deficiencies in the underlying network measurements) and internally self-c...

• Type: Conference
• Year: 2004
• Where: IMC
• Authors: Walter Willinger, David Alderson, Lun Li
• Keywords: IMC 2004, Infinite Variance Distributions, Variance Distributions, finite Variance Distributions