On the Distribution of Traffic Volumes in the Internet and its Implications

Submitted by richard on Fri, 06/28/2019 - 10:23
Mohammed Alasmar, George Parisis, Richard G. Clegg and Nickolay Zakhleniuk
Proc IEEE Infocom
Year
2019
Abstract
Getting good statistical models of traffic on network
links is a well-known, often-studied problem. A lot of attention
has been given to correlation patterns and flow duration. The
distribution of the amount of traffic per unit time is an equally
important but less studied problem. We study a large number of
traffic traces from many different networks including academic,
commercial and residential networks using state-of-the-art sta-
tistical techniques. We show that the log-normal distribution is a
better fit than the Gaussian distribution commonly claimed in the
literature. We also investigate a second heavy-tailed distribution
(the Weibull) and show that its performance is better than
Gaussian but worse than log-normal. We examine anomalous
traces which are a poor fit for all distributions tried and show
that this is often due to traffic outages or links that hit maximum
capacity.
We demonstrate the utility of the log-normal distribution
in two contexts: predicting the proportion of time traffic will
exceed a given level (for service level agreement or link capacity
estimation) and predicting 95th percentile pricing. We also show
the log-normal distribution is a better predictor than Gaussian
or Weibull distributions.
Description

This paper updates previous work on fitting traffic profiles. We use more modern statistical techniques to question (and refute) previous assumptions about heavy tails in statistics. In this case we believe that the best fit for traffic volume per unit time is the log-normal distribution. Tail distributions an have big impacts for capacity planning and for prediction of pricing (say 95th percentile).

Preprint
bibtex
@INPROCEEDINGS{infocom_traffic_2019,
author={M. {Alasmar} and G. {Parisis} and R. {Clegg} and N. {Zakhleniu}},
booktitle={Proc. IEEE INFOCOM},
title={On the Distribution of Traffic Volumes in the Internet and its Implications},
year={2019},
}

doi
https://doi.org/10.1109/INFOCOM.2019.8737483
Paper type