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

Paper type: 
Conference paper
Mohammed Alasmar, George Parisis, Richard G. Clegg and Nickolay Zakhleniuk
Proc IEEE Infocom
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.
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).
@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}, }