On the distribution of traffic volumes in the internet and its implications

Alasmar, Mohammed, Parisis, George, Clegg, Richard and Zakhleniuk, Nickolay (2018) On the distribution of traffic volumes in the internet and its implications. The 38th IEEE International Conference on Computer Communications (INFOCOM 2019), Paris, France, 29th April-2nd May 2019. Published in: Proceedings of the 38th Annual IEEE International Conference on Computer Communications,. (Accepted)

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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.

Item Type: Conference Proceedings
Keywords: Traffic modelling; Network planning; Bandwidth provisioning; Traffic billing
Schools and Departments: School of Engineering and Informatics > Informatics
Research Centres and Groups: Foundations of Software Systems
Related URLs:
Depositing User: Georgios Angelos Parisis
Date Deposited: 03 Dec 2018 16:11
Last Modified: 19 Dec 2018 16:06
URI: http://srodev.sussex.ac.uk/id/eprint/80551

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