Variance of Aggregated Web Traffic

Robert Morris and Dong Lin

Published in the Proceedings of the IEEE INFOCOM 2000 Conference


If data traffic were Poisson, increases in the amount of traffic aggregated on a network would rapidly decrease the relative size of bursts. The discovery of pervasive long-range dependence demonstrates that real network traffic is burstier than any possible Poisson model. We present evidence that, despite being non-Poisson, aggregating Web traffic causes it to smooth out as rapidly as Poisson traffic. That is, the relationship between changes in mean bandwidth and changes in variance is the same for Web traffic as it is for Poisson traffic.

We derive our evidence from traces of real traffic in two ways. First, by observing how variance changes over the large range of mean bandwidths present in 24 hour traces. Second, by observing the relationship of variance and mean bandwidth for individual users and combinations of users. Our conclusion, that variance changes linearly with mean bandwidth, should be useful (and encouraging) to anyone provisioning a network for a large aggregate load of Web traffic.

PostScript, PDF.