Empirical testing of the infinite source poisson data traffice model
Journal article, 2003
The infinite source Poisson model is a fluid queue
approximation of network data transmission that
assumes that sources begin constant rate transmissions of data at Poisson
time points for random lengths of time. This model has been a popular
one as analysts attempt to provide explanations for observed features
in telecommunications data such as self-similarity, long range
dependence and heavy tails. We survey some features of this model
in cases where transmission length distributions have (a) tails so heavy
that means are infinite, (b) heavy tails with finite mean and
infinite variance and (c) finite variance. We survey the
self-similarity properties of various descriptor processes in this
model and then present analyses of four data sets which show that
certain features of the model are consistent with the data while
others are contradicted. The data sets are 1) the Boston University
1995 study of web sessions, 2) the UC Berkeley home IP HTTP
data collected in November 1996, 3) traces collected
in end of 1997 at a Customer Service Switch in Munich,
and 4) detailed data from a corporate Ericsson WWW server
from October 1998.
scaling
regular variation
heavy tails
Pareto tails
data transmission modelling
internet traffic
self-similarity