Very few techniques have been propo

Very few techniques have been proposed for estimating traffic matrices
in the context of Internet traffic. Our work on POP-to-POP
traffic matrices (TM) makes two contributions. The primary contribution
is the outcome of a detailed comparative evaluation of the
three existing techniques. We evaluate these methods with respect
to the estimation errors yielded, sensitivity to prior information
required and sensitivity to the statistical assumptions they make.
We study the impact of characteristics such as path length and the
amount of link sharing on the estimation errors. Using actual data
from a Tier-1 backbone, we assess the validity of the typical assumptions
needed by the TM estimation techniques. The secondary
contribution of our work is the proposal of a new direction for TM
estimation based on using choice models to model POP fanouts.
These models allow us to overcome some of the problems of existing
methods because they can incorporate additional data and information
about POPs and they enable us to make a fundamentally
different kind of modeling assumption. We validate this approach
by illustrating that our modeling assumption matches actual Internet
data well. Using two initial simple models we provide a proof
of concept showing that the incorporation of knowledge of POP
features (such as total incoming bytes, number of customers, etc.)
can reduce estimation errors. Our proposed approach can be used
in conjunction with existing or future methods in that it can be used
to generate good priors that serve as inputs to statistical inference
techniques.