Traffic Matrix
Estimation: Existing Techniques and New Directions. A. Medina (Sprint Labs, Boston University) , N. Taft (Sprint Labs), K. Salamatian (University of Paris VI), S. Bhattacharyya,
C. Diot (Sprint Labs)
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 {\it 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.
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