Traffic Matrix Estimation on a Large IP Backbone

- A Comparison on Real Data

                    
This paper considers the problem of estimating the point to point
traffic matrix in an operational IP backbone. Contrary to previous
studies, that have used a partial traffic matrix or demands estimated
from aggregated Netflow traces, we use a unique data set of complete
traffic matrices from a global IP network measured over five-minute
intervals. This allows us to do an accurate data analysis on the
time-scale of typical link-load measurements and enables us to make a
balanced evaluation of different traffic matrix estimation techniques. 
We describe the data collection infrastructure, present spatial and
temporal demand distributions, investigate the stability of fan-out
factors, and analyze the mean-variance relationships between demands. 
We perform a critical evaluation of existing and novel methods for traffic 
matrix estimation, including recursive fanout estimation, worst-case
bounds, regularized estimation techniques, and methods that rely on
mean-variance relationships. We discuss the weaknesses and strengths of
the various methods, and highlight ddifferences in the results for the
European and American subnetworks.