 
Efficient Estimation of EndToEnd Network Properties
Eric Kolaczyk
Boston University
It is often desirable to monitor endtoend properties, such as loss rates
or packet delays, across an entire computer network. However, active
endtoend measurement in such settings does not scale well, and so
complete networkwide measurement quickly becomes infeasible. More
efficient measurement strategies are therefore needed. Previous work,
examining this problem from a linear algebraic perspective, has shown that
for exact recovery of complete endtoend network properties, the number
of paths that need to be monitored can be reduced to approximately the
number of links in the network. Here we argue that in fact measurement
strategies of even greater efficiency are possible.
We begin by recasting the problem as one of statistical prediction and
show that endtoend network properties may be accurately predicted in
many cases using a significantly smaller set of carefully chosen paths
than needed for exact recovery. We formulate a general framework for the
prediction problem, propose a simple class of predictors for standard
quantities of interest (e.g., averages, totals, differences), and show
that linear algebraic methods of subset selection may be used to make
effective choice of which paths to measure. We explore the accuracy of
the resulting methods both analytically and numerically, in the context of
real network topologies of varying size. The feasibility of our methods
derives from the low effective rank of routing matrices as encountered in
practice, which appears to be a new observation of interest in its own
right.
