  The robustness and integrity of IP networks require efficient tools for
traffic monitoring and analysis, which scale well with traffic volume and
network size. We address the problem of optimal large-scale flow monitoring of
computer networks under resource constraints. We propose a stochastic
optimization framework where traffic measurements are done by exploiting the
spatial (across network links) and temporal relationship of traffic flows.
Specifically, given the network topology, the state-space characterization of
network flows and sampling constraints at each monitoring station, we seek an
optimal packet sampling strategy that yields the best traffic volume estimation
for all flows of the network. The optimal sampling design is the result of a
concave minimization problem; then, Kalman filtering is employed to yield a
sequence of traffic estimates for each network flow. We evaluate our algorithm
using real-world Internet2 data.
