A State-Space Approach for Optimal Traffic Monitoring via Network Flow Sampling

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 prob-lem of optimal large-scale flow monitoring of computer net-works under resource constraints. We propose a stochas-tic 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 net-work flows and sampling constraints at each monitoring sta-tion, we seek an optimal packet sampling strategy that yields the “best " traffic volume estimation for all flows of the net-work. 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. 1