ENBIS: European Network for Business and Industrial Statistics

Optimal Sampling in State Space Models with Applications to Network Monitoring

23 September 2009, 10:05 – 10:25

Abstract

Submitted by

George Michailidis

Authors

George Michailidis

Affiliation

The University of Michigan

Abstract

Advances in networking technology have enabled network engineers to use sampled data from routers to estimate network flow volumes and track them over time. However, low sampling rates result in large noise in traffic volume estimates. We propose to combine data on individual flows obtained from sampling with highly aggregate data for the tracking problem at hand. Specifically, we introduce a linearized state space model for the estimation of network traffic flow volumes from the combined data. Further, we formulate the problem of obtaining optimal sampling rates under router resource constraints as an experiment design problem. The usefulness of the approach in the context of network monitoring is illustrated on both emulated and real network data.

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