Optimal Combination of Sampled Network Measurements

IP network traffic is commonly measured at multiple points in order that all traffic passes at least one observation point. The resulting measurements are subsequently joined for network analysis.

Many network management applications use measured traffic rates (differentiated into classes according to some key) as their input data. But two factors complicate the analysis. Traffic can be represented multiple times in the data, and the increasing use of sampling during measurement means some classes of traffic may be poorly represented.

In this paper, we show how to combine sampled traffic measurements in way that addresses both of the above issues. We construct traffic rate estimators that combine data from different measurement datasets with minimal or close to minimal variance. This is achieved by robust adaptation to the estimated variance of each constituent. We motivate the method with two applications: estimating the interface-level traffic matrix in a router, and estimating network-level flow rates from measurements taken at multiple routers.