Inference and labeling of metric-induced network topologies

Abstract

The development and deployment of distributed network-aware applications and services require the ability to compile and maintain a model of the underlying network resources with respect to (one or more) characteristic properties of interest. To be manageable, such models must be compact, and to be general-purpose, they should enable a representation of properties along temporal, spatial, and measurement resolution dimensions. We propose MINT - a general framework for the construction of such metric-induced models using end-to-end measurements. We present the basic theoretical underpinnings of MINT for a broad class of metrics obeying certain properties. We instantiate MINT for two metrics of interest, namely packet loss rates and bottleneck bandwidth. For the loss rate metric, we leverage previously proposed end-to-end techniques for the estimation of shared losses to characterize loss topologies. We present results of simulations and Internet measurements that confirm the effectiveness and robustness of our loss topology constructions over a wide range of network conditions.