Inference and Labeling of Metric-Induced Network Topologies.

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, should enable a representation of properties along temporal, spatial, and measurement resolution dimensions. In this paper, 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 performance metrics, and describe PERISCOPE, a Linux embodiment of MINT constructions. We instantiate MINT and PERISCOPE for a specific metric of interest—namely, packet loss rates—and present results of simulations and Internet measurements that confirm the effectiveness and robustness of our constructions over a wide range of network conditions. [ABSTRACT FROM AUTHOR]