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Inference and Labeling of Metric-Induced Network Topologies
November 2005 (vol. 16 no. 11)
pp. 1053-1065

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, 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.

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Index Terms:
End-to-end measurement, packet-pair probing, Bayesian probing, Internet tomography, performance evaluation.
Azer Bestavros, John W. Byers, Khaled A. Harfoush, "Inference and Labeling of Metric-Induced Network Topologies," IEEE Transactions on Parallel and Distributed Systems, vol. 16, no. 11, pp. 1053-1065, Nov. 2005, doi:10.1109/TPDS.2005.138
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