Decentralizing Network Inference Problems with Multiple-Description Fusion Estimation (MDFE)

Two forms of network inference (or tomography) problems have been studied rigorously: traffic matrix estimation or completion based on link-level traffic measurements, and link-level loss or delay inference based on end-to-end measurements. These problems are often posed as UnderDetermined Linear Inverse (UDLI) problems and solved in a centralized manner, where all the measurements are collected at a central node, which then applies a variety of inference techniques to estimate the attributes of interest. This paper proposes a novel framework for decentralizing these large-scale UDLI network inference problems by intelligently partitioning it into smaller sub-problems and solving them independently and in parallel.

Provided by: University of Calgary Topic: Mobility Date Added: May 2013 Format: PDF

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