Spatio-Temporal Compressive Sensing and Internet Traffic Matrices

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Executive Summary

Many basic network engineering tasks (e.g., traffic engineering, capacity planning, and anomaly detection) rely heavily on the availability and accuracy of traffic matrices. However, in practice it is challenging to reliably measure traffic matrices. Missing values are common. This observation brings one into the realm of compressive sensing, a generic technique for dealing with missing values that exploits the presence of structure and redundancy in many realworld systems. Despite much recent progress made in compressive sensing, existing compressive-sensing solutions often perform poorly for traffic matrix interpolation, because real traffic matrices rarely satisfy the technical conditions required for these solutions.

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