Compressive sensing network inference with multiple-description fusion estimation

We have previously introduced Multiple Description Fusion Estimation (MDFE) framework that partitions a large-scale Under-Determined Linear Inverse (UDLI) problem into smaller sub-problems that can be solved independently and in parallel. The resulting estimates, referred to as multiple descriptions, can then be fused together to compute the global estimate. In this paper, we extend MDFE framework to make it compatible with Compressive Sensing (CS) network inference, where the attributes of interests (i.e. unknowns) are fluctuating rapidly over time and/or space. For this purpose, we propose a new clustering based technique to intelligently divide a large-scale compressive sensing problem into smaller sub-problems where observations between sub-spaces contain redundancy. We apply this new framework, referred to as Compressive Sensing MDFE (CS-MDFE), to three classical inference problems in networking: traffic matrix estimation, traffic matrix completion, and loss inference. Using real topologies and traces, we demonstrate how CS-MDFE can improve the estimation accuracy and speed up computation time, and how it enhances robustness against noise and failures. We also show that this framework is compatible with different CS inference techniques.