Structural Models for Dual Modality Data With Application to Network Tomography

Harsh Singhal, George Michailidis

Abstract: We propose models for the joint distribution of two modalities for network flow volumes. While these models are motivated by computer network applications, the underlying structural assumptions are more generally applicable. In the case of computer network flow volumes, this corresponds to joint modeling for packet and byte volumes and enables computer network tomography, whose goal is to estimate characteristics of source-destination flows based on aggregate link measurements. Network tomography is a prototypical example of a linear inverse problem on graphs. We introduce two generative models for the relation between packet and byte volumes, establish identifiability of their parameters, and discuss different estimating procedures. The proposed estimators of the flow characteristics are evaluated using both simulated and emulated data. Finally, the proposed models allow us to estimate parameters of the packet size distribution, thus providing additional insights into the composition of network traffic.