Estimating latent processes on a network from indirect measurements

TL;DR

This paper develops a novel multilevel state-space model and inference strategies to estimate point-to-point network traffic from aggregate measurements, improving scalability and accuracy in complex network scenarios.

Contribution

It introduces a new multilevel state-space model and a model-based regularization approach for efficient inference of network traffic from indirect measurements.

Findings

The proposed inference method outperforms existing approaches.

Model-based regularization enhances scalability to larger networks.

The approach is effective in real corporate and academic networks.

Abstract

In a communication network, point-to-point traffic volumes over time are critical for designing protocols that route information efficiently and for maintaining security, whether at the scale of an internet service provider or within a corporation. While technically feasible, the direct measurement of point-to-point traffic imposes a heavy burden on network performance and is typically not implemented. Instead, indirect aggregate traffic volumes are routinely collected. We consider the problem of estimating point-to-point traffic volumes, x_t, from aggregate traffic volumes, y_t, given information about the network routing protocol encoded in a matrix A. This estimation task can be reformulated as finding the solutions to a sequence of ill-posed linear inverse problems, y_t = A x_t, since the number of origin-destination routes of interest is higher than the number of aggregate measurements available. Here, we introduce a novel multilevel state-space model of aggregate traffic volumes with realistic features. We implement a naive strategy for estimating unobserved point-to-point traffic volumes from indirect measurements of aggregate traffic, based on particle filtering. We then develop a more efficient two-stage inference strategy that relies on model-based regularization: a simple model is used to calibrate regularization parameters that lead to efficient and scalable inference in the multilevel state-space model. We apply our methods to corporate and academic networks, where we show that the proposed inference strategy outperforms existing approaches and scales to larger networks. We also design a simulation study to explore the factors that influence the performance. Our results suggest that model-based regularization may be an efficient strategy for inference in other complex multilevel models.