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arXiv · 2109.07743

Optimal Probing with Statistical Guarantees for Network Monitoring at Scale

Muhammad Jehangir Amjad·Christophe Diot·Dimitris Konomis·Branislav Kveton·Augustin Soule·Xiaolong Yang
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Abstract

Cloud networks are difficult to monitor because they grow rapidly and the budgets for monitoring them are limited. We propose a framework for estimating network metrics, such as latency and packet loss, with guarantees on estimation errors for a fixed monitoring budget. Our proposed algorithms produce a distribution of probes across network paths, which we then monitor; and are based on A- and E-optimal experimental designs in statistics. Unfortunately, these designs are too computationally costly to use at production scale. We propose their scalable and near-optimal approximations based on the Frank-Wolfe algorithm. We validate our approaches in simulation on real network topologies, and also using a production probing system in a real cloud network. We show major gains in reducing the probing budget compared to both production and academic baselines, while maintaining low estimation errors, even with very low probing budgets.

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Muhammad Jehangir Amjad, Christophe Diot, Dimitris Konomis, Branislav Kveton, Augustin Soule, Xiaolong Yang. 2021-09-16. Optimal Probing with Statistical Guarantees for Network Monitoring at Scale. https://arxiv.org/abs/2109.07743

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