Faro: a scalable and reliable outage detection algorithm for IoT Mobile Virtual Network Aggregators

Internet of Things (IoT) Mobile Network Aggregators (MNAs) are an increasingly successful paradigm for providing ubiquitous mobile Internet connectivity for Smart Devices. They leverage the 4G/5G roaming architecture offered by Mobile Network Operators (MNOs) worldwide to offer a single gateway to the Internet. In this context, promptly detecting network outages is fundamental: Mobile Network Aggregators (MNAs) do not have control over the visited network infrastructure, and they offer connectivity to a huge number of possibly unmanaged devices. However, this activity is currently a painstaking process carried out manually by network engineers, who review trends in control plane signalling messages that are forwarded in the Mobile Network Aggregators (MNAs) managed core. In this paper, we present Faro, a self-supervised solution for the automatic detection of network outages. Faro leverages a contrastive learning framework that is particularly suited for this scenario, characterised by very skewed input data, few samples, and the use of confidence scores to automatically detect changes in the input data. We evaluate Faro using a real-world dataset comprising more than 5 million control plane messages across multiple countries and over 6 months, demonstrating its ability to accurately diagnose all outage scenarios in less than 15 minutes.