LAUSR

Network softwarization technologies challenge legacy fault management systems. Coordination and dependency among different novel software components for orchestration, switching, virtual machine and container management creates novel monitoring points, besides novel sources of faults, bugs and vulnerabilities. To cope with the high heterogeneity and granularity of software components, we propose a modular network AI framework to detect anomalies, toward closed-loop automation. We design an AI-empowered anomaly detection framework able to assess the running state and the state deviations of a softwarized infrastructure, monitored through different features grouped depending on their layer and connect-compute stack component. Our framework learns the nominal working conditions of the infrastructure, with respect to which anomalies are detected, and characterized tracing back the layer and component root cause; it includes a network state assessment technique that leverages anomalies characterization through their most visible symptoms. We implement and validate the proposed framework through experimental tests on a containerized IP Multimedia Subsystem platform.