OpenNOP: an open-source network observability platform enabling multi-vendor multi-layer monitoring and ML analysis

Abstract

Network operators rely on the fault, configuration, accounting, performance, and security (FCAPS) model for efficient network management using traditional monitoring solutions that are often costly and proprietary. This paper introduces OpenNOP, an open-source, multi-layer, and multi-vendor network observability platform designed for fault detection, configuration tracking, and performance monitoring. OpenNOP collects and processes network metrics in a time-series database, enabling real-time visualization and AI-driven predictive analytics. Deployed in a multi-vendor optical transport testbed, it facilitates ML-based inference of network disturbances. OpenNOP uses scripted automation to control the generation of network disturbances and the collection of L1/L2/L3 metrics and then train and test ML models to infer the noise profile based on those metrics. By providing a scalable and extensible alternative to proprietary tools, OpenNOP advances network monitoring, predictive maintenance, and AI explainability.