Model-based Approach to Multi-domain Monitoring Data Aggregation

Abstract

The essential propellant for any closed-loop management mechanism is data related to the managed entity. While this is a general evidence, it becomes even more true when dealing with advanced closed-loop systems like the ones supported by Artificial Intelligence (AI), as they require a trustworthy, up-to-date and steady flow of state data to be applicable. Modern network infrastructures provide a vast amount of disparate data sources, especially in the multi-domain scenarios considered by the ETSI Industry Specification Group (ISG) Zero Touch Network and Service Management (ZSM) framework, and proper mechanisms for data aggregation, pre-processing and normalization are required to make possible AI-enabled closed-loop management. So far, solutions proposed for these data aggregation tasks have been specific to concrete data sources and consumers, following ad-hoc approaches unsuitable to address the vast heterogeneity of data sources and potential data consumers. This paper presents a model-based approach to a data aggregator framework, relying on standardized data models and telemetry protocols, and integrated with an open-source network orchestration stack to support their incorporation within network service lifecycles.

Toward Building a Semantic Network Inventory for Model-Driven Telemetry

“…In this sense, MDT enables near real-time monitoring of a network infrastructure, for example, by dynamically detecting a shutdown of an interface, or by identifying network anomalies based on BGP information [2]. For these reasons, YANG data models, and consequently MDT, are considered key enablers for network monitoring automation [3].…”