Utilizing digital twins for data-driven risk identification and root cause analysis of a distributed and heterogeneous system

Abstract

A device may receive system data identifying computational components and software components of a distributed and heterogeneous system executing a hybrid cloud application, and may create digital twins for the computational components and the software components. The device may create a central digital twin to receive functional data, operational data, and key performance indicators (KPIs) from the digital twins, and may create complex KPIs based on the functional data, the operational data, and the KPIs. The device may modify, based on the complex KPIs, one or more of the digital twins to generate additional KPIs, and process the additional KPIs, with a principal component analysis model and a self-organizing maps model, to detect anomalies in the distributed and heterogeneous system. The device may generate, based on the anomalies, a KPI cause vector identifying a root cause associated with the anomalies, and may perform actions based on the root cause.