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.

Abstract: A first device may provide, via a global digital twin of the first device, a communication mode assignment, of a communication mode, to a local digital twin of a second device. The communication mode assignment is to cause the local digital twin to communicate with the global digital twin via the communication mode. The first device may generate, via the global digital twin, a task assignment, and may provide, via the global digital, the task assignment to the local digital twin. The first device may update, via the global digital twin, a model based on the task assignment, and may receive, via the global digital twin and from the local digital twin, a model update associated with the local digital twin. The first device may update, via the global digital twin, the model based on the model update.

Abstract: A device may generate a knowledge model based on a knowledge model schema, data residency constraints, and a data classification ontology associated with a cloud application, and may perform a dynamic flow analysis of the cloud application data and the data source identifiers to generate a data flow graph. The device may process the data flow graph, with the knowledge model, to determine sensitive attributes in the data flow graph, and may identify sensitive data sources that include the sensitive attributes and sensitive assets based on the data flow graph and the sensitive data sources. The device may process the sensitive data sources and the sensitive assets, with a machine learning model, to determine methods for identifying misconfigurations, and may utilize the methods to identify misconfigurations and severities of the misconfigurations. The device may generate remediation actions for correcting the cloud application based on the severities of the misconfigurations.

Abstract: In some examples, policy-based application architecture generation may include generating, based on a knowledge model schema, data residency policies, and a data classification ontology, a knowledge model, and determining, based on the knowledge model, whether an application includes regulated data. Based on an analysis of application data and user data, user location and regulated data insights may be generated to determine location specific data residency policies. Location specific regulated data in-flow and data source hosted location insights may be analyzed to determine a location compliance assessment that includes an indication of whether a location associated with the application is compliant or not compliant with the location specific data residency policies.