Abstract: In one example, a computing node includes a metric dependency graph knowledge base to store a data structure representing a relationship between a plurality of metrics. Further, the computing node may include a processor and a memory having a metric recommendation unit. The metric recommendation unit may determine a first metric of a monitored computing-instance while a user interacts with a GUI of a monitoring application. Further, the metric recommendation unit may retrieve the data structure corresponding to the first metric. The data structure may include the first metric and a plurality of dependent metrics associated with the first metric. Further, the metric recommendation unit may apply a machine learning model on the data structure to determine a second metric from the plurality of dependent metrics. Furthermore, the metric recommendation unit may output the second metric related to the first metric on the GUI.

Abstract: Computer-implemented methods, media, and systems for automatic discovery of application resources for application backup in a container orchestration platform (e.g., a Kubernetes system) are disclosed. In an example method, a pod of an application deployed in a container orchestration platform is identified. Then an owner object of the pod is determined. Resources mounted on the pod and on the owner object of the pod in the container orchestration platform are checked. Based on the pod, the owner object of the pod, and the resources mounted on the pod and on the owner object of the pod, a resource hierarchy of the application is constructed. A backup specification for backup of the application is identified. Based on the backup specification and the resource hierarchy of the application, resources of the application are backed up.

Abstract: In one example, a computing node includes a metric dependency graph knowledge base to store a data structure representing a relationship between a plurality of metrics. Further, the computing node may include a processor and a memory having a metric recommendation unit. The metric recommendation unit may determine a first metric of a monitored computing-instance while a user interacts with a GUI of a monitoring application. Further, the metric recommendation unit may retrieve the data structure corresponding to the first metric. The data structure may include the first metric and a plurality of dependent metrics associated with the first metric. Further, the metric recommendation unit may apply a machine learning model on the data structure to determine a second metric from the plurality of dependent metrics. Furthermore, the metric recommendation unit may output the second metric related to the first metric on the GUI.