Abstract: Techniques are provided for dynamic alert suppression policy management. In one embodiment, the techniques involve receiving an event stream, wherein the event stream includes metric values comprising at least one of: log anomaly data and metric anomaly data, determining an anomalous event based on the event stream, determining a persistent region of the anomalous event, determining a quantum representation of the persistent region, determining X-Y values of the persistent region based on the quantum representation, and generating a policy based on a set of the X-Y values.

Abstract: Techniques are provided for dynamic alert suppression policy management. In one embodiment, the techniques involve receiving an event stream, wherein the event stream includes metric values comprising at least one of: log anomaly data and metric anomaly data, determining an anomalous event based on the event stream, determining a persistent region of the anomalous event, determining a quantum representation of the persistent region, determining X-Y values of the persistent region based on the quantum representation, and generating a policy based on a set of the X-Y values.

Abstract: Systems/techniques for generating training data via reinforcement learning fault-injection are provided. A system can access a computing application. In various aspects, the system can train one or more machine learning models based on responses of the computing application to iterative fault-injections determined via reinforcement learning. More specifically, the system can: inject a first fault into the computing application; record a resultant dataset outputted by the computing application in response to the first fault; train the one or more machine learning models on the resultant dataset and the first fault; compute a reinforcement learning reward based on performance metrics of the one or more machine learning models and based on a quantity of the resultant dataset; update, via execution of a reinforcement learning algorithm, the fault-injection policy based on the reinforcement learning reward; and inject a second fault into the computing application, based on the updated fault-injection policy.