Abstract: A controller obtains data stored in one or more data structures generated based on a defined task to be performed for a selected event. The data includes a set of constraints for the defined task. One or more task solutions generated for the defined task using the provided data are obtained. A determination is made as to whether the one or more task solutions include a task solution that satisfies one or more defined criteria. Based on determining that the one or more task solutions do not include the task solution that satisfies the one or more defined criteria, the set of constraints is automatically adjusted to provide an adjusted set of constraints. The adjusted set of constraints is to be automatically provided to a solution generator to be used to obtain the task solution that satisfies the one or more defined criteria.

Abstract: A method is presented to facilitate the training of a very large number of machine-learning performance models used to detect anomalies in computing operations. The models are grouped together according to model type, and are allocated to different pods of a computing environment that is used to carry out the operations being monitored. Initial training of models in a group is carried out while monitoring resource usage, and a particular pod is selected for further training based on the resource usage. The pod selected for training preferably has a minimum change in resource usage before and after the initial training. A different pod can be selected for scoring the trained models. The pod selected for scoring preferably has a maximum resource usage during an initial scoring among all pods.