Abstract: Provided are techniques for model generation for determining optimal components of a storage subsystem based on measurements and models. Data for a storage subsystem having a plurality of components is collected, where the data comprises data for application resources, data for workload measurements, metadata, tags, and policies, and wherein the data comprises volume specific characteristics and storage functions. A plurality of models are generated based on the collected data, where each of the models includes a subset of the plurality of components of the storage subsystem. Characteristics for a new storage subsystem are received. The characteristics are matched to a model of the plurality of models. A recommendation of components of the matching model is created to create the new storage subsystem.

Abstract: Optimizing non-functional requirements across components of a high availability cluster may include receiving metadata from each of a plurality of components of a high availability network cluster. The metadata is indicative of a value, measured by each of the plurality of components, of one or more non-functional requirements of a service level agreement (SLA) associated with an application. A predicted non-functional requirement is calculated using the metadata based on a model. The model is trained to determine the predicted non-functional requirement based on relationships between the metadata of each of the plurality of components. A potential violation of the SLA is determined based on a comparison of the predicted non-functional requirement and the metadata.