Abstract: A computer-implemented method is provided for predicting cloud enablement from storage and data metrics harnessed from across stack. The computer-implemented method includes identifying a corpus of data to be classified, and configuring at least one access threshold and at least one sensitivity threshold. The computer-implemented method also includes classifying at least a portion the data within the corpus based on the at least one access threshold and the at least one sensitivity threshold. Finally, the computer-implemented method includes outputting a model, based on the classification, that identifies at least a portion of the data for migration for enabling a hybrid cloud environment.

Abstract: A method for performing efficient data sampling across a storage stack for training machine learning (ML) models. The method includes obtaining, by a processor, data. The processor clusters the data into clusters based on similarities of the obtained data across an entire storage stack including: storage infrastructure metrics, file metrics and application dependency taxonomy. The processor performs a random sampling process to sample representative data from each cluster. The sampled representative data are combined to generate training data for processing predictive analytics.

Abstract: A computer-implemented method is provided for predicting cloud enablement from storage and data metrics harnessed from across stack. The computer-implemented method includes identifying a corpus of data to be classified, and configuring at least one access threshold and at least one sensitivity threshold. The computer-implemented method also includes classifying at least a portion the data within the corpus based on the at least one access threshold and the at least one sensitivity threshold. Finally, the computer-implemented method includes outputting a model, based on the classification, that identifies at least a portion of the data for migration for enabling a hybrid cloud environment.