Abstract: A method, system and computer program product for generating sets of training programs for machine learning models. Fixed values of one or more workload metrics are received from a user, where the workload metrics correspond to low-level program features which define particular low-level application behavior. A profile using the fixed values of the workload metrics is then created. A suite of synthetic applications is generated using the created profile to form a set of training programs which target particular aspects of program behavior. A machine learning model is then trained using the set of training programs. Since the generated synthetic applications provide a broader coverage of the program state-space, the formed set of training programs more accurately targets performance behavior thereby improving the prediction accuracy of the machine learning based predictive models.

Abstract: A method, system and computer program product for generating sets of training programs for machine learning models. Fixed values of one or more workload metrics are received from a user, where the workload metrics correspond to low-level program features which define particular low-level application behavior. A profile using the fixed values of the workload metrics is then created. A suite of synthetic applications is generated using the created profile to form a set of training programs which target particular aspects of program behavior. A machine learning model is then trained using the set of training programs. Since the generated synthetic applications provide a broader coverage of the program state-space, the formed set of training programs more accurately targets performance behavior thereby improving the prediction accuracy of the machine learning based predictive models.