Abstract: Examples of methods for predicting processing workloads are described herein. In some examples, a method may include predicting a processing workload for a set of machine learning models. In some examples, the method may include loading a machine learning model of the set of machine learning models from non-volatile memory based on the predicted processing workload.

Abstract: For each of a number of workloads, time intervals within execution performance information that was collected during execution of the workload on a first hardware platform are correlated with corresponding time intervals within execution performance information that was collected during execution of the workload on a second hardware platform. For a workload, the time intervals within the execution performance information on the second hardware platform are correlated to the time intervals within the execution performance information the first hardware platform during which the same parts of the workload were executed. A machine learning model that outputs predicted performance on the second hardware platform relative to known performance on the first hardware platform is trained. The model is trained from the correlated time intervals within the execution performance information for each workload on the hardware platforms.

Abstract: A program is executed on a first computer system and the execution of the program is monitored. A plurality of operation records are created based upon the monitoring, where each operation record is associated with an operation carried out during execution of the program. A first value of a cumulative performance indicator associated with the execution of the program on the first computer system is determined. For each operation record, a value of a performance indicator associated with carrying out the operation on a second computer system is predicted. For an operation record, the value of the performance indicator is predicted based on a performance model associated with carrying out operations on the second computer system. A second value of the cumulative performance indicator is determined, which is associated with execution of the program on the second computer system and is based on the predicted values of the performance indicator.

Abstract: An example mobile system is disclosed. The system comprises an audio input device to continuously capture audio data associated with a user, a display unit, a processor, connected to the audio unit and the display unit, to detect stress data based on the audio data associated with the user over a period of time, measure a change in the stress data over the period of time, determine usage data on the mobile system by the user over the period of time, perform an analysis of the change in the stress data in view of the usage data, and propose an action to manage the change in the stress data based on the analysis.