Abstract: An example of a non-transitory computer-readable medium storing machine-readable instructions. The instructions may cause the processor to receive a configuration file that identifies data sources coupled to the processor. The instructions may cause the processor to collect a data set from the data source based on data from the configuration file. The instructions may cause the processor to create a statistical analysis based on the data set and transmit the statistical analysis to a server.

Abstract: An example of a non-transitory computer-readable medium storing machine-readable instructions. The instructions may cause the processor to receive a configuration file that identifies data sources coupled to the processor. The instructions may cause the processor to collect a data set from the data source based on data from the configuration file. The instructions may cause the processor to create a statistical analysis based on the data set and transmit the statistical analysis to a server.

Abstract: In an example implementation according to aspects of the present disclosure, a computing device and computer readable media to receive a temperature, operating speed, maximum operating speed, and utilization rate of a processor. A processor performance metric may be calculated based on the operating speed, maximum operating speed, and utilization rate. The processor performance metric and temperature may be used to retrieve a value in a thermal mapping. The value in the thermal mapping may be compared against a threshold to determine if a processor thermal metric event has occurred. An indication to the user may be presented in the event of a thermal metric event.

Abstract: Examples described herein include examples of method for predicting battery performance of a battery comprising collecting battery data corresponding to a plurality of batteries with characteristics similar to the battery during a first time period, storing the collected battery data in a staging memory, generating a logarithmic regression based on the collected battery data, and predicting battery performance for the battery based on the logarithmic regression.

Abstract: An example of a non-transitory computer-readable medium storing machine-readable instructions. The instructions may cause the processor to receive a configuration file that identifies data sources coupled to the processor. The instructions may cause the processor to collect a data set from the data source based on data from the configuration file. The instructions may cause the processor to create a statistical analysis based on the data set and transmit the statistical analysis to a server.

Abstract: An example method of collecting an n-component subset of resource usage data descriptive of usage of resources of a device or a system includes normalizing a set of resource usage data. Normalizing the set of resource usage data includes comparing usage of a first resource by a first component with usage of the first resource by other components.

Abstract: In an example, a method includes monitoring, by at least one processor, a computing system. An alert related to the computing system may be generated based on a threshold value. An analysts handling of the alert may be monitored and, based on the analysts handling of the alert, the threshold value for generating alerts may be adjusted.

Abstract: A technique includes, for each storage drive of a plurality of storage drives, determining a metric associated with the storage drive based at least in part on a plurality of attributes associated with the storage drive; and determining a health of a given storage drive of the plurality of storage drives based at least in part on the metric associated with the given storage drive and the metric associated with at least one other storage drive of the plurality of storage drives.

Abstract: In some examples, a computing device can determine a data state from telemetry data received from a plurality of client devices, determine a state change using the data state determined from the telemetry data, determine, using the state change, a potential cause of a fault of a component of a client device of the plurality of client devices, where the potential cause of the fault is determined in response to receiving a support inquiry from the client device, and display a timeline of state changes, where the timeline includes the determined state change of the client device.

Abstract: A technique includes, for each storage drive of a plurality of storage drives, determining a metric associated with the storage drive based at least in part on a plurality of attributes associated with the storage drive; and determining a health of a given storage drive of the plurality of storage drives based at least in part on the metric associated with the given storage drive and the metric associated with at least one other storage drive of the plurality of storage drives.

Abstract: In some examples, a computing device can determine a data state from telemetry data received from a plurality of client devices, determine a state change using the data state determined from the telemetry data, determine, using the state change, a potential cause of a fault of a component of a client device of the plurality of client devices, where the potential cause of the fault is determined in response to receiving a support inquiry from the client device, and display a timeline of state changes, where the timeline includes the determined state change of the client device.

Abstract: Examples described herein include examples of method for predicting battery performance of a battery comprising collecting battery data corresponding to a plurality of batteries with characteristics similar to the battery during a first time period, storing the collected battery data in a staging memory, generating a logarithmic regression based on the collected battery data, and predicting battery performance for the battery based on the logarithmic regression.