A set command is issued to transfer a diagnostic parameter record to a communication component of the computing environment. The diagnostic parameter record specifies a diagnostic action to be taken by the communication component to obtain diagnostic information and specifies a version of the diagnostic information to be obtained. Based, in part, on issuing the set command, the diagnostic information is obtained. The version of the diagnostic information obtained is the version specified, based on the version specified being supported by the communication component.

A data processing system implements adaptive telemetry sampling by obtaining first telemetry data from a plurality of telemetry data sources, analyzing the first telemetry data to identify a subset of telemetry data sources for which a reduced sampling rate may be implemented, determining a reduced sampling rate for each event type of the plurality of event types, selecting a subset of the event types for which the reduced sampling rate is to be applied, obtaining second telemetry data from the subset of telemetry data sources at the reduced sampling rate associated with each event type of the subset of event types, analyzing the second telemetry data to determine one or more estimated metric values for one or more metrics, and generating a report comprising the one or more estimated metric values and an estimated total cost saving based on an estimated cost saving associated with each event type.

A method includes maintaining an event logging database including entries corresponding to events processed at local servers. The method includes, in response to receiving a first message from a first local server, where the first message indicates that a first event occurred, identifying a causal parent event preceding the first event. The causal parent event is a most recent event that occurred at the first local server. The method includes identifying a temporal parent event preceding the first event. The temporal parent event is a most recent event that occurred across all of the plurality of local servers. The method includes generating a new entry based on data from the first message, an identifier of a first entry recording the causal parent event, and an identifier of a second entry recording the temporal parent event. The method includes adding the new entry to the event logging database.

A method for use in determining a status of a mobile electronic device, comprises repeatedly reading information from a debug log stored in a ringbuffer of the mobile electronic device, repeatedly using the information read from the debug log to construct and maintain an event history for the mobile electronic device, and repeatedly determining the status of the mobile electronic device based on the event history. The method may be used for determining a status of a mobile electronic device and, in particular though not exclusively, for use in determining a status of one or more hardware elements of a mobile phone, a smartphone, a tablet and/or a laptop. A system and a computer program for use in determining a status of a mobile electronic device are also disclosed.

An approach is provided in which the approach builds a combination model that includes a normal status model and an abnormal status model. The normal status model is built from a set of time-sequenced normal status records and the abnormal status model is built from a set of time-sequenced abnormal status records. The approach computes a set of time-sequenced coefficient combination values of the normal status model and the abnormal status model based on applying a set of fitting coefficient characteristics to the normal status model and the abnormal status model. The approach performs goal seek analysis on a system using the combination model and the set of time-sequenced coefficient combination values.

Systems, computer-implemented methods, and/or computer program products facilitating a process to identify and respond to a primary electronic message are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a determination component can determine that a primary electronic message has not received a response electronic message. An analysis component can generate a generated electronic message addressing the informational or emotional content of the primary electronic message. In one or more embodiments, an updating component can update the analytical model based on one or more feedbacks to the generated electronic message, where the analytical model can remain active while being updated. The one or more feedbacks can comprise a feedback from an entity-in-the-loop monitoring outputs of the analytical model including the generated electronic message.

Disclosed is a universal profiling device operable to simulate a performance monitoring unit for a heterogeneous system. The universal profiling device includes a main circuit and a storage circuit. The main circuit is configured to execute at least one of multiple steps including an active data collection step and a passive data collection step. The active data collection step registers a callback function for an event of a designated object according to predetermined setting or user setting, and actively calls the callback function to obtain information of the event. The passive data collection step registers the event of the designated object according to the predetermined setting or user setting and thereby receives the information of the event without requesting the designated object, wherein the information of the event is stored in the storage circuit.

An embodiment may involve, based on a pre-defined trigger associated with a particular application, reading error data from a resource that is used by the particular application, wherein persistent storage contains definitions of a plurality of error scenarios, a plurality of fix scripts, and associations between each of the plurality of error scenarios and one or more of the plurality of fix scripts; applying one or more rules to the error data, wherein the rules involve pattern matching or parsing; based on applying the one or more rules, determining a particular error scenario represented in the error data, wherein the particular error scenario is one of the plurality of error scenarios; determining, based on the associations, a particular fix script associated with the particular error scenario, wherein the particular fix script is one of the plurality of fix scripts; and causing execution of the particular fix script.

Methods, apparatus, and processor-readable storage media for device component management using deep learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from one or more enterprise devices; determining, for each of the one or more enterprise devices, values for multiple device attributes by processing the obtained telemetry data; generating, for each of the one or more enterprise devices, at least one prediction related to lifecycle information of at least one device component by processing the determined attribute values using one or more deep learning techniques; and performing one or more automated actions based at least in part on the at least one generated prediction.

A log monitoring system uses log monitoring rules to monitor log data generated by applications executing on a client computing device. By monitoring log data, the system detects that one or more triggering events have occurred on the client computing device. In response, the log monitoring system can perform one or more appropriate remedial actions. Additionally, in response to the detected event(s), the log monitoring system can extract a select subset of relevant data from the client and transmit the subset of data to a separate repository for storage and/or processing.