Abstract: An information handling system includes a processor, a memory device, and a PMU to provide power to the processor and memory device. The information handling system further includes an emitter-assisted heatpipe including an enclosed hollow chamber to house a working fluid capable of being vaporized upon application of heat at an evaporator end of the heatpipe, an ion emitter placed at the evaporator end of the heatpipe where the working fluid is vaporized upon application of heat to create a vapor within the enclosed hollow chamber of the heatpipe, the ion emitter to create ions within the vapor, an ion collector placed at a condenser end of the emitter-assisted heatpipe where the vapor condenses into a liquid, and an ionic driving circuit operatively coupled to the ion emitter, the generated ions to be attracted to the ion collector creating an ionic airflow of the vapor within the emitter-assisted heatpipe.

Abstract: A data communication network includes client systems and an edge device. The client systems each provide telemetry data to the data communication network, tags a first portion of the telemetry data with first metadata, and tags a second portion of the telemetry data with second metadata. The first metadata indicates that the first portion is passthrough data, and the second metadata indicates that the second portion is federatable data. The edge device receives the federatable data from each of the client systems, federates the federatable data into federated data, and provides the federated data. The federated data is of a smaller quantity than the received federatable data. The backend system receives the passthrough data from the client systems, receives the federated data from the edge device, analyzes the passthrough data and the federated data, and configures the data communication network based upon the analysis.

Abstract: An information handling system includes a processor, a memory device, and a PMU to provide power to the processor and memory device. The information handling system further includes an emitter-assisted heatpipe including an enclosed hollow chamber to house a working fluid capable of being vaporized upon application of heat at an evaporator end of the heatpipe, an ion emitter placed at the evaporator end of the heatpipe where the working fluid is vaporized upon application of heat to create a vapor within the enclosed hollow chamber of the heatpipe, the ion emitter to create ions within the vapor, an ion collector placed at a condenser end of the emitter-assisted heatpipe where the vapor condenses into a liquid, and an ionic driving circuit operatively coupled to the ion emitter, the generated ions to be attracted to the ion collector creating an ionic airflow of the vapor within the emitter-assisted heatpipe.

Abstract: An information handling system includes a processor, a memory device, and a PMU to provide power to the processor and memory device. The information handling system further includes an emitter-assisted heatpipe including an enclosed hollow chamber to house a working fluid capable of being vaporized upon application of heat at an evaporator end of the heatpipe, an ion emitter placed at the evaporator end of the heatpipe where the working fluid is vaporized upon application of heat to create a vapor within the enclosed hollow chamber of the heatpipe, the ion emitter to create ions within the vapor, an ion collector placed at a condenser end of the emitter-assisted heatpipe where the vapor condenses into a liquid, and an ionic driving circuit operatively coupled to the ion emitter, the generated ions to be attracted to the ion collector creating an ionic airflow of the vapor within the emitter-assisted heatpipe.

Abstract: A system, method, and computer-readable medium are disclosed for configuring and offering upgrade capability to information handling systems. A deep learning or machine learning model (DL/ML) model is trained to optimize a particular configuration and use cases for an information handling system and provides various levels of upgrades for the use cases. Levels are identified as base or upgrade and mapped to a licensing layer that enables or disables use of performance levels based on weights that enable base output level classes and disable upgrade output level classes. An offer to upgrade is made as to upgrade levels upon a determination of probabilities of performance output level classes.

Abstract: An information handling system may include a processor, a battery configured to provide electrical energy to components of the information handling system, and a basic input/output system (BIOS) communicatively coupled to the processor and embodied by executable instructions embodied in non-transitory computer readable media, the instructions configured to, when executed by the processor, in response to one of execution of a BIOS managed operation or a request for execution of the BIOS managed operation: determine an energy requirement for execution of the BIOS managed operation; determine an energy capacity of the battery; determine if the energy capacity is sufficient to satisfy the energy requirement; and limit one or more features of the BIOS managed operation in response to the energy capacity being insufficient to satisfy the energy requirement.

Abstract: A system, method, and computer-readable medium are disclosed for configuring and offering upgrade capability to information handling systems. A deep learning or machine learning model (DL/ML) model is trained to optimize a particular configuration and use cases for an information handling system and provides various levels of upgrades for the use cases. Levels are identified as base or upgrade and mapped to a licensing layer that enables or disables use of performance levels based on weights that enable base output level classes and disable upgrade output level classes. An offer to upgrade is made as to upgrade levels upon a determination of probabilities of performance output level classes.

Abstract: A system, method, and computer-readable medium are disclosed for optimizing performance of an information handling system comprising: identifying a statistical model for use when optimizing performance of the information handling system; sampling the performance of the information handling system, the sampling being performed iteratively; and, adjusting the performance of the information handling system by applying optimized system configurations to the information handling system, the optimized parameters being based upon the statistical model.

Abstract: A system, method, and computer-readable medium are disclosed for performing a dynamic application optimization operation, comprising: instrumenting a plurality of system parameters of a client information handling system for monitoring; instructing a user to execute a particular application on the client information handling system; obtaining a plurality of samples of the plurality of system parameters; performing a machine learning operation using the plurality of samples of the plurality of system parameters, the machine learning operation training a machine learning model to generate a profile for the particular application and an operating mode of the particular application; applying the profile to the client information handling system to provide a new information handling system configuration, the new information handling system configuration optimizing the information handling system for the particular application.

Abstract: In some examples, a computing device may determine that a selected application is executing and gather, over a predetermined time interval, data associated with operations being performed to the input/output stack by the selected application. After gathering the data, a classifier may analyze the data and determine a particular workload type from a predefined set of workload types associated with the selected application. The computing device may select a particular profile from a plurality of predefined profiles based at least in part on the particular workload type, and modify, based on the particular profile, a plurality of parameters to create a plurality of modified parameters. The modified parameters may reduce an execution time of performing the operations to the input/output stack.

Abstract: In some examples, a computing device may determine that a selected application is executing and gather, over a predetermined time interval, data associated with operations being performed to the input/output stack by the selected application. After gathering the data, a classifier may analyze the data and determine a particular workload type from a predefined set of workload types associated with the selected application. The computing device may select a particular profile from a plurality of predefined profiles based at least in part on the particular workload type, and modify, based on the particular profile, a plurality of parameters to create a plurality of modified parameters. The modified parameters may reduce an execution time of performing the operations to the input/output stack.

Abstract: A system, method, and computer-readable medium are disclosed for performing a dynamic application optimization operation, comprising: instrumenting a plurality of system parameters of a client information handling system for monitoring; instructing a user to execute a particular application on the client information handling system; obtaining a plurality of samples of the plurality of system parameters; performing a machine learning operation using the plurality of samples of the plurality of system parameters, the machine learning operation training a machine learning model to generate a profile for the particular application and an operating mode of the particular application; applying the profile to the client information handling system to provide a new information handling system configuration, the new information handling system configuration optimizing the information handling system for the particular application.

Abstract: Embodiments provide the ability to configure software backup and restoration procedures on an IHS (Information Handling System) with minimal or no input from a user. Embodiments utilize local monitors on an IHS in order to generate metadata describing the use of software applications installed on the IHS. A remote analytics engine process the metadata received from multiple participating IHSs to generate inputs used by the restoration and backup procedures implemented by the IHS. The metadata generated by an IHS is used to determine valuations for the applications and files installed on the IHS. These valuations may then be utilized to identify files of significant value to the user, which may then be designated for backup by the backup procedures implemented by the IHS. The generated valuations may also be used to select the applications that should be reinstalled in order to restore the IHS to an approximated prior operating state.

Abstract: A system, method, and computer-readable medium are disclosed for identifying a simulated application load that represents a target application workload to use when executing a performance optimization operation based upon a particular workload; testing the information handling system being optimized by executing the simulated application load that represents the target application workload on the information handling system; profiling a workload of the information handling system based upon executing the simulated application load that represents the target application workload; applying performance optimization changes to the information handling system based upon the data analysis, the performance optimization changes being applied automatically and seamlessly; analyzing performance of the information handling system after the performance analysis changes are applied; and, presenting information regarding performance improvement as to allow a user to appreciate an effect of the performance optimization chan

Abstract: A system, method, and computer-readable medium are disclosed for optimizing performance of an information handling system comprising: identifying a statistical model for use when optimizing performance of the information handling system; sampling the performance of the information handling system, the sampling being performed iteratively; and, adjusting the performance of the information handling system by applying optimized system configurations to the information handling system, the optimized parameters being based upon the statistical model.

Abstract: Embodiments provide the ability to configure software backup and restoration procedures on an IHS (Information Handling System) with minimal or no input from a user. Embodiments utilize local monitors on an IHS in order to generate metadata describing the use of software applications installed on the IHS. A remote analytics engine process the metadata received from multiple participating IHSs to generate inputs used by the restoration and backup procedures implemented by the IHS. The metadata generated by an IHS is used to determine valuations for the applications and files installed on the IHS. These valuations may then be utilized to identify files of significant value to the user, which may then be designated for backup by the backup procedures implemented by the IHS. The generated valuations may also be used to select the applications that should be reinstalled in order to restore the IHS to an approximated prior operating state.

Abstract: Dissatisfaction variables track inputs made by an end user at an information handling system that are associated with end user dissatisfaction regarding the operation of the information handling system, such as corrective actions, so that an alert issues if a dissatisfaction variable changes by a predetermined amount over a predetermined time period. Dissatisfaction variables identify a pattern of a change in behavior by an end user rather than the operational status of system components. For example, an increase in end user forced shutdowns of the information handling system indicates end user dissatisfaction with the information handling system, such as repeated freezes of the operating system so that performing a timely diagnostics will improve end user satisfaction and proactively correct and impending system failure.

Abstract: Dissatisfaction variables track inputs made by an end user at an information handling system that are associated with end user dissatisfaction regarding the operation of the information handling system, such as corrective actions, so that an alert issues if a dissatisfaction variable changes by a predetermined amount over a predetermined time period. Dissatisfaction variables identify a pattern of a change in behavior by an end user rather than the operational status of system components. For example, an increase in end user forced shutdowns of the information handling system indicates end user dissatisfaction with the information handling system, such as repeated freezes of the operating system so that performing a timely diagnostics will improve end user satisfaction and proactively correct and impending system failure.