Abstract: An information handling system includes a processor, a memory device, and a power management unit (PMU) to provide power to the processor and memory device. The information handling system may also include an ion emitter fan cooling system that includes an ion emitter fan housed within an ion emitter fan housing that includes a plurality of emitter fan blades that rotate with tips operating as ion emitters. The ion emitter fan cooling system may also include an ionic driving circuit operatively coupled to the ion emitter fan and the plurality of emitter fan blades to provide a free electron source at tips of the plurality of emitter fan blades to ionize gases as an emitter within the ion emitter fan housing and create charged ions to create an airflow out of an external opening of the ion emitter fan housing having a collector.

Abstract: An information handling system includes a hardware processor, a memory device, and a PMU to provide power to the processor and memory device. The information handling system includes an ion emitter/collector cooling system including an ion emitter and an ion collector, the ion emitter to generate ions from the atmosphere and the ion collector to collect and deionize those ions, the creation of ions by the ion emitter creating an airflow through and out of the ion collector and at least one sensor to detect and measure at least one non-static ozone predictor measurement describing ozone production at the ion emitter/collector cooling system. Further, the processor executing code instructions of an intelligent ozone estimation and mitigation system including a trained ozone estimation machine learning model to use, as input, the non-static ozone predictor measurement and provide, as output, an ozone estimation value estimating the amount of ozone produced at the ion emitter.

Abstract: An information handling system includes a hardware processor, a memory device, and a PMU to provide power to the processor and memory device. The information handling system includes an ion emitter/collector cooling system including an ion emitter and an ion collector, the ion emitter to generate ions from the atmosphere and the ion collector to collect and deionize those ions, the creation of ions by the ion emitter creating an airflow through and out of the ion collector and at least one sensor to detect and measure at least one non-static ozone predictor measurement describing ozone production at the ion emitter/collector cooling system. Further, the processor executing code instructions of an intelligent ozone estimation and mitigation system including a trained ozone estimation machine learning model to use, as input, the non-static ozone predictor measurement and provide, as output, an ozone estimation value estimating the amount of ozone produced at the ion emitter.

Abstract: An information handling system includes a powered hardware processor and a memory device. The information handling system includes an ion emitter/collector blower cooling system including an ion emitter/collector blower having an ion emitter and an ion collector, the ion emitter/collector blower being operatively coupled to a fan and located between the fan and an exhaust vent formed in a top edge of the chassis of the information handling system. An ionic driving circuit is operatively coupled to the ion emitter via a high voltage to ionize gases at the ion emitter/collector blower to create charged ions that generate an airflow to and through the ion collector. The information handling system also includes an air intake vent located at the fan in the chassis so that the airflow is brought into the chassis via the fan, through the fan and ion emitter/collector blower, and out of the air exhaust vent.

Abstract: An information handling system comprising a processor, memory device and a power source including a fan and an ion emitter/collector blower cooling system including an ion emitter and an ion collector placed in fluidic series with the fan to generate an airflow along a voltage field from the ion emitter to the ion collector and with the fan. An embedded controller to execute code instructions of an ion emitter/collector blower and fan control system to control the activation of the ion emitter/collector blower and the fan by activating the ion emitter/collector blower at a varying levels depending on a type of power source that the information handling system is coupled to, meeting a temperature threshold, and a detected operating mode of the information handling system to prioritize the ion emitter/collector blower over the fan to limit noise and power.

Abstract: An information handling system includes a hardware processor, a memory device, and a PMU to provide power to the processor and memory device. The information handling system includes an ion emitter/collector cooling system including an ion emitter and an ion collector, the ion emitter to generate ions from the atmosphere and the ion collector to collect and deionize those ions, the creation of ions by the ion emitter creating an airflow through and out of the ion collector and at least one sensor to detect and measure at least one non-static ozone predictor measurement describing ozone production at the ion emitter/collector cooling system. Further, the processor executing code instructions of an intelligent ozone estimation and mitigation system including a trained ozone estimation machine learning model to use, as input, the non-static ozone predictor measurement and provide, as output, an ozone estimation value estimating the amount of ozone produced at the ion emitter.

Abstract: An information handling system includes a processor, a memory device, and a power management unit (PMU) to provide power to the processor and memory device. The information handling system may also include an ion emitter fan cooling system that includes an ion emitter fan housed within an ion emitter fan housing that includes a plurality of emitter fan blades that rotate with tips operating as ion emitters. The ion emitter fan cooling system may also include an ionic driving circuit operatively coupled to the ion emitter fan and the plurality of emitter fan blades to provide a free electron source at tips of the plurality of emitter fan blades to ionize gases as an emitter within the ion emitter fan housing and create charged ions to create an airflow out of an external opening of the ion emitter fan housing having a collector.

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 may also include an enclosed ion emitter cooling system including an ion emitter hub and an ion collector ring surrounding the ion emitter hub, the processor executing code instructions of an ion emitter control system to activate an ionic driving circuit, and an ionic driving circuit operatively coupled to the ion emitter hub and applying a first voltage to produce and repel ionized gases at the ion emitter hub and the ion collector ring and applying a second voltage to attract and deionize those ionized gases wherein movement of the ionized gases from the ion emitter hub to the ion collector ring causes an airflow within the enclosed ion emitter cooling system.

Abstract: This disclosure provides systems, methods, and devices for controlling a processor of an information handling system to improve performance specifically of a foreground application executing on the processor. In a first aspect, a method includes receiving information regarding an application context of a foreground application executing on the information handling system; determining a power modulation for a component of the information handling system based on the application context of the foreground application; and controlling the component of the information handling system based on the power modulation. Other aspects and features are also claimed and described.

Abstract: Embodiments of systems and methods for remote assisted optimization of applications executed by an Information Handling System (IHS) are described. In an illustrative, non-limiting embodiment, an IHS may include computer-executable instructions for determining one or more application performance features of a target application using an application machine learning (ML) engine, and generating one or more application profile recommendations for the target application according to the determined application performance features. Using the profile recommendations, the instructions adjust one or more settings of the IHS to optimize a performance of the target application, and transmit the application profile recommendations to a server that is configured to provide a service for the target application. The server then uses the one or more application profile recommendations to provision the service for use by the target application.

Abstract: Embodiments of systems and methods for managing performance optimization of a target application executed by an Information Handling System (IHS) are described. In an illustrative, non-limiting embodiment, an IHS may includes executable code to measure a performance of a target application at each of multiple configuration clusters that are applied to an IHS in which each configuration cluster includes multiple configuration settings of one or more resources that are used to execute the target application on the IHS. Using the measured performance values, the instructions may then select one of the configuration clusters that causes the target application to operate at an optimum performance level, and modify the IHS to operate with the one selected configuration cluster.

Abstract: Embodiments of systems and methods for managing performance optimization of applications executed by an Information Handling System (IHS) are described. In an illustrative, non-limiting embodiment, an IHS may include computer-executable instructions to, for each of multiple resources used to execute a target application, receive one or more machine learning (ML)-based hints associated with each resource that have been generated by a ML-based optimization service, and generate one or more augmented hints for at least one of the resources using a ML-to-ML orchestration service. The ML-to-ML orchestration service then transmits the augmented hints to the ML-based optimization service that combines the augmented ML-based hints with the one or more internally generated hints to generate augmented profile recommendations that are, in turn, used to adjust one or more settings of the resource to optimize a performance of the target application executed by the resource.

Abstract: Embodiments of systems and methods for a client driven cloud network access system and method are described. In an illustrative, non-limiting embodiment, an IHS may include computer-executable instructions for receiving a request to perform an operation provided by a cloud service from an application, and generating an aggregated data packet including the request, a requested performance level to be provided by the service, and application telemetry data associated with a measured performance level of the application. The aggregated data packet is then transmitted to a plurality of servers that are each configured to provide the cloud service. Each of the servers generates and sends a response to the aggregated data packet that includes an advertised performance level at which the server can provide the cloud service. The IHS may then select one of the servers according to the advertised performance level, and communicate with the selected server to provide the requested operation.

Abstract: Embodiments of systems and methods for a client driven cloud network access system and method are described. In an illustrative, non-limiting embodiment, an IHS may include computer-executable instructions for receiving a request to perform an operation provided by a cloud service from an application, and generating an aggregated data packet including the request, a requested performance level to be provided by the service, and application telemetry data associated with a measured performance level of the application. The aggregated data packet is then transmitted to a plurality of servers that are each configured to provide the cloud service. Each of the servers generates and sends a response to the aggregated data packet that includes an advertised performance level at which the server can provide the cloud service. The IHS may then select one of the servers according to the advertised performance level, and communicate with the selected server to provide the requested operation.

Abstract: Embodiments of systems and methods for managing performance optimization of applications executed by an Information Handling System (IHS) are described. In an illustrative, non-limiting embodiment, an IHS may include computer-executable instructions to, for each of multiple resources used to execute a target application, receive one or more machine learning (ML)-based hints associated with each resource that have been generated by a ML-based optimization service, and generate one or more augmented hints for at least one of the resources using a ML-to-ML orchestration service. The ML-to-ML orchestration service then transmits the augmented hints to the ML-based optimization service that combines the augmented ML-based hints with the one or more internally generated hints to generate augmented profile recommendations that are, in turn, used to adjust one or more settings of the resource to optimize a performance of the target application executed by the resource.

Abstract: Embodiments of systems and methods for remote assisted optimization of applications executed by an Information Handling System (IHS) are described. In an illustrative, non-limiting embodiment, an IHS may include computer-executable instructions for determining one or more application performance features of a target application using an application machine learning (ML) engine, and generating one or more application profile recommendations for the target application according to the determined application performance features. Using the profile recommendations, the instructions adjust one or more settings of the IHS to optimize a performance of the target application, and transmit the application profile recommendations to a server that is configured to provide a service for the target application. The server then uses the one or more application profile recommendations to provision the service for use by the target application.

Abstract: Embodiments of systems and methods for managing performance optimization of a target application executed by an Information Handling System (IHS) are described. In an illustrative, non-limiting embodiment, an IHS may includes executable code to measure a performance of a target application at each of multiple configuration clusters that are applied to an IHS in which each configuration cluster includes multiple configuration settings of one or more resources that are used to execute the target application on the IHS. Using the measured performance values, the instructions may then select one of the configuration clusters that causes the target application to operate at an optimum performance level, and modify the IHS to operate with the one selected configuration cluster.

Abstract: System performance of a first information handling system may be adjusted based on system usage. Performance parameters may be determined by a second information handling system based on the system usage and may be used, by the first information handling system, to adjust system performance. Configuration of the first information handling system may thus be distributed to two or more tiers. The second information handling system can be more efficient with determining operating parameters for the first information handling system when the second system is not power limited, as when the first information handling system is a mobile device.

Abstract: Airflow cooling is provided in an IHS (Information Handling System), where the airflow cooling may be adapted to reduce noise generated by the airflow cooling system and to provide cooling that is particularized to specific users. The operation of a software application by a user is detected. A workload profile associated with that application is determined and used to provide airflow cooling. The workload profile specifies parameters for providing cooling during use of applications associated with a first type of resource utilization. Upon detecting use of a different application, a different workload profile associated with applications of a second type of resource utilization may be determined and used to provide cooling. The user may provide noise feedback inputs specifying noise level preferences that are used in adapting the cooling provided by workload profiles. The workload profiles may be further adapted to reduce noise generated by the cooling system while still providing adequate cooling.

Abstract: Airflow cooling is provided in an IHS (Information Handling System), where the airflow cooling may be adapted to reduce noise generated by the airflow cooling system and to provide cooling that is particularized to specific users. The operation of a software application by a user is detected. A workload profile associated with that application is determined and used to provide airflow cooling. The workload profile specifies parameters for providing cooling during use of applications associated with a first type of resource utilization. Upon detecting use of a different application, a different workload profile associated with applications of a second type of resource utilization may be determined and used to provide cooling. The user may provide noise feedback inputs specifying noise level preferences that are used in adapting the cooling provided by workload profiles. The workload profiles may be further adapted to reduce noise generated by the cooling system while still providing adequate cooling.