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: An orchestrator associated with a collaboration application client executed by a near end device dynamically adapts volume level and/or other audio quality parameters to deliver a consistent voice experience to a collaboration participant. The orchestrator is informed by multiple machine learning engines collecting and analyzing inputs from one or more existing sensor-based functions embedded in the near end device. The orchestrator determine an audio configuration of the device and audio preferences of the user. Identities of far end participants are determined and their profiles are mapped against the user volume preferences. The orchestrator functions as an informing agent to the audio signal processing engine of the device, managing speaker output volume and microphone gain settings, based upon the machine learning engines and the sensor-based functions. The sensor based functions may detect proximity, head pose, gaze point, eye position, facial identities, mood, and so forth.

Abstract: An information handling system may predict, for a first time period, a first resource usage of a first information handling system resource for an application. The information handling system may also predict, for the first time period, a second resource usage level of a second information handling system resource different from the first information handling system resource for the application. The information handling system may adjust one or more performance parameters for the information handling system based on the first resource usage level and the second resource usage level.

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: Adaptive CPU power limit tuning can be performed. A mode detector can employ a machine learning model to detect a mode of operation on a computing device. As the computing device operates in the various modes, a mode boundary tuner of the mode detector can evaluate performance measurements to determine whether the currently defined boundaries between the various modes are optimal for the particular computing device. When the mode boundary tuner determines that a more optimal boundary definition exists, it can dynamically change the boundary to thereby tune the mode selection process on the particular computing device. A power limit setter may also be employed to set the CPU's power limits based on the mode detected by the mode detector. As the computing device operates in a mode, a power limit tuner can evaluate performance measurements and adjust the power limits to thereby tune the mode-specific power limits to the workload that is currently being executed.

Abstract: An information handling system may use algorithms implemented in hardware or software for allocating system resources to prioritize applications based on user interaction with the information handling system. An information handling system may optimize the power consumption in an information handling system or electronic device by adjusting performance parameters that redirect available thermal and power headroom to applications that the user is interacting with. The information handling system may monitor user interaction with windows rendered by the information handling system, whether on an internal display or external display. For example, a refresh rate of a monitor displaying a rendered window with less interaction than other rendered windows may be reduced to reduce power consumption related to operation of that monitor. Additional thermal headroom of the information handling system may be used to improve responsiveness of rendered windows with which the user is interacting.

Abstract: Systems and methods are disclosed herein that allow an information handling system (IHS) to determine the best visual quality for a video game application. In one embodiment, the disclosed systems and methods may determine optimal in-game settings for a video game application by assigning numerical values to the in-game settings, running a benchmark test using a first set of in-game settings having a first set of numerical values, monitoring the FPS generated during the benchmark test, combining the first set of numerical values and the monitored FPS into a game variable score, and incrementally changing the in-game settings and rerunning the benchmark test to generate new game variable scores. Once the monitored FPS reaches a target FPS, the set of in-game settings with the highest game variable score may be selected as the optimal in-game settings for the video game application.

Abstract: An orchestrator associated with a collaboration application client executed by a near end device dynamically adapts volume level and/or other audio quality parameters to deliver a consistent voice experience to a collaboration participant. The orchestrator is informed by multiple machine learning engines collecting and analyzing inputs from one or more existing sensor-based functions embedded in the near end device. The orchestrator determine an audio configuration of the device and audio preferences of the user. Identities of far end participants are determined and their profiles are mapped against the user volume preferences. The orchestrator functions as an informing agent to the audio signal processing engine of the device, managing speaker output volume and microphone gain settings, based upon the machine learning engines and the sensor-based functions. The sensor based functions may detect proximity, head pose, gaze point, eye position, facial identities, mood, and so forth.

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 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: An information handling system may use algorithms implemented in hardware or software for allocating system resources to prioritize applications based on user interaction with the information handling system. An information handling system may optimize the power consumption in an information handling system or electronic device by adjusting performance parameters that redirect available thermal and power headroom to applications that the user is interacting with. The information handling system may monitor user interaction with windows rendered by the information handling system, whether on an internal display or external display. For example, a refresh rate of a monitor displaying a rendered window with less interaction than other rendered windows may be reduced to reduce power consumption related to operation of that monitor. Additional thermal headroom of the information handling system may be used to improve responsiveness of rendered windows with which the user is interacting.

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: User responsiveness on an information handling system may be improved by classifying an application based on its importance and/or relevance for an individual user with the goal of prioritizing resource allocation to improve responsiveness and performance of applications. The classification may include analyzing telemetry data to determine the most important applications for a user, such as by determining an application's importance and/or relevance to a particular user, and determine the resource utilization of that application from a macro perspective. After classification, changing characteristics of an application may be monitored and used to dynamically allocate system resources to the application during runtime. In this manner, priority on resource allocations for certain resources may be adapted to fit the user and the application, and adapt to the changing requirements and scenarios.

Abstract: In some examples, a computing device executing an application may gather data associated with a usage of multiple computing resources (e.g., CPU, GPU, storage, memory, and the like) of the computing device, generate one or more packets to carry the data, and set a priority of each of the one or more packets. The computing device may send the one or more packets to a server, to other computing devices, or both. The server or the other computing devices may send a new profile. The computing device may modify a hardware and software configuration of the computing device based at least in part on the new profile to create a modified configuration and execute the application, resulting in the application having at least one of a reduced latency or an increased throughput using the modified configuration.

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 for determining one or more resource performance features of a resource used by the IHS using a first machine learning (ML) service, and determining one or more application performance features of a target application executed by the resource using a second ML service. Using the determined resource performance features and the application performance features, the instructions may generate a profile recommendation for the target application, and adjust one or more settings of the resource to optimize a performance of the target application executed by the resource.

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.