Dell Products Inc.

Emc IP Holding Company LLC

Farzad Khosrowpour

Mitchell Anthony Markow

Kevin Thomas Marks

Nikhil Manohar Vichare

Seth Feder

Marco Antonio Peereboom

Dharmesh M Patel

Liam B Quinn

Gaurav Chawla

Rohit K Chawla

Senthil Kumaran Baladhandayutham

Travis Christian North

Amihai Savir

Jacob Cherian

John S Loffink

Stephen Gouze Luning

Douglas Jarrett Peeler

Lee B Zaretsky

Anat Parush Tzur

Suraj M Varma

Sinem K Gulbay

Mitch Anthony Markow

Ahmad A J Ali

Jong Seo Lee

Fnu Jasleen

Wen-hao Zeng

Kai Yam Wu

Steven Hunt

William B Canaday

Justin A Gray

Steven A Sexton

Jasleen Singh

An information handling system detects transmission of input/output data, and enables a data direct input/output capability associated with a PCIe port based on the PCIe performance policy. The system performs a direct memory access transfer of the input/output data to a cache based on the PCIe performance policy, wherein the input/output data is transferred through the PCIe port with the data direct input/output capability.

Dynamic peripheral component interconnect-express performance management

An edge network includes an information handling system configured to manage a shared memory, and a client device. A series of edge systems each include a portion of the shared memory. The information handling system determines that the client device is bottlenecked in processing a service, locks data associated with the service in the shared memory of a first edge system, permits a second edge system to access the first data, and directs the second edge system to process the service.

Latency aware dynamic memory map

Systems and methods are provided that may be implemented on an information handling system to improve the performance of currently-executing target application/s by dynamically adjusting or changing the graphics frame rate (frames per second 'FPS') of other concurrently-executing application/s that are utilizing graphics resources. The disclosed systems and methods may be implemented to use resource sharing (e.g., central processing unit (CPU)/graphics processing unit (GPU) power sharing) in the system to provide more system resources to the system and the target application/s. The disclosed systems and methods may be so implemented to understand which application/s are currently running in the foreground or are predicted to be used and interacted with by the user, and to understand which remaining currently-executing background application/s are graphics intensive.

Systems and methods for adjusting frame rate of graphics intensive applications based on priority to improve information handling system performance

Embodiments may include a system and method for configuring operation of an information handling system based on a plurality of variants. Embodiments may determine the statistical operations for the variants, define priorities (for example less noise vs. better performance) and use iterative approximation of each variant in relation to all variants to select a profile and then configure operation of the information handling system based on the selected profile.

System and method for dynamically configuring an information handling system based on a profile

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. The determination of application importance and/or relevance and subsequent adaptation of system resource allocation may be performed using a model-based algorithm.

Model-based resource allocation for an information handling system

An information handling system builds a performance model for a network based on key performance indicators, and evaluates performance of applications using the key performance indicators according to useability metrics. The system predicts network bandwidth requirement based on the performance model for the network and the performance of the applications, adjusts power consumption of the network based on the predicted network bandwidth requirement, and reallocates power saved from the adjusting of the power consumption of the network to a component of the information handling system.

Dynamic control of network speed at a solution level

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.

Data tagging and federation in client-to-edge networks

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.

Power control for improving foreground application performance in an information handling system

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.

System and method for remote assisted optimization of native services

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.

Configuration cluster-based performance optimization of applications in an information handling system (IHS)

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.

Adjusting audio volume and quality of near end and far end talkers

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.

ML-to-ML orchestration system and method for system wide information handling system (IHS) optimization

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.

Client driven cloud network access system and method

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.

Adaptive CPU power limit tuning

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.

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Pflugerville, TX, United States of America

Farzad Khosrowpour