Electronic device and method for controlling data throughput based on heat generation in electronic device

    公开(公告)号:US11977931B2

    公开(公告)日:2024-05-07

    申请号:US16931969

    申请日:2020-07-17

    CPC classification number: G06F9/5094 G01K7/22 G01K13/00

    Abstract: An electronic device is provided. An electronic device includes at least one antenna module, a first communication circuit configured to provide first communication via the at least one antenna module, a plurality of temperature sensor, at least one processor operationally connected to the first communication circuit and the plurality of temperature sensors, and a memory. The memory may store instructions which, when executed, cause the at least one processor to obtain a first temperature associated with the electronic device via the plurality of temperature sensors, identify a second temperature associated with the first communication based on the first temperature being equal to or higher than a first threshold value, identify an operation state of at least one application, in which data throughput associated with the first communication is equal to or more than designated throughput, based on the second temperature being equal to or higher than a second threshold value, and adjust first data throughput of a first application, which is operating in a background state, among the at least one application.

    Risk-based scheduling of containerized application services

    公开(公告)号:US11934889B2

    公开(公告)日:2024-03-19

    申请号:US16929575

    申请日:2020-07-15

    Abstract: The present disclosure is a system for risk-based scheduling of a containerized application service. The system may include a scheduler extender which is configured to receive a list of nodes available to process an application or part of an application to be completed by one or more nodes of the list of nodes and receive information from one or more information technology (IT) sensors configured to measure an aspect of an IT operation associated with nodes of the list of nodes. The scheduler extender may be configured to filter the list of nodes based on the information from the one or more information technology (IT) sensors which measure an aspect of an IT operation associated with nodes of the list of nodes.

    Method for reducing execution jitter in multi-core processors within an information handling system

    公开(公告)号:USRE49781E1

    公开(公告)日:2024-01-02

    申请号:US16817238

    申请日:2020-03-12

    CPC classification number: G06F9/223 G06F9/5094 Y02D10/00

    Abstract: A method of reducing execution jitter includes a processor having several cores and control logic that receives core configuration parameters. Control logic determines if a first set of cores are selected to be disabled. If none of the cores is selected to be disabled, the control logic determines if a second set of cores is selected to be jitter controlled. If the second set of cores is selected to be jitter controlled, the second set of cores is set to a first operating state. If the first set of cores is selected to be disabled, the control logic determines a second operating state for a third set of enabled cores. The control logic determines if the third set of enabled cores is jitter controlled, and if the third set of enabled cores is jitter controlled, the control logic sets the third set of enabled cores to the second operating state.

    ORCHESTRATING DATACENTER WORKLOADS BASED ON ENERGY FORECASTS

    公开(公告)号:US20230401111A1

    公开(公告)日:2023-12-14

    申请号:US18249958

    申请日:2021-09-22

    CPC classification number: G06F9/5094 G06F9/505

    Abstract: Various embodiments of the present technology relate to systems and methods for orchestrating workflows to operate multi-site datacenters based on energy and cloud-computing supply and demand. In an embodiment, a method of operating a datacenter orchestration engine is provided. The method comprises, on a per-tenant basis, obtaining, via one or more machine learning models, at least a real-time utilization of energy grids and an energy supply of the energy grids available for use by datacenters over a period of time; obtaining, via the one or more machine learning models, a cloud-computing demand projected for the datacenters during the period of time; generating a cloud-computing optimization plan based at least on the real-time utilization of the energy grids, the available energy supply, and the cloud-computing demand; and providing the cloud-computing optimization plan to one or more tenants in a multi-tenant environment.

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