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    Mapping telemetry data to states for efficient resource allocation
    14.
    发明授权
    Mapping telemetry data to states for efficient resource allocation 有权

    公开(公告)号：US11983573B2

    公开(公告)日：2024-05-14

    申请号：US17376249

    申请日：2021-07-15

    Applicant: EMC IP Holding Company LLC

    Inventor： Eduardo Vera Sousa ,  Tiago Salviano Calmon

    IPC: G06F9/50 ,  G06F11/30 ,  G06F11/34 ,  G06F18/22 ,  G06N3/04

    CPC classification number: G06F9/505 ,  G06F9/5083 ,  G06F11/3006 ,  G06F11/3433 ,  G06F18/22 ,  G06N3/04

    Abstract: Techniques described herein relate to a method for resource allocation using fingerprint representations of telemetry data. The method may include receiving, at a resource allocation device, a request to execute a workload; obtaining, by the resource allocation device, telemetry data associated with the workload; identifying, by the resource allocation device, a breakpoint based on the telemetry data; identifying, by the resource allocation device, a workload segment using the breakpoint; generating, by the resource allocation device, a fingerprint representation using the workload segment; performing, by the resource allocation device, a search in a fingerprint catalog using the fingerprint representation to identify a similar fingerprint; obtaining, by the resource allocation device, a resource allocation policy associated with the similar fingerprint; and performing, by the resource allocation device, a resource policy application action based on the resource allocation policy.

    Contribution incrementality machine learning models
    15.
    发明授权
    Contribution incrementality machine learning models 有权

    公开(公告)号：US11983089B2

    公开(公告)日：2024-05-14

    申请号：US17278395

    申请日：2019-12-05

    Applicant: Google LLC

    Inventor： Xinlong Bao ,  Ali Nasiri Amini ,  Jing Wang ,  Mert Dikmen ,  Amy Richardson ,  Dinah Shender ,  Junji Takagi ,  Sen Li ,  Ruoyi Jiang ,  Yang Jiao ,  Yang Zhang ,  Zhuo Zhang

    IPC: G06Q30/02 ,  G06F11/34 ,  G06N20/00

    CPC classification number: G06F11/3433 ,  G06F11/3428 ,  G06N20/00

    Abstract: Methods, systems, and computer programs encoded on a computer storage medium, for training and using machine learning models are disclosed. Methods include creating a model that represents relationships between user attributes, content exposures, and performance levels for a target action using organic exposure data specifying one or more organic exposures experienced by a particular user over a specified time prior to performance of a target action by the particular user and third party exposure data specifying third party exposures of a specified type of digital component to the particular user over the specified time period. Using the model, an incremental performance level attributable to each of the third party exposures at an action time when the target action was performed by the particular user is determined. Transmission criteria for at least some digital components to which the particular user was exposed are modified based on the incremental performance.

    DATABASE SIMULATION MODELING FRAMEWORK
    19.
    发明公开
    DATABASE SIMULATION MODELING FRAMEWORK 审中-公开

    公开(公告)号：US20240103994A1

    公开(公告)日：2024-03-28

    申请号：US18530914

    申请日：2023-12-06

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Zi YE ,  Justin Grant MOELLER ,  Ya LIN ,  Willis LANG

    IPC: G06F11/34 ,  G06F11/30 ,  G06F16/21 ,  G06F16/27

    CPC classification number: G06F11/3433 ,  G06F11/3006 ,  G06F11/3457 ,  G06F16/217 ,  G06F16/27

    Abstract: Methods, systems, and computer program products are provided for creating a resource management testing environment. An initial population of databases is established in a database ring, having an in initial count of databases and different types of databases that are determined based on an initial database population model. The initial population model receives ring classification information for the database ring from a ring grouping model. A sequence of database population-change events is generated based on a model, to change the population of the databases over time in the ring. An orchestration framework performs testing of resource manager operations based on the model-defined initial population of databases and the model-defined populations of databases changed over time. Model-defined resource usage metrics for each database are utilized to test the resource manager operations. Resource usage metrics and database add/drop events of a production system are used to train the models.

    Distributed storage workload management
    20.
    发明授权
    Distributed storage workload management 有权

    公开(公告)号：US11941443B2

    公开(公告)日：2024-03-26

    申请号：US17238115

    申请日：2021-04-22

    Applicant: EMC IP Holding Company LLC

    Inventor： Garvin O'Brien

    IPC: G06F9/50 ,  G06F11/30 ,  G06F11/34

    CPC classification number: G06F9/5016 ,  G06F9/505 ,  G06F11/3034 ,  G06F11/3433

    Abstract: Workloads, e.g., synthetic workloads, on one or more storage systems in an dynamic, automated manner, for example, to load test the one or more storage systems. A distributed system may be employed in which a workload information server (WIS) serves one or more clients referred to herein as workload control components (WCCs) that analyze workload information of the one or more storage systems, and control the modification of workloads thereon based on this analysis, through the WIS. The WIS also may serve one or more clients referred to herein as workload generation controllers (WGCs) that monitor workloads on the one or more storage systems, report workload information to the WIS and generate, modify or remove workloads on the one or more storage systems according to instructions received from the WIS in response to requests (e.g., hints) from the one or more WGCs.

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