EDGE COMPUTING WORKLOAD BALANCING

    公开(公告)号:US20220164242A1

    公开(公告)日:2022-05-26

    申请号:US17102581

    申请日:2020-11-24

    Abstract: A set of workload criteria is determined from a workload associated with a plurality of sources. The workload is divided among a set of workload groups according to the set of workload criteria and a first workload scheduler. A set of edge computing resources is assigned to each workload group within the set according to the set of workload criteria and the set of workload groups. A portion of the workload associated with a subset of the plurality of sources is handled by a first subset of edge computing resources and a second workload scheduler, where the subset of sources is associated with a first workload group. The handling includes balancing, by the second workload scheduler, the portion of the workload among the subset of sources. The handled workload is reported to a control center.

    Caching a Block of Data in a Multi-Tenant Cache Storage Device Based on Space Usage Boundary Estimates

    公开(公告)号:US20180300242A1

    公开(公告)日:2018-10-18

    申请号:US15485347

    申请日:2017-04-12

    Abstract: Mechanisms are provided for managing caching of data in a multi-tenant cache storage device utilized by a distributed dataset based application. The mechanisms retrieve Resilient Distributed Dataset (RDD) block size information for an RDD associated with an application. A probability distribution of RDD block sizes is generated based on the RDD block size information and a maximum size for a RDD block of data is estimated based on the probability distribution. An amount of free space in a portion of the multi-tenant cache storage device allocated to the application is estimated based on the estimated maximum size for the RDD block of data. Cache operations for caching data associated with the application to the multi-tenant cache storage device are managed based on the estimated amount of free space in the portion of the multi-tenant cache storage device allocated to the application.

    CROSS-MODAL SEMI-SUPERVISED DATA LABELING

    公开(公告)号:US20220172106A1

    公开(公告)日:2022-06-02

    申请号:US17108240

    申请日:2020-12-01

    Abstract: One or more computer processors extract respective features for each inter-modal sample in an inter-modal dataset, for each intra-modal sample in an intra-modal dataset, and a subsequent sample, wherein the inter-modal dataset and the intra-modal dataset are contained in a multi-modal training dataset. The one or more computer processors estimate an inter-modal label utilizing inter-modal label transformation of a subsequent sample. The one or more computer processors estimate an intra-modal label utilizing intra-modal label transformation of the subsequent sample. The one or more computer processors label the subsequent sample with a cross-modal label by combining the estimated inter-modal label and the estimated intra-modal label.

    WORKLOAD SCHEDULER FOR HIGH AVAILABILITY
    4.
    发明公开

    公开(公告)号:US20240069974A1

    公开(公告)日:2024-02-29

    申请号:US17822155

    申请日:2022-08-25

    CPC classification number: G06F9/505 G06F11/0751

    Abstract: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include synchronizing a policy between a primary node and a compute node and maintaining a resource registry on a client of the primary node. The operations may include communicating a direct communication between the client and the compute node, and the direct communication may include a first task. The operations may include returning a first result for the first task directly from the compute node to the client.

    SYNCHRONIZING ITEM RECOMMENDATIONS ACROSS APPLICATIONS USING MACHINE LEARNING

    公开(公告)号:US20230135794A1

    公开(公告)日:2023-05-04

    申请号:US17452732

    申请日:2021-10-28

    Abstract: A computer provides item recommendations for transactions. The computer receives a request to conduct a first transaction by a user and receives a first list of items and associated First List Item Metadata “FLIM”. The computer applies a first application ranking methodology to the FLIM and presents an Arranged First List of Items to the user. After completing the transaction, the computer identifies an Updated First List Item “UFLI” having a Predetermined Metadata Changing Condition. The computer identifies, items similar to the UFLI and revises the corpus metadata by applying a correction action to the UFLI and similar items. The computer receives a request to conduct a second transaction by a user and receives a second list of items and Second List Item Metadata “SLIM”. The computer applies a second application ranking methodology to the SLIM and presents an Arranged Second List of Items to the user.

    Edge computing workload balancing

    公开(公告)号:US11586480B2

    公开(公告)日:2023-02-21

    申请号:US17102581

    申请日:2020-11-24

    Abstract: A set of workload criteria is determined from a workload associated with a plurality of sources. The workload is divided among a set of workload groups according to the set of workload criteria and a first workload scheduler. A set of edge computing resources is assigned to each workload group within the set according to the set of workload criteria and the set of workload groups. A portion of the workload associated with a subset of the plurality of sources is handled by a first subset of edge computing resources and a second workload scheduler, where the subset of sources is associated with a first workload group. The handling includes balancing, by the second workload scheduler, the portion of the workload among the subset of sources. The handled workload is reported to a control center.

    Caching a block of data in a multi-tenant cache storage device based on space usage boundary estimates

    公开(公告)号:US10572383B2

    公开(公告)日:2020-02-25

    申请号:US15485347

    申请日:2017-04-12

    Abstract: Mechanisms are provided for managing caching of data in a multi-tenant cache storage device utilized by a distributed dataset based application. The mechanisms retrieve Resilient Distributed Dataset (RDD) block size information for an RDD associated with an application. A probability distribution of RDD block sizes is generated based on the RDD block size information and a maximum size for a RDD block of data is estimated based on the probability distribution. An amount of free space in a portion of the multi-tenant cache storage device allocated to the application is estimated based on the estimated maximum size for the RDD block of data. Cache operations for caching data associated with the application to the multi-tenant cache storage device are managed based on the estimated amount of free space in the portion of the multi-tenant cache storage device allocated to the application.

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