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    VERIFIABLE MLOPS TO TRAIN ML MODELS ON AUTONOMOUS ENVIRONMENTS
    1.
    发明申请
    VERIFIABLE MLOPS TO TRAIN ML MODELS ON AUTONOMOUS ENVIRONMENTS 有权

    公开(公告)号：US20250045623A1

    公开(公告)日：2025-02-06

    申请号：US18362684

    申请日：2023-07-31

    Applicant: Red Hat, Inc.

    Inventor： Huamin Chen ,  Marcelo Amaral ,  Chen Wang ,  Sunyanan Choochotkaew ,  Eun Kyung Lee ,  Parul Singh ,  Kaiyi Liu

    IPC: G06N20/00

    Abstract: Systems and methods are presented to provide a first machine learning model to a collaboration platform. The systems and methods receive a second machine learning model from the collaboration platform that indicates the second machine learning model is based on the first machine learning model. The systems and methods test the second machine learning model using criteria corresponding to the first machine learning model to determine whether the second machine learning model is valid. In turn, the systems and methods publish the second machine learning model to a repository in response to determining that the second machine learning model is valid.

    ENERGY EFFICIENT COMPUTING WORKLOAD PLACEMENT
    2.
    发明公开
    ENERGY EFFICIENT COMPUTING WORKLOAD PLACEMENT 审中-公开

    公开(公告)号：US20230418688A1

    公开(公告)日：2023-12-28

    申请号：US17851409

    申请日：2022-06-28

    Applicant: Red Hat, Inc.

    Inventor： Chen Wang ,  Huamin Chen

    IPC: G06F9/50

    CPC classification number: G06F9/5094 ,  G06F9/5083 ,  G06F9/505 ,  G06F9/5038

    Abstract: A method includes obtaining an energy consumption profile for a plurality of computing nodes, determining resource utilization characteristics of each of the plurality of computing nodes, and estimating energy consumption for each of the plurality of computing nodes in view of the energy consumption profile and resource utilization characteristics of the plurality of computing nodes. The method further includes determining placement of a new workload on one or more of the plurality of computing nodes in view of the estimated energy consumption for each of the plurality of computing nodes and resource requirements of the new workload.

    ENERGY AWARE DATA REPLICATION
    3.
    发明公开
    ENERGY AWARE DATA REPLICATION 审中-公开

    公开(公告)号：US20230376101A1

    公开(公告)日：2023-11-23

    申请号：US17747890

    申请日：2022-05-18

    Applicant: Red Hat, Inc.

    Inventor： Huamin Chen ,  Chen Wang ,  Yehuda Sadeh-Weinraub

    IPC: G06F1/3206 ,  G06F1/28

    CPC classification number: G06F1/3206 ,  G06F1/28 ,  G06F2212/1028

    Abstract: It is determined that data stored on a storage device associated with a computing device powered by a power source is to be transmitted to a remote computing device. A power state of the power source is determined. An encoding mechanism is selected from a plurality of different encoding mechanisms based on the power state of the power source. The data is encoded based on the encoding mechanism to generate encoded data. The encoded data is transmitted to the remote computing device.

    ASSIGNING COMPUTER WORKLOADS TO NODES BASED ON ENERGY CONSUMPTION MODES OF THE NODES
    6.
    发明公开
    ASSIGNING COMPUTER WORKLOADS TO NODES BASED ON ENERGY CONSUMPTION MODES OF THE NODES 审中-公开

    公开(公告)号：US20230305905A1

    公开(公告)日：2023-09-28

    申请号：US17700584

    申请日：2022-03-22

    Applicant: Red Hat, Inc.

    Inventor： Huamin Chen ,  Chen Wang

    IPC: G06F9/50 ,  H04L67/61

    CPC classification number: G06F9/5083 ,  G06F9/5038 ,  G06F9/5044 ,  H04L67/322 ,  G06F2209/508

    Abstract: Computer workloads can be assigned to nodes of a distributed computing environment based on energy consumption modes employed by the nodes. In one example, a system can determine a first tag assigned to a workload. The first tag can indicate a compatibility of the workload with one or more energy consumption modes employable by one or more nodes of a distributed computing environment. The system can also determine a second tag assigned to a node of the distributed computing environment. The second tag can indicate an energy consumption mode employed by the node. The system can then determine a correspondence between the first tag and the second tag indicating that the workload is compatible with the energy consumption mode employed by the node. Based on determining the correspondence between the first tag and the second tag, the system can assign the workload to the node.

    CLOUD NATIVE AUTO-LABELING SYSTEM TO TRAIN CODE GENERATION MODELS
    7.
    发明申请
    CLOUD NATIVE AUTO-LABELING SYSTEM TO TRAIN CODE GENERATION MODELS 有权

    公开(公告)号：US20250068409A1

    公开(公告)日：2025-02-27

    申请号：US18455284

    申请日：2023-08-24

    Applicant: Red Hat, Inc.

    Inventor： Huamin Chen ,  Chen Wang

    IPC: G06F8/65 ,  G06F8/77 ,  G06N3/08

    Abstract: Systems and methods are disclosed that deploy software code from a dataset into a computing environment. The systems and method collect energy metrics of the software code while executing in the computing environment. The systems and methods determine a sustainability label for the software code based on the energy metrics. The systems and methods assign the sustainability label to the software code to produce a sustainability-based dataset.

    DYNAMICALLY ADAPTIVE AUTOSCALING FOR OBJECT STORAGE
    8.
    发明公开
    DYNAMICALLY ADAPTIVE AUTOSCALING FOR OBJECT STORAGE 审中-公开

    公开(公告)号：US20240220135A1

    公开(公告)日：2024-07-04

    申请号：US18091586

    申请日：2022-12-30

    Applicant: Red Hat, Inc.

    Inventor： Yuval Lifshitz ,  Chen Wang ,  Huamin Chen

    IPC: G06F3/06

    CPC classification number: G06F3/0626 ,  G06F3/0644 ,  G06F3/0673

    Abstract: The present disclosure is a new, innovative system and methods for dynamically adaptive autoscaling. An example system includes a memory and at least one processing device in communication with the memory. The processing device is configured to receive a request at a storage gateway microservice's request queue and process the request at the storage gateway microservice. The processing device is configured to store or retrieve data related to the processed request using a storage backend microservice. The processing device is configured to report to an observer, in a fixed interval, input, output, and resource usage metrics related to the processing of the request and storing or retrieving data. The observer is configured to determine a scaling decision using the metrics and transmit it to at least one scaler, which performs a scaling action on the storage gateway microservice, storage backend microservice, or both based on the scaling decision.

    CLOUD CLUSTER STORAGE RESOURCE AUTOSCALER
    10.
    发明公开
    CLOUD CLUSTER STORAGE RESOURCE AUTOSCALER 审中-公开

    公开(公告)号：US20230418671A1

    公开(公告)日：2023-12-28

    申请号：US17850774

    申请日：2022-06-27

    Applicant: Red Hat, Inc.

    Inventor： Huamin Chen ,  Chen Wang ,  Yuval Lifshitz

    IPC: G06F9/50

    CPC classification number: G06F9/5016

    Abstract: Methods, systems, and computer program products herein provide operations or techniques for managing resource allocation in a data storage environment. According to aspects of the present disclosure, one or more storage nodes of hierarchy on a common hierarchy level are identified as a management group. For example, the one or more storage nodes of hierarchy may include one or more object storage daemons (OSDs) of a controlled replication under scalable hashing (CRUSH) group or the like. The resource utilization in a subset of the one or more storage nodes in the management group are monitored. Based on the monitored resource utilization, a processing device may determine respective scaling factors for allocating resources to the one or more storage nodes in the management group. The processing device may then adjust the resource allocation using the respective scaling factors in the one or more storage nodes.

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