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    Analytics-driven direction for computer storage subsystem device behavior
    3.
    发明授权
    Analytics-driven direction for computer storage subsystem device behavior 有权

    公开(公告)号：US11436145B1

    公开(公告)日：2022-09-06

    申请号：US17301290

    申请日：2021-03-30

    Applicant: KYNDRYL, INC.

    Inventor： Anil Kumar Narigapalli ,  Laxmikantha Sai Nanduru ,  Clea Zolotow ,  Gavin Charles O'Reilly ,  Venkateswarlu Basyam

    IPC: G06F12/0862 ,  G06N20/20

    Abstract: A computer directs activity within a computer storage subsystem. The computer identifies a computer operating environment including a computer, and a storage subsystem connected to a group of storage devices. The compute receives metadata representing current and historic performance metrics of said computer operating environment. The computer identifies a first device associated with a first behavior profile governed by a power law distribution, and a second device associated with a second behavior profile governed by a normal distribution. The computer trains Machine Learning (ML) models based on the behavior profiles. The computer establishes Device Performance Rules based on the ML models. The computer forecasts time-based storage system requirements based, at least in part on the Device Performance Rules. The computer prefetches data to a cache component based, at least in part on said forecasted system requirements, in accordance with a time reference available to said computer.

    Dynamically assigning storage objects to compartment constructs of a storage system to reduce application risk
    4.
    发明授权
    Dynamically assigning storage objects to compartment constructs of a storage system to reduce application risk 有权

    公开(公告)号：US12056353B2

    公开(公告)日：2024-08-06

    申请号：US18093267

    申请日：2023-01-04

    Applicant: Kyndryl, Inc.

    Inventor： Anil Kumar Narigapalli ,  Laxmikantha Sai Nanduru ,  Pritpal S. Arora ,  James E. Olson ,  Mark Vincent Chitti

    IPC: G06F3/06

    CPC classification number: G06F3/0604 ,  G06F3/0614 ,  G06F3/0644 ,  G06F3/067

    Abstract: A computer-implemented method, according to one embodiment, includes logically partitioning a storage system into a plurality of compartment constructs, and mapping hosts in communication with the storage system to the compartment constructs, thereby enabling interoperability among the hosts and the compartment constructs. The interoperability of the hosts and the compartment constructs is analyzed, and the interoperability is based on storage software and/or firmware versions being run by the hosts. The method further includes defining, based on the analysis, risk profiles for applications run on the hosts, and determining, based on the risk profiles, recommendations for assignment and mapping of the hosts with the compartment constructs. Ownership of storage objects is assigned to the compartment constructs based on the recommendations. Each of the storage objects define a logical partition of one of the hosts and a logical partition of a storage volume of the storage system.

    DYNAMICALLY ASSIGNING STORAGE OBJECTS TO COMPARTMENT CONSTRUCTS OF A STORAGE SYSTEM TO REDUCE APPLICATION RISK
    5.
    发明公开
    DYNAMICALLY ASSIGNING STORAGE OBJECTS TO COMPARTMENT CONSTRUCTS OF A STORAGE SYSTEM TO REDUCE APPLICATION RISK 审中-公开

    公开(公告)号：US20240220102A1

    公开(公告)日：2024-07-04

    申请号：US18093267

    申请日：2023-01-04

    Applicant: Kyndryl, Inc.

    Inventor： Anil Kumar Narigapalli ,  Laxmikantha Sai Nanduru ,  Pritpal S. Arora ,  James E. Olson ,  Mark Vincent Chitti

    IPC: G06F3/06

    CPC classification number: G06F3/0604 ,  G06F3/0614 ,  G06F3/0644 ,  G06F3/067

    Abstract: A computer-implemented method, according to one embodiment, includes logically partitioning a storage system into a plurality of compartment constructs, and mapping hosts in communication with the storage system to the compartment constructs, thereby enabling interoperability among the hosts and the compartment constructs. The interoperability of the hosts and the compartment constructs is analyzed, and the interoperability is based on storage software and/or firmware versions being run by the hosts. The method further includes defining, based on the analysis, risk profiles for applications run on the hosts, and determining, based on the risk profiles, recommendations for assignment and mapping of the hosts with the compartment constructs. Ownership of storage objects is assigned to the compartment constructs based on the recommendations. Each of the storage objects define a logical partition of one of the hosts and a logical partition of a storage volume of the storage system.

    DIGITAL TWIN SIMULATION OF EQUILIBRIUM STATE
    6.
    发明申请
    DIGITAL TWIN SIMULATION OF EQUILIBRIUM STATE 有权

    公开(公告)号：US20230062028A1

    公开(公告)日：2023-03-02

    申请号：US17412649

    申请日：2021-08-26

    Applicant: KYNDRYL, INC.

    Inventor： Sarbajit K. Rakshit ,  Pritpal S. Arora ,  Laxmikantha Sai Nanduru

    IPC: G06F30/20 ,  G06T19/00 ,  G16Y20/20 ,  G16Y20/10

    Abstract: A processor may receive object data associated with a position and an orientation of a first object in an environment from IoT sensors. The processor may generate a digital twin simulation of the first object. In some embodiments, the digital twin simulation may include data associated with the relative positions and orientations of one or more other objects to the first object. The processor may calculate forces acting on the first object. The processor may identify whether the first object is in a state of instability.

    Optimizing device-to-device communication protocol selection in an edge computing environment
    7.
    发明授权
    Optimizing device-to-device communication protocol selection in an edge computing environment 有权

    公开(公告)号：US11553038B1

    公开(公告)日：2023-01-10

    申请号：US17508045

    申请日：2021-10-22

    Applicant: KYNDRYL, INC.

    Inventor： Sarbajit K. Rakshit ,  Pritpal S. Arora ,  Laxmikantha Sai Nanduru

    IPC: H04L41/5022 ,  H04L67/1001

    Abstract: A method for optimizing device-to-device communication protocol selection in an edge computing environment is provided. The method includes: receiving a request for a service from a user device, wherein the computing system is one of plural edge computing devices in an edge computing environment; determining computational tasks performed in providing the service; selecting, using a machine learning model, a set of the edge computing devices to perform the computational tasks and communication protocols for the set of the edge computing devices to use while performing the computational tasks, wherein the machine learning model is configured to select the set of the edge computing devices and the communication protocols based on minimizing a time to perform the computational tasks; and sending instructions to perform the computational tasks, thereby causing the set of the edge computing devices to perform the service in response to the request from the user device.

    Data analytics for mitigation of data center thermal issues
    9.
    发明授权
    Data analytics for mitigation of data center thermal issues 有权

    公开(公告)号：US11644876B2

    公开(公告)日：2023-05-09

    申请号：US17301378

    申请日：2021-03-31

    Applicant: KYNDRYL, INC.

    Inventor： Anil Kumar Narigapalli ,  Laxmikantha Sai Nanduru ,  Venkateswarlu Basyam ,  Srilakshmi Surapaneni

    IPC: G06F1/20 ,  H05K7/20

    CPC classification number: G06F1/206 ,  H05K7/20709

    Abstract: Mitigating the impact of data center thermal environmental issues on production applications includes retrieving, by a computer, from a centralized repository first data corresponding to I/O and processing activities of an infrastructure component executing one or more applications, second data corresponding to an application-to-infrastructure map, and third data corresponding to a business priority of the one or more applications. Based on the first data and the second data, the one or more applications are mapped to heat generation values of the infrastructure component, and based on the mapping a thermal load of the one or more applications on the infrastructure component is determined using data analytics. Using the third data, the computer identifies an execution priority for the one or more applications, generates a correlative mapping between the execution priority and the thermal load of the one or more applications, and generates a resolution plan based on the correlative mapping.

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