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    MOBILE DEVICE AND SHELF ASSISTED ORDER FULFILLMENT
    11.
    发明公开
    MOBILE DEVICE AND SHELF ASSISTED ORDER FULFILLMENT 审中-公开

    公开(公告)号：US20230196277A1

    公开(公告)日：2023-06-22

    申请号：US17553047

    申请日：2021-12-16

    Applicant: International Business Machines Corporation

    Inventor： Sudheesh S. Kairali ,  Sarbajit K. Rakshit

    IPC: G06Q10/08

    CPC classification number: G06Q10/087

    Abstract: In an approach to facilitate assisted order fulfillment, a computer-implemented method receives order data corresponding to orders each comprising items, determines an item location for the items in an inventory shelf system comprising mobile shelves, determine a pickup path for a mobility device to travel within the inventory shelf system to retrieve the items based at least on the item location. Further, the method may include transmitting item retrieval data to the mobility device instructing the mobility device to retrieve the items from the mobile shelves along the pickup path, transmitting shelf arrangement data to the inventory shelf system instructing the mobile shelves to achieve an arrangement corresponding to the pickup path, and transmitting item delivery instructions to the inventory shelf system instructing the shelves to deliver the items to the mobility device as the mobility device traverses the item location along the optimal pickup path.

    Intelligently adaptive log level management of a service mesh
    13.
    发明授权
    Intelligently adaptive log level management of a service mesh 有权

    公开(公告)号：US11561849B1

    公开(公告)日：2023-01-24

    申请号：US17568928

    申请日：2022-01-05

    Applicant: International Business Machines Corporation

    Inventor： Sudheesh S. Kairali ,  Sarbajit K. Rakshit

    IPC: G06F11/00 ,  G06F11/07 ,  G06F9/54 ,  G06F11/14

    Abstract: Systems, methods and/or computer program products dynamically managing log levels of microservices in a service mesh based on predicted error rates of calls made to the service mesh. A first AI module predicts health, status and/or failures of microservices individually or as part of microservice chains with a particular confidence level. Using health status mapped to the microservices and historical information inputted into a knowledge base (including error rates), the first AI module predicts error rates of the API call for each user profile or generally by the service mesh. A second AI module analyzes the predictions provided by the first AI module and determines whether the predictions meet threshold levels of confidence. To improve the confidence of predictions that are below threshold levels, the second AI module dynamically adjusts application logs of the microservices and/or proxies thereof to an appropriate level to capture more detailed information within the logs.

    Dynamic handling of service mesh loads using sliced replicas and cloud functions
    14.
    发明授权
    Dynamic handling of service mesh loads using sliced replicas and cloud functions 有权

    公开(公告)号：US11522948B1

    公开(公告)日：2022-12-06

    申请号：US17592972

    申请日：2022-02-04

    Applicant: International Business Machines Corporation

    Inventor： Sudheesh S. Kairali ,  Sarbajit K. Rakshit

    IPC: H04L67/1008 ,  H04L45/42 ,  H04L67/1012 ,  H04L67/1014

    Abstract: Systems, methods and/or computer program products managing momentary increases in resource requirements of microservices within a service mesh using sliced replicas or pre-configured cloud functions. The service mesh tracks incoming API calls of applications requesting use of specific microservice capabilities. Over time capabilities being called are categorized by the service mesh based on call frequency. Service mesh predicts patterns of expected increases in requests for each capability of the microservices. As the increase in the number of API calls for the capability become more than a threshold level predicted by the service mesh, a sliced replica of the microservice is created or a pre-configured cloud function is enabled to handle requests specifically for the capability causing the momentary increase in resource requirements, while the service mesh continues routing API calls for other capabilities to the requested microservice. Upon resource requirements subsiding, the sliced replica or pre-configured cloud function is removed.

    Smart egress in service mesh
    15.
    发明授权
    Smart egress in service mesh 有权

    公开(公告)号：US11489939B1

    公开(公告)日：2022-11-01

    申请号：US17569633

    申请日：2022-01-06

    Applicant: International Business Machines Corporation

    Inventor： Sudheesh S. Kairali ,  Sarbajit K. Rakshit

    IPC: H04L67/568

    Abstract: A processor may receive a microservice chain. The processor may analyze the microservice chain for one or more microservices in the microservice chain that include deducible information. The processor may trigger, from the deducible information, a deduced microservice. The processor may process the deduced microservice. The processor may cache an outcome from the deduced microservice.

    Optimizing timeouts and polling intervals
    17.
    发明授权
    Optimizing timeouts and polling intervals 有权

    公开(公告)号：US11157318B2

    公开(公告)日：2021-10-26

    申请号：US16448375

    申请日：2019-06-21

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

    Inventor： Sudheesh S. Kairali

    IPC: G06F9/50 ,  G06F9/455 ,  G06F11/30

    Abstract: An approach is provided for managing a timeout and polling interval of an operation of an application. A recommendation specifying the timeout and polling interval is selected. The timeout and polling interval are applied to a deployed image. Based on polling intervals, numbers of polls for operations, identifications of the operations, and environments of the operations, a minimum number of polls of the operation in an environment before a successful completion of the operation is determined and an old polling interval used between the polls of the operation is determined. If the minimum number of polls is greater than one, the polling interval specified in the recommendation is determined as the minimum number of polls multiplied by the old polling interval. If the minimum number of polls equals one, the polling interval specified in the recommendation is determined by decreasing the old polling interval by a configurable factor.

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