METHOD AND APPARATUS FOR DATA OFFLOADING FOR VEHICLE EDGE COMPUTING

    公开(公告)号:US20230180092A1

    公开(公告)日:2023-06-08

    申请号:US17671057

    申请日:2022-02-14

    CPC classification number: H04W36/32 H04W72/0446 H04W4/44

    Abstract: A data offloading method for vehicle edge computing is provided. The data offloading method may include transmitting input data to a road side unit (RSU) closest to a vehicle through an uplink, and receiving output data processed by the RSU closest to the vehicle through a downlink. The transmitting of the input data to the RSU closest to the vehicle through the uplink may include comparing a location of the vehicle with locations of candidate RSUs, and the transmitting of the input data to the RSU closest to the vehicle through the uplink and receiving of the output data processed by the RSU closest to the vehicle through the downlink may be performed in a state of occupying at least one time frame among time frames allocated for a communication between the vehicle and the candidate RSUs.

    INFORMATION PROCESSING APPARATUS AND INFORMATION PROCESSING METHOD

    公开(公告)号:US20230171205A1

    公开(公告)日:2023-06-01

    申请号:US18056785

    申请日:2022-11-18

    CPC classification number: H04L47/805 H04L47/83 H04L47/823

    Abstract: An information processing apparatus comprises a controller. The controller is configured to execute: acquiring a plurality of datasets, each of the datasets being configured with a combination of training data and a correct answer label; and implementing machine learning of an estimation model using the acquired plurality of datasets, wherein the training data includes workload information about an application constructed based on a microservice architecture and resource use information about resources used for each of components included in the application, in a learning target environment, the correct answer label is configured to show a true value of quality of service of the application, and the machine learning comprises training the estimation model such that, for each of the datasets, an estimated value of the quality of service calculated with the estimation model based on the training data corresponds to the true value shown by the correct answer label.

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