Service plane optimizations with learning-enabled flow identification

    公开(公告)号:US11252170B2

    公开(公告)日:2022-02-15

    申请号:US16534987

    申请日:2019-08-07

    Abstract: The disclosed technology relates to a process for optimizing data flow within a computer network. The technology utilizes shared memory and machine learning logic to improve the efficiency of how computing resources are used during a transmission of data packets in the computer network. The shared memory is implemented during the transmission of data packets between the data plane and the service plane so that the copying of data packets after the data packets have been received and processed by an application is not necessary. The machine learning logic is implemented during the processing of the data packets in order to adjust a frequency or extent that the data packets (and corresponding source of the data packets) need to be evaluated to ensure that malicious content is not being transmitted across the computer network.

    Service plane optimizations with learning-enabled flow identification

    公开(公告)号:US12255910B2

    公开(公告)日:2025-03-18

    申请号:US18462025

    申请日:2023-09-06

    Abstract: The disclosed technology relates to a process for optimizing data flow within a computer network. The technology utilizes shared memory and machine learning logic to improve the efficiency of how computing resources are used during a transmission of data packets in the computer network. The shared memory is implemented during the transmission of data packets between the data plane and the service plane so that the copying of data packets after the data packets have been received and processed by an application is not necessary. The machine learning logic is implemented during the processing of the data packets in order to adjust a frequency or extent that the data packets (and corresponding source of the data packets) need to be evaluated to ensure that malicious content is not being transmitted across the computer network.

    SERVICE PLANE OPTIMIZATIONS WITH LEARNING-ENABLED FLOW IDENTIFICATION

    公开(公告)号:US20230421594A1

    公开(公告)日:2023-12-28

    申请号:US18462025

    申请日:2023-09-06

    CPC classification number: H04L63/1425 H04L43/062

    Abstract: The disclosed technology relates to a process for optimizing data flow within a computer network. The technology utilizes shared memory and machine learning logic to improve the efficiency of how computing resources are used during a transmission of data packets in the computer network. The shared memory is implemented during the transmission of data packets between the data plane and the service plane so that the copying of data packets after the data packets have been received and processed by an application is not necessary. The machine learning logic is implemented during the processing of the data packets in order to adjust a frequency or extent that the data packets (and corresponding source of the data packets) need to be evaluated to ensure that malicious content is not being transmitted across the computer network.

    Service plane optimizations with learning-enabled flow identification

    公开(公告)号:US11777973B2

    公开(公告)日:2023-10-03

    申请号:US17592160

    申请日:2022-02-03

    CPC classification number: H04L63/1425 H04L43/062

    Abstract: The disclosed technology relates to a process for optimizing data flow within a computer network. The technology utilizes shared memory and machine learning logic to improve the efficiency of how computing resources are used during a transmission of data packets in the computer network. The shared memory is implemented during the transmission of data packets between the data plane and the service plane so that the copying of data packets after the data packets have been received and processed by an application is not necessary. The machine learning logic is implemented during the processing of the data packets in order to adjust a frequency or extent that the data packets (and corresponding source of the data packets) need to be evaluated to ensure that malicious content is not being transmitted across the computer network.

    Millimeter wave (mmWave) radio resource allocation scheme for vehicle-to-infrastructure (V2I) communications

    公开(公告)号:US10798755B2

    公开(公告)日:2020-10-06

    申请号:US16265533

    申请日:2019-02-01

    Abstract: In one embodiment, a millimeter wave (mmWave) radio resource allocation scheme for vehicle-to-infrastructure (V2I) communications is disclosed, which may illustratively comprise receiving, by a base station, a connection request of a plurality of connection requests from a mobile station of a plurality of mobile stations; determining, by the base station, a resource block allocation scheme that is formulated as a two dimensional rectangular bin for the plurality of mobile stations; allocating, by the base station, one or more resource sub-blocks of the resource block allocation scheme to the mobile station using at least one packing parameter; and controlling, by the base station, the mobile station to communicate with the base station using the one or more resource sub-blocks.

    SERVICE PLANE OPTIMIZATIONS WITH LEARNING-ENABLED FLOW IDENTIFICATION

    公开(公告)号:US20220159027A1

    公开(公告)日:2022-05-19

    申请号:US17592160

    申请日:2022-02-03

    Abstract: The disclosed technology relates to a process for optimizing data flow within a computer network. The technology utilizes shared memory and machine learning logic to improve the efficiency of how computing resources are used during a transmission of data packets in the computer network. The shared memory is implemented during the transmission of data packets between the data plane and the service plane so that the copying of data packets after the data packets have been received and processed by an application is not necessary. The machine learning logic is implemented during the processing of the data packets in order to adjust a frequency or extent that the data packets (and corresponding source of the data packets) need to be evaluated to ensure that malicious content is not being transmitted across the computer network.

    MILLIMETER WAVE (mmWave) RADIO RESOURCE ALLOCATION SCHEME FOR VEHICLE-TO-INFRASTRUCTURE (V2I) COMMUNICATIONS

    公开(公告)号:US20200128591A1

    公开(公告)日:2020-04-23

    申请号:US16265533

    申请日:2019-02-01

    Abstract: In one embodiment, a millimeter wave (mmWave) radio resource allocation scheme for vehicle-to-infrastructure (V2I) communications is disclosed, which may illustratively comprise receiving, by a base station, a connection request of a plurality of connection requests from a mobile station of a plurality of mobile stations; determining, by the base station, a resource block allocation scheme that is formulated as a two dimensional rectangular bin for the plurality of mobile stations; allocating, by the base station, one or more resource sub-blocks of the resource block allocation scheme to the mobile station using at least one packing parameter; and controlling, by the base station, the mobile station to communicate with the base station using the one or more resource sub-blocks.

    SERVICE PLANE OPTIMIZATIONS WITH LEARNING-ENABLED FLOW IDENTIFICATION

    公开(公告)号:US20210044605A1

    公开(公告)日:2021-02-11

    申请号:US16534987

    申请日:2019-08-07

    Abstract: The disclosed technology relates to a process for optimizing data flow within a computer network. The technology utilizes shared memory and machine learning logic to improve the efficiency of how computing resources are used during a transmission of data packets in the computer network. The shared memory is implemented during the transmission of data packets between the data plane and the service plane so that the copying of data packets after the data packets have been received and processed by an application is not necessary. The machine learning logic is implemented during the processing of the data packets in order to adjust a frequency or extent that the data packets (and corresponding source of the data packets) need to be evaluated to ensure that malicious content is not being transmitted across the computer network.

    SERVICE PLANE OPTIMIZATIONS WITH LEARNING-ENABLED FLOW IDENTIFICATION

    公开(公告)号:US20250168185A1

    公开(公告)日:2025-05-22

    申请号:US19034804

    申请日:2025-01-23

    Abstract: The disclosed technology relates to a process for optimizing data flow within a computer network. The technology utilizes shared memory and machine learning logic to improve the efficiency of how computing resources are used during a transmission of data packets in the computer network. The shared memory is implemented during the transmission of data packets between the data plane and the service plane so that the copying of data packets after the data packets have been received and processed by an application is not necessary. The machine learning logic is implemented during the processing of the data packets in order to adjust a frequency or extent that the data packets (and corresponding source of the data packets) need to be evaluated to ensure that malicious content is not being transmitted across the computer network.

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