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.

    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: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.

    SYSTEM AND METHOD FOR DYNAMIC BANDWIDTH ADJUSTMENTS FOR CELLULAR INTERFACES IN A NETWORK ENVIRONMENT
    9.
    发明申请
    SYSTEM AND METHOD FOR DYNAMIC BANDWIDTH ADJUSTMENTS FOR CELLULAR INTERFACES IN A NETWORK ENVIRONMENT 审中-公开
    用于网络环境中细胞界面的动态带宽调整的系统和方法

    公开(公告)号:US20160261514A1

    公开(公告)日:2016-09-08

    申请号:US14639748

    申请日:2015-03-05

    Abstract: A method is provided in one example embodiment and may include determining a predicted average throughput for each of one or more cellular interfaces and adjusting bandwidth for each of the one or more of the cellular interfaces based, at least in part, on the predicted average throughput determined for each of the one or more cellular interfaces. Another method can be provided, which may include determining a variance in path metrics for multiple cellular interfaces and updating a routing table for the cellular interfaces using the determined variance if there is a difference between the determined variance and a previous variance determined for the cellular interfaces. Another method can be provided, which may include monitoring watermark thresholds for a MAC buffer; generating an interrupt when a particular watermark threshold for the MAC buffer is reached; and adjusting enqueueing of uplink packets into the MAC buffer based on the interrupt.

    Abstract translation: 在一个示例性实施例中提供了一种方法,并且可以包括:至少部分地基于预测的平均吞吐量来确定一个或多个蜂窝接口中的每个蜂窝接口的预测平均吞吐量并且调整所述一个或多个蜂窝接口中的每一个的带宽 为一个或多个蜂窝接口中的每一个确定。 可以提供另一种方法,其可以包括确定多个蜂窝接口的路径度量的方差,并且如果确定的方差与为蜂窝接口确定的先前方差之间存在差异,则使用所确定的方差来更新蜂窝接口的路由表 。 可以提供另一种方法,其可以包括监视MAC缓冲器的水印阈值; 当达到MAC缓冲器的特定水印阈值时产生中断; 并根据中断调整上行链路数据包进入MAC缓冲区。

    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.

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