Machine learning based end to end system for tcp optimization

    公开(公告)号:US11233704B2

    公开(公告)日:2022-01-25

    申请号:US16775807

    申请日:2020-01-29

    Abstract: Bypass network traffic records are generated for a web application. Sufficient statistics of network optimization parameters are calculated for network performance categories. The bypass network traffic records are partitioned for the network performance categories into network traffic buckets. Sufficient statistics and the network traffic buckets are used to generate network quality mappings. The network quality mappings are used as training instances to train a machine learner for generating network optimization policies to be implemented by user devices.

    ESTIMATION OF NETWORK QUALITY METRICS FROM NETWORK REQUEST DATA

    公开(公告)号:US20210234782A1

    公开(公告)日:2021-07-29

    申请号:US16775819

    申请日:2020-01-29

    Abstract: Network request data is collected over a time window. The network request data is filtered to generate bypass network traffic records. Network performance categories are generated from the bypass network traffic records. Sufficient statistics of network optimization parameters are calculated for the network performance categories. The sufficient statistics of the network optimization parameters are used to generate network optimization parameters to determine data download performances of web applications.

    Machine learning based end to end system for TCP optimization

    公开(公告)号:US11570059B2

    公开(公告)日:2023-01-31

    申请号:US17507430

    申请日:2021-10-21

    Abstract: Bypass network traffic records are generated for a web application. Sufficient statistics of network optimization parameters are calculated for network performance categories. The bypass network traffic records are partitioned for the network performance categories into network traffic buckets. Sufficient statistics and the network traffic buckets are used to generate network quality mappings. The network quality mappings are used as training instances to train a machine learner for generating network optimization policies to be implemented by user devices.

    MACHINE LEARNING BASED END TO END SYSTEM FOR TCP OPTIMIZATION

    公开(公告)号:US20210234769A1

    公开(公告)日:2021-07-29

    申请号:US16775807

    申请日:2020-01-29

    Abstract: Bypass network traffic records are generated for a web application. Sufficient statistics of network optimization parameters are calculated for network performance categories. The bypass network traffic records are partitioned for the network performance categories into network traffic buckets. Sufficient statistics and the network traffic buckets are used to generate network quality mappings. The network quality mappings are used as training instances to train a machine learner for generating network optimization policies to be implemented by user devices.

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