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    INCORPORATION OF EXPERT KNOWLEDGE INTO MACHINE LEARNING BASED WIRELESS OPTIMIZATION FRAMEWORK
    2.
    发明申请
    INCORPORATION OF EXPERT KNOWLEDGE INTO MACHINE LEARNING BASED WIRELESS OPTIMIZATION FRAMEWORK 审中-公开

    公开(公告)号：US20190342770A1

    公开(公告)日：2019-11-07

    申请号：US16511632

    申请日：2019-07-15

    Applicant: salesforce.com, inc.

    Inventor： Tejaswini Ganapathi ,  Satish Raghunath ,  Shauli Gal

    IPC: H04W24/02 ,  H04L12/24 ,  H04W24/08 ,  G06N20/00 ,  H04L29/06

    Abstract: A polytope is generated, based on expert input, in an output parameter space. The polytope constrains network parameters to value ranges that are a subset of possible values represented in the output parameter space. Network traffic data associated with data requests to computer applications based on static policies is collected over a time block. Each static policy in the plurality of static policies comprises parameter values, for network parameters in the set of network parameters, that are constrained to be within the polytope. Machine learning is used to estimate best parameter values for the network parameters that are constrained to be within the polytope. The best parameter values are verified by comparing to parameter values determined from a black box optimization. The best parameter values are propagated to be used by user devices to make new data requests to the computer applications.

    Incorporation of expert knowledge into machine learning based wireless optimization framework
    3.
    发明授权
    Incorporation of expert knowledge into machine learning based wireless optimization framework 有权

    公开(公告)号：US10448267B2

    公开(公告)日：2019-10-15

    申请号：US15803557

    申请日：2017-11-03

    Applicant: salesforce.com, inc.

    Inventor： Tejaswini Ganapathi ,  Satish Raghunath ,  Shauli Gal

    IPC: H04W24/02 ,  H04W24/08 ,  H04L12/24 ,  H04L29/06 ,  G06F15/18 ,  G06N20/00

    Abstract: A polytope is generated, based on expert input, in an output parameter space. The polytope constrains network parameters to value ranges that are a subset of possible values represented in the output parameter space. Network traffic data associated with data requests to computer applications based on static policies is collected over a time block. Each static policy in the plurality of static policies comprises parameter values, for network parameters in the set of network parameters, that are constrained to be within the polytope. Machine learning is used to estimate best parameter values for the network parameters that are constrained to be within the polytope. The best parameter values are verified by comparing to parameter values determined from a black box optimization. The best parameter values are propagated to be used by user devices to make new data requests to the computer applications.

    AUTOMATIC PERFORMANCE MONITORING AND HEALTH CHECK OF LEARNING BASED WIRELESS OPTIMIZATION FRAMEWORK
    4.
    发明申请
    AUTOMATIC PERFORMANCE MONITORING AND HEALTH CHECK OF LEARNING BASED WIRELESS OPTIMIZATION FRAMEWORK 审中-公开

    公开(公告)号：US20190261200A1

    公开(公告)日：2019-08-22

    申请号：US16398990

    申请日：2019-04-30

    Applicant: salesforce.com, inc.

    Inventor： Tejaswini Ganapathi ,  Satish Raghunath ,  Xu Che

    IPC: H04W24/02 ,  H04W24/08

    Abstract: Network traffic data associated with computer applications is collected based on static policies. First network parameter vectors are generated over a time period. Each network parameter vector of the first network parameter vectors comprises first optimal values, estimated by a Bayesian learning module using a generative model, for network parameters. Second network parameter vectors are generated over the same time period. Each network parameter vector of the second network parameter vectors comprises second optimal values, computed by a best parameter generator through optimizing an objective function, for the network parameters. It is determined whether the first network parameter vectors converge to the second network parameter vectors and whether network parameter optimization for the network parameters is performing normally.

    Machine learning based end to end system for tcp optimization
    5.
    发明授权
    Machine learning based end to end system for tcp optimization 有权

    公开(公告)号：US11233704B2

    公开(公告)日：2022-01-25

    申请号：US16775807

    申请日：2020-01-29

    Applicant: salesforce.com, inc.

    Inventor： Tejaswini Ganapathi ,  Satish Raghunath ,  Shauli Gal

    IPC: H04L12/24 ,  H04L12/819 ,  H04L12/26

    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
    6.
    发明申请
    ESTIMATION OF NETWORK QUALITY METRICS FROM NETWORK REQUEST DATA 有权

    公开(公告)号：US20210234782A1

    公开(公告)日：2021-07-29

    申请号：US16775819

    申请日：2020-01-29

    Applicant: salesforce.com, inc.

    Inventor： Tejaswini Ganapathi ,  Shauli Gal ,  Satish Raghunath ,  Kartikeya Chandrayana

    IPC: H04L12/26

    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.

    Automatic performance monitoring and health check of learning based wireless optimization framework
    8.
    发明授权
    Automatic performance monitoring and health check of learning based wireless optimization framework 有权

    公开(公告)号：US10959113B2

    公开(公告)日：2021-03-23

    申请号：US16398990

    申请日：2019-04-30

    Applicant: salesforce.com, inc.

    Inventor： Tejaswini Ganapathi ,  Satish Raghunath ,  Xu Che

    IPC: H04W24/02 ,  H04W24/08

    Abstract: Network traffic data associated with computer applications is collected based on static policies. First network parameter vectors are generated over a time period. Each network parameter vector of the first network parameter vectors comprises first optimal values, estimated by a Bayesian learning module using a generative model, for network parameters. Second network parameter vectors are generated over the same time period. Each network parameter vector of the second network parameter vectors comprises second optimal values, computed by a best parameter generator through optimizing an objective function, for the network parameters. It is determined whether the first network parameter vectors converge to the second network parameter vectors and whether network parameter optimization for the network parameters is performing normally.

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