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公开(公告)号：US20210014126A1
公开(公告)日：2021-01-14
申请号：US17037501
申请日：2020-09-29
Applicant: salesforce.com, inc.
Inventor： Tejaswini Ganapathi , Satish Raghunath , Xu Che , Shauli Gal , Andrey Karapetov
Abstract: An data driven approach to generating synthetic data matrices is presented. By retrieving historical network traffic data, probabilistic models are generated. Optimal distribution families for a set of independent data segments are determined. Applications are tested and performance metrics are determined based on the generated synthetic data matrices.
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公开(公告)号：US20190138362A1
公开(公告)日：2019-05-09
申请号：US15803624
申请日：2017-11-03
Applicant: salesforce.com, inc.
Inventor： Tejaswini Ganapathi , Satish Raghunath , Shauli Gal , Kartikeya Chandrayana , Steve Wilburn
Abstract: Network traffic data associated with data requests to computer applications is collected. Specific values for specific scope-level fields are used to identify a specific scope. Traffic shares for combinations of values for specific sub-scope-level fields are determined. Based on the traffic shares, specific sub scopes are identified within the specific scope. It is determined whether customized network strategies developed specifically for the specific sub scopes are to be applied to handling new data requests that share the specific values for the specific scope-level fields and the specific combinations of values for the specific sub-scope-level fields. In response to determining that a customized network strategy for a sub scope is to be applied, estimated optimal values for network parameters in the customized network strategy are to be used by user devices to make new data requests to the computer applications.
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公开(公告)号：US11695674B2
公开(公告)日：2023-07-04
申请号：US17538815
申请日：2021-11-30
Applicant: salesforce.com, inc.
Inventor： Tejaswini Ganapathi , Shauli Gal , Satish Raghunath , Kartikeya Chandrayana
IPC: H04L43/0864 , H04L43/0882 , H04L43/0811
CPC classification number: H04L43/0882 , H04L43/0811 , H04L43/0864
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.
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公开(公告)号：US11570059B2
公开(公告)日：2023-01-31
申请号：US17507430
申请日：2021-10-21
Applicant: salesforce.com, inc.
Inventor： Tejaswini Ganapathi , Satish Raghunath , Shauli Gal
IPC: H04L41/16 , H04L47/21 , H04L43/062 , H04L41/142
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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公开(公告)号：US20210266384A1
公开(公告)日：2021-08-26
申请号：US17316492
申请日：2021-05-10
Applicant: salesforce.com, inc.
Inventor： Tejaswini Ganapathi , Kartikeya Chandrayana , Satish Raghunath
IPC: H04L29/06 , H04L12/26 , G06F16/958
Abstract: Network requests are made to download a data object for a display page with different time delays. Page load outcomes of the display page are determined. A criticality of downloading the data object with respect to the display page is determined using page load outcomes. Criticalities of data objects of the display page are used to generate a specific data object download order that prioritizes critical and/or blocking objects of the display page.
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Patent Agency Ranking
公开(公告)号：US20210234769A1
公开(公告)日：2021-07-29
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.
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公开(公告)号：US10609119B2
公开(公告)日：2020-03-31
申请号：US15803614
申请日：2017-11-03
Applicant: salesforce.com, inc.
Inventor： Tejaswini Ganapathi , Satish Raghunath , Kartikeya Chandrayana , Shauli Gal
Abstract: Network traffic data associated with data requests to computer applications based on static policies is collected. An optimization order is established among network parameters. A first network parameter of a higher rank in the optimization order is estimated based on the collected network traffic data before one or more other network parameters of lower ranks are estimated. Optimal values for the other network parameters are estimated based at least in part on the estimated first optimal value for the first network parameter. The estimated first optimal value of the first network parameter and the estimated optimal values for the other network parameters are propagated to be used by user devices to make new data requests to the computer applications.
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公开(公告)号：US20190141549A1
公开(公告)日：2019-05-09
申请号：US15803509
申请日：2017-11-03
Applicant: salesforce.com, inc.
Inventor： Tejaswini Ganapathi , Satish Raghunath , Shauli Gal , Kartikeya Chandrayana , Xu Che , Andrey Karapetov
Abstract: An data driven approach to emulating application performance is presented. By retrieving historical network traffic data, probabilistic models are generated to simulate wireless networks. Optimal distribution families for network values are determined. Performance data is captured from applications operating on simulated user devices operating on a virtual machine with a network simulator running sampled tuple values.
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29.发明申请公开(公告)号：US20190141543A1
公开(公告)日：2019-05-09
申请号：US15803586
申请日：2017-11-03
Applicant: salesforce.com, inc.
Inventor： Tejaswini Ganapathi , Satish Raghunath , Xu Che
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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30.发明申请公开(公告)号：US20190141113A1
公开(公告)日：2019-05-09
申请号：US15803614
申请日：2017-11-03
Applicant: salesforce.com, inc.
Inventor： Tejaswini Ganapathi , Satish Raghunath , Kartikeya Chandrayana , Shauli Gal
Abstract: Network traffic data associated with data requests to computer applications based on static policies is collected. An optimization order is established among network parameters. A first network parameter of a higher rank in the optimization order is estimated based on the collected network traffic data before one or more other network parameters of lower ranks are estimated. Optimal values for the other network parameters are estimated based at least in part on the estimated first optimal value for the first network parameter. The estimated first optimal value of the first network parameter and the estimated optimal values for the other network parameters are propagated to be used by user devices to make new data requests to the computer applications.
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