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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：US12009989B2
公开(公告)日：2024-06-11
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
IPC: H04L41/14 , G05B17/02 , G06F16/2458 , G06F17/16 , G06N7/01 , H04L41/142 , H04L43/08 , H04L43/0829 , H04L43/0852 , H04L43/087 , H04L43/0888
CPC classification number: H04L41/145 , G05B17/02 , G06F16/2477 , G06F17/16 , G06N7/01 , H04L41/142 , H04L43/08 , H04L43/0829 , H04L43/0858 , H04L43/087 , H04L43/0888
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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3.发明授权公开(公告)号：US10405208B2
公开(公告)日：2019-09-03
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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4.发明授权公开(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
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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公开(公告)号：US10791035B2
公开(公告)日：2020-09-29
申请号：US15803501
申请日：2017-11-03
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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Patent Agency Ranking
公开(公告)号：US10548034B2
公开(公告)日：2020-01-28
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: A 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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7.发明申请公开(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
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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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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9.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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公开(公告)号：US20190140910A1
公开(公告)日：2019-05-09
申请号：US15803501
申请日：2017-11-03
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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