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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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Patent Agency Ranking
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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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：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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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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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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