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公开(公告)号：US10560332B2
公开(公告)日：2020-02-11
申请号：US16273150
申请日：2019-02-12
Applicant: salesforce.com, inc.
Inventor： Shauli Gal , Satish Raghunath , Kartikeya Chandrayana , Tejaswini Ganapathi
Abstract: An adaptive multi-phase approach to estimating network parameters is presented. By gathering and aggregating raw network traffic data and comparing against default network parameters, a training data set may be generated. A black box optimization may be used in tandem with a supervised learning algorithm to bias towards better choices and eventually pick network parameters which optimize performance. Data delivery strategies are applied to deliver content using the optimized network policies based on the estimated parameters.
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12.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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公开(公告)号：US20190173760A1
公开(公告)日：2019-06-06
申请号：US16273150
申请日：2019-02-12
Applicant: salesforce.com, inc.
Inventor： Shauli Gal , Satish Raghunath , Kartikeya Chandrayana , Tejaswini Ganapathi
CPC classification number: H04L41/0893 , H04L41/046 , H04L43/16 , H04L67/02 , H04L67/28 , H04L67/2833 , H04L69/16
Abstract: An adaptive multi-phase approach to estimating network parameters is presented. By gathering and aggregating raw network traffic data and comparing against default network parameters, a training data set may be generated. A black box optimization may be used in tandem with a supervised learning algorithm to bias towards better choices and eventually pick network parameters which optimize performance. Data delivery strategies are applied to deliver content using the optimized network policies based on the estimated parameters.
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14.发明申请公开(公告)号：US20190141542A1
公开(公告)日：2019-05-09
申请号：US15803557
申请日：2017-11-03
Applicant: salesforce.com, inc.
Inventor： Tejaswini Ganapathi , Satish Raghunath , Shauli Gal
CPC classification number: H04W24/02 , G06N20/00 , H04L41/0823 , H04L41/0893 , H04L41/14 , H04L69/16 , H04W24/08
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.
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公开(公告)号：US20180331908A1
公开(公告)日：2018-11-15
申请号：US15593635
申请日：2017-05-12
Applicant: salesforce.com, inc.
Inventor： Shauli Gal , Satish Raghunath , Kartikeya Chandrayana , Tejaswini Ganapathi
CPC classification number: H04L41/0893 , H04L41/046 , H04L43/16 , H04L67/02 , H04L67/28 , H04L67/2833 , H04L69/16
Abstract: An adaptive multi-phase approach to estimating network parameters is presented. By gathering and aggregating raw network traffic data and comparing against default network parameters, a training data set may be generated. A black box optimization may be used in tandem with a supervised learning algorithm to bias towards better choices and eventually pick network parameters which optimize performance. Data delivery strategies are applied to deliver content using the optimized network policies based on the estimated parameters.
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Patent Agency Ranking
公开(公告)号：US11483374B2
公开(公告)日：2022-10-25
申请号：US16723952
申请日：2019-12-20
Applicant: salesforce.com, inc.
Inventor： Tejaswini Ganapathi , Satish Raghunath , Kartikeya Chandrayana , Shauli Gal
IPC: G06N20/00 , H04L41/14 , H04L41/16 , H04L43/08 , H04L67/06 , H04L69/163 , H04L69/16 , H04W24/02 , H04L67/56 , H04L67/568
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：US20220094619A1
公开(公告)日：2022-03-24
申请号：US17538815
申请日：2021-11-30
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.
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公开(公告)号：US11271840B2
公开(公告)日：2022-03-08
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 , 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：US20210234942A1
公开(公告)日：2021-07-29
申请号：US16775834
申请日：2020-01-29
Applicant: salesforce.com, inc.
Inventor： Tejaswini Ganapathi , Kartikeya Chandrayana , Satish Raghunath
IPC: H04L29/06 , G06F16/958 , H04L12/26
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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公开(公告)号：US10944631B1
公开(公告)日：2021-03-09
申请号：US16775847
申请日：2020-01-29
Applicant: salesforce.com, inc.
Inventor： Tejaswini Ganapathi , Satish Raghunath , Kartikeya Chandrayana
Abstract: Network requests are made to download a data object with different settings of network parameters. Download outcomes of the data object as requested by the network requests are determined. An elasticity of downloading the data object is determined with respect to a specific network parameter in the network parameters. The elasticity is used to generate a network optimization policy that identifies an optimal value for the specific network parameter to be implemented by user devices and/or other devices/elements for downloading the data object.
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