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.

公开(公告)号：US20190104037A1

公开(公告)日：2019-04-04

申请号：US15722746

申请日：2017-10-02

Applicant: salesforce.com, inc.

Inventor： Shauli Gal ,  Satish Raghunath ,  Kartikeya Chandrayana ,  Gabriel Tavridis ,  Kevin Wang

IPC: H04L12/26 ,  H04L12/24

CPC classification number: H04L43/16 ,  H04L41/046 ,  H04L41/064 ,  H04L41/0893 ,  H04L43/08

Abstract: A data-driven approach to network performance diagnosis and root-cause analysis is presented. By collecting and aggregating data attribute values across multiple components of a content delivery system and comparing against baselines for points of inspection, network performance diagnosis and root-cause analysis may be prioritized based on impact on content delivery. Recommended courses of action may be determined and provided based on the tracked network performance analysis at diagnosis points.

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.

公开(公告)号：US20210367874A1

公开(公告)日：2021-11-25

申请号：US17164810

申请日：2021-02-01

Applicant: salesforce.com, inc.

Inventor： Shauli Gal ,  Satish Raghunath ,  Kartikeya Chandrayana ,  Gabriel Tavridis ,  Kevin Wang

IPC: H04L12/26 ,  H04L12/24

Abstract: A data-driven approach to network performance diagnosis and root-cause analysis is presented. By collecting and aggregating data attribute values across multiple components of a content delivery system and comparing against baselines for points of inspection, network performance diagnosis and root-cause analysis may be prioritized based on impact on content delivery. Recommended courses of action may be determined and provided based on the tracked network performance analysis at diagnosis points.

公开(公告)号：US20210359972A1

公开(公告)日：2021-11-18

申请号：US17157945

申请日：2021-01-25

Applicant: salesforce.com, inc.

Inventor： Shauli Gal ,  Satish Raghunath ,  Kartikeya Chandrayana

IPC: H04L29/12 ,  H04L29/08

Abstract: A CDN traffic is optimized by a client-side system that maps the servers in the CDN system. Content requests from client devices for domain names are forwarded to servers in the CDN system that may be selected from the map to prevent a cache miss in the a server for a particular request for content.

公开(公告)号：US11050706B2

公开(公告)日：2021-06-29

申请号：US15466664

申请日：2017-03-22

Applicant: salesforce.com, inc.

Inventor： Shauli Gal ,  Satish Raghunath ,  Kartikeya Chandrayana

IPC: G06F15/16 ,  H04L29/12

Abstract: Network performance data, such as routing trip time between autonomous systems and data centers, is gathered and aggregated to determine optimal mappings of autonomous systems and data centers. Autonomous system based DNS steering may be automated by repeating a life cycle of determining the optimal mappings. Data delivery strategies are applied to a portion of a network to deliver content using the optimal mappings.

公开(公告)号：US10795954B2

公开(公告)日：2020-10-06

申请号：US15836679

申请日：2017-12-08

Applicant: salesforce.com, inc.

Inventor： Shauli Gal ,  Satish Raghunath ,  Kartikeya Chandrayana

IPC: G06F16/9535 ,  G06F16/9038 ,  G06F16/242

Abstract: Data analysis is performed through a series of commands that apply functions to an initial scope of data. In a client-server architecture, a data analyst may interact with and view a scope of data through a series of commands. Query formation may be performed at a server to generate reports of data to be presented at the client.

公开(公告)号：US10778522B2

公开(公告)日：2020-09-15

申请号：US15841099

申请日：2017-12-13

Applicant: salesforce.com, inc.

Inventor： Gabriel Tavridis ,  Kartikeya Chandrayana ,  Maria Garcia Cerdeno ,  Russell Larsen ,  Satish Raghunath ,  Shauli Gal ,  Wojciech Koszek

IPC: G06F15/177 ,  H04L12/24 ,  H04L12/26 ,  G06N20/00

Abstract: A dynamic approach to optimizing configuration of network parameters is presented. By gathering operational contexts and aggregating optimized network performance data against a baseline, a training data set may be generated. Client-side policies are determined, in part, by applying machine learning techniques on the training data set to achieve desired outcomes. Data delivery strategies are compiled at user devices to deliver content using the optimized network configuration values based on the operating contexts.

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

IPC: H04L12/26 ,  H04L12/24 ,  H04L29/06 ,  H04L29/08

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.

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

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.