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公开(公告)号:US20190052518A1
公开(公告)日:2019-02-14
申请号:US15675479
申请日:2017-08-11
Applicant: salesforce.com, inc.
Inventor: Shauli Gal , Satish Raghunath , Kartikeya Chandrayana , Gabriel Tavridis , Kevin Wang
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. Alerts may be generated to present recommended courses of action based on the tracked performance analysis.
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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
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.
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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
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.
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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
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.
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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.
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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.
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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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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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公开(公告)号:US20190052597A1
公开(公告)日:2019-02-14
申请号:US15674945
申请日:2017-08-11
Applicant: salesforce.com, inc.
Inventor: Satish Raghunath , Kartikeya Chandrayana , Shauli Gal
Abstract: Network performance data metrics are gathered and aggregated. A policy engine chooses an optimal selection of networking protocol based on the metrics. Data delivery strategies are applied to a portion of a network to deliver content using the received choice of networking protocol policy optimized by machine learning techniques.
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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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