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公开(公告)号：US20230059587A1
公开(公告)日：2023-02-23
申请号：US17894119
申请日：2022-08-23
Applicant: Salesforce.com, Inc.
Inventor： Shauli Gal , Satish Raghunath , Kartikeya Chandrayana
IPC: H04L61/4511 , H04L67/60
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：US11425019B2
公开(公告)日：2022-08-23
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: G06F15/173 , H04L43/16 , H04L43/08 , H04L41/046 , H04L41/0816 , H04L43/04 , H04L43/12 , H04L41/0893 , H04L41/0631
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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Patent Agency Ranking
公开(公告)号：US20200052995A1
公开(公告)日：2020-02-13
申请号：US16660732
申请日：2019-10-22
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：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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45.发明申请公开(公告)号：US20190342770A1
公开(公告)日：2019-11-07
申请号：US16511632
申请日：2019-07-15
Applicant: salesforce.com, inc.
Inventor： Tejaswini Ganapathi , Satish Raghunath , Shauli Gal
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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46.发明授权公开(公告)号：US10448267B2
公开(公告)日：2019-10-15
申请号：US15803557
申请日：2017-11-03
Applicant: salesforce.com, inc.
Inventor： Tejaswini Ganapathi , Satish Raghunath , Shauli Gal
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：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
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.
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