Salesforce Com, Inc.

Salesforce, Inc.

Juniper Networks, Inc.

Twin Prime, Inc.

Shauli Gal

Satish Raghunath

Kartikeya Chandrayana

Tejaswini Ganapathi

Gabriel Tavridis

Kevin Wang

Xu Che

Maria Garcia Cerdeno

Russell Larsen

Andrey Karapetov

Gopi Krishna

Sanjay Agrawal

Harsha Srinath

Radha Krishna Pagadala

Sankar Ramamoorthi

Ina Minei

Suresh Kumar Vinapamula Venkata

Wojciech A Koszek

Li Fang

Jwala Dinesh Gupta Chakka

Juan-Antonio Bernal Van Der Ven

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.

On demand synthetic data matrix generation

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.

Estimation of network quality metrics from network request data

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.

Inspecting network performance at diagnosis points

Bypass network traffic records are generated for a web application. Sufficient statistics of network optimization parameters are calculated for network performance categories. The bypass network traffic records are partitioned for the network performance categories into network traffic buckets. Sufficient statistics and the network traffic buckets are used to generate network quality mappings. The network quality mappings are used as training instances to train a machine learner for generating network optimization policies to be implemented by user devices.

Machine learning based end to end system for TCP optimization

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.

Simultaneous optimization of multiple TCP parameters to improve download outcomes for network-based mobile applications

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.

Content delivery network optimization system

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.

Network performance root-cause analysis

Machine learning based end to end system for tcp optimization

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.

Automated autonomous system based DNS steering

Based on expert input comprising recommended parameter values, a machine learning framework is constrained to perform machine learning to estimate optimal parameter values for TCP parameters in specific regions of an output parameter space. Network traffic data associated with a plurality of data requests to one or more computer applications are collected, over a time block, based on sampled parameter values for the TCP parameters. The sampled parameter values are distributed within the specific regions of the output parameter space. The machine learning is used to estimate the optimal parameter values for the TCP parameters. The optimal parameter values, for the TCP parameters, are propagated to and used by user devices to make new data requests to the computer applications.

Incorporation of expert knowledge into machine learning based wireless optimization framework

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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Mountain View, CA, United States of America

Shauli Gal