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.

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.

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.

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.

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.

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.

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.

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.

Abstract: 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.

Abstract: A system for improving network data communications performance between end-user devices and content origin servers. A plurality of accelerators are distributed across a network and communicate with end-user devices and content origin servers. Software development kits installed on end-user devices monitor control channel data traffic between the end-user devices and accelerators. Various data traffic efficiency approaches are implemented by the software development kits on the end-user devices and the plurality of accelerators resulting in improved data communications links between the end-user devices and the content origin servers.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.