Abstract: An 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.

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: Network traffic data associated with data requests to computer applications is collected. Specific values for specific scope-level fields are used to identify a specific scope. Traffic shares for combinations of values for specific sub-scope-level fields are determined. Based on the traffic shares, specific sub scopes are identified within the specific scope. It is determined whether customized network strategies developed specifically for the specific sub scopes are to be applied to handling new data requests that share the specific values for the specific scope-level fields and the specific combinations of values for the specific sub-scope-level fields. In response to determining that a customized network strategy for a sub scope is to be applied, estimated optimal values for network parameters in the customized network strategy are to be used by user devices to make new data requests to the computer applications.

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.

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 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 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.

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.

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: 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 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 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: 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: A system and method for handling critical events in service delivery gateways. Events are defined that cause a transition from a master redundancy state to a standby redundancy state in service delivery gateways and a plurality of signal-routes are stored. Each signal-route is associated with one or more of the defined events. A first defined event is detected in the first service delivery gateway and causes a transition from the first master redundancy state to the first standby redundancy state in the first service delivery gateway and a change in a first signal-route from the plurality of signal-routes in the first service delivery gateway. The change in the first signal-route is advertised and a second service delivery gateway transitions from the first standby redundancy state to the first master redundancy state.

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: In general, techniques are described for load balancing, with a service node, packet flows using stateless load balancing that adapts to server failure to provide flow affinity to initially selected servers for the duration of respective flows. In one example, service node device applies stateless load balancing to packet flows to distribute the flows among a plurality of servers. The service node determines a failure of a failed server and then receives an initial packet of a packet flow from the packet flows and forwards the initial packet to an active server. The service node generates a mapping of the packet flow to the active server, determines a recovery of the failed server, receives a subsequent packet of the packet flow, and forwards the subsequent packet of the packet flow to the active server based at least on the mapping of the packet flow to the active server.

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.