A switch may operate in a cut-through mode and a store-and-forward mode. While in a default cut-through mode, the switch continuously monitors ports for certain health metrics. If those health metrics fall below a threshold, the switch changes to operate in a store-and-forward mode, either for a predetermined period of time or until the health metrics rise above a threshold, at which point the switch can resume cut-through mode operations. If health metrics fall below an even lower threshold, or remain below threshold for a predefined period of time, the switch can automatically alert a remote system or software process.

The disclosure describes various aspects of crowdsourcing traffic data for automatic and dynamic benchmarking of applications. In an aspect, an intelligence layer, communicatively coupled to a data collection layer and a visualization layer, is configured to receive traffic data from data sources (e.g., physical appliances, probes) in the data collection layer, the data sources being associated with multiple customers, and the traffic data being associated with at least one application (e.g., word processing, video streaming) used by the multiple customers. The intelligence layer is a cloud-based layer further configured to process the traffic data to determine performance thresholds for the at least one application, and may send one or more of the performance thresholds to a data source for a different customer to be used for benchmarking the at least one application for the different customer.

The disclosure describes various aspects of crowdsourcing traffic data for automatic and dynamic benchmarking of applications. In an aspect, an intelligence layer, communicatively coupled to a data collection layer and a visualization layer, is configured to receive traffic data from data sources (e.g., physical appliances, probes) in the data collection layer, the data sources being associated with multiple customers, and the traffic data being associated with at least one application (e.g., word processing, video streaming) used by the multiple customers. The intelligence layer is a cloud-based layer further configured to process the traffic data to determine performance thresholds for the at least one application, and may send one or more of the performance thresholds to a data source for a different customer to be used for benchmarking the at least one application for the different customer.

A method for performing a performance measurement in a communication network. Each measurement point in the network identifies packets of a multipoint packet flow and selects therefrom a number of samples, based on the value of a sampling signature calculated by applying a hash function to a bit mask in each identified packet. For each sample, a performance parameter and the packet's content are provided to a management server. The management server identifies a cluster of measurement points such that each identified packet of the multipoint packet flow received by a cluster's input measurement point is also received at a cluster's output measurement point. Amongst the performance parameters provided by the cluster's measurement points, the performance parameters relating to samples belonging to a certain packet sub-flow are identified, based on the packet's content. Then, a performance measurement is performed on the packet sub-flow.

Systems and methods are disclosed herein for monitoring health of each switch of a plurality of switches on a network by selectively mirroring packets transmitted by each switch of the plurality of switches. In some embodiments, control circuitry generates a plurality of mirroring parameters, each mirroring parameter comprising an instruction to mirror a respective type of packet. The control circuitry transmits the plurality of mirroring parameters to each switch of the plurality of switches on the network, and receives, from a switch, a packet that was mirrored by the switch according to a mirroring parameter of the plurality of mirroring parameters. The control circuitry determines the respective type of the packet, executes an analysis of contents of the packet based on the respective type of the packet, and determines a health of the switch based on results of the analysis.

What is disclosed is a method for efficient capture and streaming of data packets in a network device comprises capturing data packets matching predetermined filters, packaging said data packets into samples, and aggregating one or more samples in a high speed bus payload. The method also comprises transferring said high speed bus payload to a CPU, extracting said samples from the high speed bus payload and storing said samples in a shared memory of the CPU, and accessing said samples from the shared memory for streaming to one or more client.

What is disclosed is a method for efficient capture and streaming of data packets in a network device comprises capturing data packets matching predetermined filters, packaging said data packets into samples, and aggregating one or more samples in a high speed bus payload. The method also comprises transferring said high speed bus payload to a CPU, extracting said samples from the high speed bus payload and storing said samples in a shared memory of the CPU, and accessing said samples from the shared memory for streaming to one or more client.

Systems and methods for monitoring user activity at a mobile device. A method comprises capturing one or more display frames associated with content being displayed by an application executing on of the mobile device; capturing textual information associated with the content being displayed by the application; detecting user input associated with the content being displayed by the application; and evaluating collected data including the captured display frames, the captured textual information, and the detected user input to identify an activity of interest or concern associated with the application.

Systems and methods are provided for a light-weight model for traffic classification within a network fabric. A classification model is deployed onto an edge switch within a network fabric, the model enabling traffic classification using a set of statistical features derived from packet length information extracted from the IP header for a plurality of data packets within a received traffic flow. The statistical features comprise a number of unique packet lengths, a minimum packet length, a maximum packet length, a mean packet length, a standard deviation of the packet length, a maximum run length, a minimum run length, a mean run length, and a standard deviation of run length. Based on the calculated values for the statistical features, the edge switch determines a traffic class for the received traffic flow and tags the traffic flow with an indication of the determined traffic class.

A technique includes receiving, from one or more sensors, sensor data samples; receiving radio network information data samples associated with a radio network; determining, based on an association of one or more received sensor data samples with one or more of the received radio network information data samples, a first set of one or more associated sensor and radio network information data samples; developing a model that is trained based on at least a portion of the first set the associated sensor and radio network information data samples that are relevant to performance of the radio network; and improving performance of the radio network based on at least the model.