The invention relates to a method for diagnosing a fieldbus type network. The method comprises the steps of measuring, using a signal measuring device such as an oscilloscope, a bus signal of the fieldbus type network, providing the measured bus signal to a computer system, and generating, by the computer system, a diagnosis. The diagnosis is performed by executing a step of comparing, by the computer system, the measured bus signal with signals in a database of bus signals and corresponding diagnoses; and/or feeding, by the computer system, the measured bus signal to a trained statistical model trained to diagnose the fieldbus type network; as well as a step of outputting the diagnosis based on the output of the comparison and/or the output of the statistical model.

In the present disclosure, a plurality of types of data are received from a base station and these pieces of data are learned, whereby a first model capable of calculating an estimated value of an index indicating an operation status of the base station is generated. Then this estimated value is compared to a measured value of the index indicating the operation status of the base station.

Described herein are systems and methods for fault tolerance in a network cloud environment. In accordance with various embodiments, the present disclosure provides an improved fault tolerance solution, and improvement in the fault tolerance of systems, by way of failure prediction, or prediction of when an underlying infrastructure will fail, and using the predictions to counteract the failure by spinning up or otherwise providing new component pieces to compensate for the failure.

A method of classifying traffic data in a network comprises at a Network Data Analytics Function (NWDAF), receiving information relating to traffic data with a known classification from one or more first network functions, and developing a model for classifying future traffic data based on the information relating to the traffic data with a known classification. The method also involves at a second network function, storing a representation of the developed model. The method also involves at a third network function, receiving the representation of the developed model from the second network function, and installing the representation of the developed model at a fourth network function. The method also involves at the fourth network function, classifying traffic data using the developed model.

A method of classifying traffic data in a network comprises at a Network Data Analytics Function (NWDAF), receiving information relating to traffic data with a known classification from one or more first network functions, and developing a model for classifying future traffic data based on the information relating to the traffic data with a known classification. The method also involves at a second network function, storing a representation of the developed model. The method also involves at a third network function, receiving the representation of the developed model from the second network function, and installing the representation of the developed model at a fourth network function. The method also involves at the fourth network function, classifying traffic data using the developed model.

A failure effects estimating device includes an input unit that inputs a log and a traffic amount obtained from a communication system when an abnormality occurs, an estimating unit that estimates a failure effects amount in the communication system, on the basis of the log and the traffic amount, and an output unit that outputs the failure effects amount estimated by the estimating unit.

A system, process, and computer-readable medium for configuring agents for monitoring deployed applications is described. A system, process, and computer-readable medium for configuring monitoring user interfaces, e.g., monitoring dashboards, that use information made available from the agents is also described. Through using application data available during creation of the agents, the agents may be configured using the user interface as modified by selections and displaying subsequent choices from the received application data. Using knowledge of the generated agents, monitoring dashboards may be generated via developers interacting with a user interface providing a list of available metrics accessible by the generated agents. Using the one or more user interfaces, developers may generate agents and/or monitoring dashboards with greater efficiency.

An example method includes recording, by a node out of a plurality of nodes, occurrence of one or more baseline node events, generating a statistical data corresponding to a recorded occurrence of the one or more baseline node events over a pre-determined period, comparing one or more subsequent node events with the statistical data, and communicating data corresponding to the one or more subsequent node events to the central control device, in response to determining that the one or more subsequent node events satisfy the event deviation threshold.

This disclosure describes systems, methods, and devices related to anomaly detection of CPE firmware revisions. A method may include collecting metrics data for a plurality of customer-provided equipment (CPE) models over a window of time; training a first autoencoder for a first CPE model of the plurality of CPE models using at least a portion of the metrics data to detect anomalies within a plurality of firmware versions of the first CPE model; identifying, using the first autoencoder, that a first firmware version of the plurality of firmware versions is anomalous across a first time series; and storing data indicating that the first firmware version of the plurality of firmware versions is anomalous across the first time series. Metrics data may include one or more of interactive voice response (IVR) session data; calls handled data; and truck schedule data.

An example method for discovering and grouping application endpoints in a network environment is provided and includes discovering endpoints communicating in a network environment, calculating affinity between the discovered endpoints, and grouping the endpoints into separate endpoint groups (EPGs) according to the calculated affinity, each EPG comprising a logical grouping of similar endpoints for applying common forwarding and policy logic according to logical application boundaries. In specific embodiments, the affinity includes a weighted average of network affinity, compute affinity and user specified affinity.