A fault root cause analysis method and apparatus are provided. The method includes: obtaining a first alarm event set, where the first alarm event set includes a plurality of alarm events; for a first alarm event in the first alarm event set, extracting a feature vector of the first alarm event, where a part of or all features of the feature vector are used to represent a relationship between the first alarm event and another alarm event in the first alarm event set; and determining, based on the feature vector of the first alarm event, whether the first alarm event is a root cause alarm event. In this application, whether the first alarm event is the root cause alarm event is determined based on a feature vector of the relationship between the first alarm event and the another alarm event, and the accuracy of fault root cause identification is improved.

Systems, devices, media, and methods are presented for splitting decision trees between server and client. The client of the systems and methods sends a configuration query. The server of the system and method receives the configuration query. The server retrieves Config rule(s) according to the configuration query. Each of the Config rule(s) can be represented by decision tree(s). The server evaluates the decision tree(s). If a definitive True or False cannot be derived from the evaluation using server knowledge, the server prunes the decision tree(s) and returns them to client side for further evaluation.

Systems, devices, media, and methods are presented for splitting decision trees between server and client. The client of the systems and methods sends a configuration query. The server of the system and method receives the configuration query. The server retrieves Config rule(s) according to the configuration query. Each of the Config rule(s) can be represented by decision tree(s). The server evaluates the decision tree(s). If a definitive True or False cannot be derived from the evaluation using server knowledge, the server prunes the decision tree(s) and returns them to client side for further evaluation.

A device may receive alarm data identifying alarms associated with an occurrence of an event, and may identify, from the alarm data, a set of alarms that include false positives. The device may perform feature engineering on the set of alarms to extract features from a feature store and may train a model with the features to generate a trained model. The device may process the alarm data, with the trained model, to determine rules for reducing a quantity of future alarms that include the false positives, and may identify, from the rules for reducing the quantity of future alarms, a set of rules that satisfy a threshold for reducing the quantity of future alarms. The device may perform one or more actions based on the set of rules.

A method of measuring video stream visual stability, the method including receiving a first set of network packets carrying data of the video stream, determining network performance metrics for a session associated with the first set of network packets, retrieving priority fault errors from a packet header of at least one network packet of the first set of the network packets, adding the priority fault errors and the network performance metrics to time series data, and applying a machine learning model to the time series data to obtain a visual stability score for the first set of network packets.

The present invention is directed to communication systems. According to a specific embodiment, the present invention provides a network device that collects telemetry measurements related to the quality of data communication. A machine learning algorithm generates probability determinations using the telemetry measurements and an inference model. The probability determinations are used to generate alarms signals or activate repair algorithms. There are other embodiments as well.

Proactively detecting one or more handover error in a communication link between base stations [14y, 14z] in a wireless communication network. A method may include determining at least one possible solution to the one or more handover error using an artificial intelligence engine 76. A method may include implementing the at least one possible solution to correct the one or more handover error. The artificial intelligence engine 76 may include an artificial intelligence algorithm, a machine learning algorithm, a deep learning algorithm, a neural network algorithm; and/or big data analysis algorithm. The artificial intelligence engine 76 may utilize at least one database [92, 94, 96] including a plurality of solutions. The plurality of solutions may include solutions to errors in communication networks that occurred in the past. The artificial intelligence engine 76 may successively implement each of the possible solutions until the one ore more handover error is corrected.

Systems and methods for analyzing root-causes of Wi-Fi issues in a Wi-Fi system associated with a Local Area Network (LAN) are described in the present disclosure. A method, according to one embodiment, includes a step of monitoring a Wi-Fi system associated with a LAN to detect authentication failures in the Wi-Fi system. In response to detecting an authentication failure in the Wi-Fi system, the method also includes the step of analyzing the authentication failure to determine one or more root-causes of the authentication failure. The method also includes pushing changes to the Wi-Fi system to automatically remediate the one or more root-causes in the Wi-Fi system.

The disclosure describes techniques for network monitoring and fault localization. For example, a controller comprises one or more processors operably coupled to a memory configured to: receive a first one or more Quality of Experience (QoE) metrics measured by a first probe traversing a first path comprising one or more links; receive a second one or more QoE metrics measured by a second probe traversing a second path comprising one or more links; determine, from the first one or more QoE metrics, that the first path has an anomaly; determine, from the second one or more QoE metrics, that the second path has an anomaly; and determine, in response to determining the first path and the second path has an anomaly, based on the type of metrics and the type of links, that an intersection between the first path and the second path is a root cause of the anomaly.

Systems and methods are described herein for logging system events within an electronic machine using an event log structured as a collection of tree-like cause and effect graphs. An event to be logged may be received. A new event node may be created within the event log for the received event. One or more existing event nodes within the event log may be identified as having possibly caused the received event. One or more causal links may be created within the event log between the new event node and the one or more identified existing event nodes. The new event node may be stored as an unattached root node in response to not identifying an existing event node that may have caused the received event.