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.

The present invention discloses a network burst load evacuation method for edge servers, which takes a time and average penalty function of all tasks performed by the edge system as a minimum optimization goal. This method not only takes into account the fairness of all users in the system, but also ensures that the unloading tasks of all users in the system can be completed in a relatively shortest time, and a new quantitative measure is proposed for improving user QoS response. In the implementation process of the algorithm in the present invention, a particle swarm algorithm is used to solve an optimal target of the system, This algorithm has a fast execution speed and high efficiency, and is especially suitable for a scene of an edge computing network system, so that when a sudden load occurs, an edge computing network system can respond in a very short time and complete the evacuation of the load, which greatly improves the fault tolerance and stability of the edge network environment.

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.

In one embodiment, an issue analysis service obtains telemetry data from a plurality of devices in a network across a plurality of time intervals. The service detects a failure event in which a device in the network is in a failure state. The service clusters the telemetry data obtained prior to the failure event into rounds according to time intervals in which the telemetry data was collected. Each round corresponds to a particular time interval. The service applies a machine learning-based classifier to each one of the rounds of clustered telemetry data to identify one or more common traits appearing in the telemetry data for each round. The service generates a trait change report indicating a change in the one or more common traits appearing in the telemetry data across the rounds leading up to the failure event.

Failure impact analysis (or “impact analysis”) is a process that involves identifying effects of a network event that are may or will results from the network event. In one example, this disclosure describes a method that includes generating, by a control system managing a resource group, a resource graph that models resource and event dependencies between a plurality of resources within the resource group; detecting, by the control system, a first event affecting a first resource of the plurality of resources, wherein the first event is a network event; and identifying, by the control system and based on the dependencies modeled by the resource graph, a second resource that is expected to be affected by the first event.

Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.

Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.

Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.

A system and method for the aggregation and grouping of previously identified, causally related abnormal operating condition, that are observed in a monitored environment, is disclosed. Agents are deployed to the monitored environment which capture data describing structural aspects of the monitored environment, as well as data describing activities performed on it, like the execution of distributed transactions. The data describing structural aspects is aggregated into a topology model which describes individual components of the monitored environments, their communication activities and resource dependencies and which also identifies and groups components that serve the same purpose, like e.g. processes executing the same code. Activity related monitoring data is constantly monitored to identify abnormal operating conditions. Data describing abnormal operating condition is analyzed in combination with topology data to identify networks of causally related abnormal operating conditions. Causally related abnormal operating conditions are then grouped using known topological resource and same purpose dependencies. Identified groups are analyzed to determine their root cause relevance.

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.