Embodiments of this application disclose a traffic monitoring method and apparatus, an integrated circuit, and a network device. When the traffic monitoring apparatus receives a packet, after determining that the traffic monitoring apparatus includes an empty first register, the traffic monitoring apparatus updates a value of first information in the first register to a measured value of a target performance indicator of the packet, and increases a value of second information in the first register by 1. The value of the second information in the first register is 0, the first information in the first register indicates a depth of a data bucket that carries a measured value of the target performance indicator of a to-be-monitored packet, and the second information in the first register indicates a quantity of packets that are in received packets and that match the value of the first information in the first register.

Techniques for monitoring network connectivity using machine learning are provided. A plurality of historical connectivity records is received, and a first machine learning model type, of a plurality of machine learning model types, is selected based on the plurality of historical connectivity records. A machine learning model, of the first machine learning model type, is trained based on the plurality of historical connectivity records, where the machine learning model learns to generate forecasted connectivity records based on the training.

There is provided a method and system for communication network management. There is provided an active TE architecture and procedure that rely on the epistemic uncertainty obtained from traffic forecasting models. According to embodiments, the traffic forecasting models can predict the mean of the network traffic demand and can extract one or more of the features relating epistemic uncertainty and the aleatoric uncertainty. According to embodiments, the epistemic uncertainty is used to vary the sampling frequency of network statistics in TE applications, for specific times or specific flows. A time-window can be used to predict network traffic can be varied (e.g. increased or decreased) to adjust the epistemic uncertainty.

A computer system for evaluating the communication performance of an autonomous vehicle is provided. The vehicle may have a vehicle controller including at least one processor in communication with at least one memory device. The processor may be programmed to receive, from a standard data transmission location network device, an evaluation data packet. The processor may be programmed to decode the evaluation data packet and initiate a diagnostic test of the vehicle based upon the decoded evaluation data packet. The processor may also be programmed to record measurements of the vehicle during the diagnostic test, and transmit the measurements to the standard data transmission location network device.

A system, method, and computer program product are provided for a determining a network situation in a communication network. In use, at least one threshold value of at least one operational parameter of a communication network is obtained, the at least one operational parameter representing at least one operational status of at least one of a computational device or a communication device. Additionally, log data of the communication network is obtained, the log data containing at least one value of the at least one operational parameter reported by at least one network entity of the communication network. The at least one value of the at least one operational parameter of the log data is compared with a corresponding threshold value of the at least one threshold value to form a detection of a network situation. Further, the detection of the network situation is reported if the at least one value of the at least one operational parameter of the log data traverses the corresponding threshold value of the at least one threshold value.

A compact data structure generation engine can be used to generate a compact data structure that represents performance data for high-scale networks. The compact data structure representing the performance data can be used to monitor the operation performed on or by a computer system to identify potentially anomalous conditions. In response, a corrective action can be taken to address the issue. This can be useful, for example, in improving the efficiency, effectiveness, and reliability of the computer system during operation.

A compact data structure generation engine can be used to generate a compact data structure that represents performance data for high-scale networks. The compact data structure representing the performance data can be used to monitor the operation performed on or by a computer system to identify potentially anomalous conditions. In response, a corrective action can be taken to address the issue. This can be useful, for example, in improving the efficiency, effectiveness, and reliability of the computer system during operation.

The claimed system and method describes a root cause analysis system for a radio access network. Some aspects include automatic identification of possible causes for network issues, their ranking, determination of the root (main) cause and execution of related best actions, alerts and reporting in order to automatically identify, mitigate or eliminate the problem.

Provided herein are systems and methods for determining relationships between events occurring in networks. Notifications describing events occurring in networks can be received and processed to determine groups of network event types. A root-cause network can be generated based on the events, with the nodes of the root-cause network representing different event types and the edges of the root-cause network indicating directional, causal relationships between the nodes. A received network event can be processed to determine potential causes of the received network event based on the root-cause network and other events received by the network.

Techniques for Qprints using telemetry-based similarity for DNS are provided. In some embodiments, a system/process/computer program product for Qprints using telemetry-based similarity for DNS in accordance with some embodiments includes aggregating a set of network related event data, wherein the set of network related event data includes Domain Name System (DNS) related query data; clustering the DNS related query data; and generating similarity clusters for domains based on their DNS related query data. For example, the set of network related event data can include passive DNS (pDNS) data aggregated over a period of time to express pDNS data at-scale, and similarity of the pDNS data aggregated over the period of time is quantified, within and across networks based on telemetry-based similarity for DNS using a statistical model.