Systems and methods for updating a configuration of an SD-WAN application using connectivity or usage data include a software-defined wide area network (SD-WAN) application executing on a client device receiving one or more of historic or predictive data relating to connectivity or usage of the client device. The SD-WAN application detects an update condition for the SD-WAN application using the one or more historic or predictive data relating to the connectivity or usage of the client device. The SD-WAN application updates a configuration of the SD-WAN application responsive to detecting the update condition. The SD-WAN application transmits application traffic using the updated configuration.

In one embodiment, a network assurance service that monitors a wireless network identifies a set of wireless network anomalies detected in the wireless network that are associated with a set of one or more network measurements. The network assurance service classifies the set of wireless anomalies as radio-related or backend-related. The network assurance service, when the set of wireless anomalies are classified as radio-related, determines that the wireless anomalies are recurring for a particular wireless access point in the network. The network assurance service initiates a change to the wireless network in part to move clients in the wireless network from the particular wireless access point to another wireless access point in the network.

Systems and methods for managing network resources are disclosed. One method can comprise receiving first information relating to network traffic parameters and receiving second information relating to one or more contextual events having an effect on the network traffic parameters. The first information and the second information and be correlated. And one or more network resources can be allocated based on the correlation of the first information and the second information.

In one embodiment, a computing system may receive a request for an optimization recommendation of a geographic area of interest covered by a communication network. The computing system may determine a network traffic trend associated with the geographic area of interest based on a current number of data samples that may be aggregated into a plurality of data points. The computing system may predict a value of a number of data samples for a future time associated with the geographic area of interest, based on the determined network traffic trend and the current number of data samples. The computing system may predict, based on the determined network traffic trend and the predicted value of the number of data samples at the future time, network traffic associated with the geographic area of interest at the future time, and send instructions for presenting the optimization recommendation based on the predicted network traffic.

A communication method and a system for converging a 5.sup.th-Generation (5G) communication system for supporting higher data rates beyond a 4.sup.th-Generation (4G) system with a technology for Internet of Things (IoT) is provided. The disclosure is applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as a smart home, a smart building, a smart city, a smart car, a connected car, health care, digital education, a smart retail, security and safety services. A method performed by a first entity performing a network data analytics function (NWDAF) is provided. The method includes receiving, from a second entity performing network function (NF), a first message for requesting observed service experience analytics, the first message including single-network slice selection assistance information (S-NSSAI) indicating a network slice, transmitting, to a third entity performing application function (AF) associated with the S-NSSAI, a second message for requesting service data associated with the observed service experience analytics, the second message including information on at least one application, receiving, from the third entity, the service data including at least one service experience for the at least one application, identifying the observed service experience analytics based on the service data, and transmitting, to the second entity, the observed service experience analytics.

This disclosure describes techniques that include determining the health of one or more routing engines included within a router. In one example, this disclosure describes a method that includes performing, by a first routing engine included within a router, routing operations, wherein the router includes a plurality of routing engines, including the first routing engine and a second routing engine; receiving, by a computing system, data including health indicators associated with the first routing engine; applying, by the computing system, a machine learning model to the data to determine, from the health indicators, a health status of the first routing engine, wherein the machine learning model has been trained to identify the health status from the health indicators; and determining, by the computing system and based on the health status of the first routing engine, whether to switch routing operations to the second routing engine from the first routing engine.

A method for automated web resource deployment is provided. The method comprises creating web resource publication requests, wherein each web resource publication request comprises a number of configuration changes necessary to publish a web resource, on a network, at a particular uniform resource location. A standard format, validation workflow, and an approval workflow are provided for automation of the web resource publication requests. Once validated and approved, web resource publication requests are automatically converted to API calls which are executed on backend servers to implement the configuration changes required in the environment without further human intervention.

A system for troubleshooting network problems is disclosed. A model can use demographic information, network usage information, and network membership information to determine an importance of a problem. The importance of the problem for the user who reported the problem, a number of other users affected by the problem, and the importance of the problem to the other users can be used to determine a priority for resolving the problem. Before and after a work order is executed to resolve the problem, network metrics can be gathered, including aggregate network metrics, and automatically presented in various user interfaces. The analysis of the metrics can be used to update a database of which work orders are assigned in response to which problems.

A remote network management platform may include persistent storage containing: (i) data related to a managed network, and (ii) a persona of a user. The remote network management platform may also include a platform application associated with a web-based user interface and using a portion of the data. The remote network management platform may also include a recommendation engine with access to a set of rules or a machine learning (ML) model corresponding to the platform application. The recommendation engine may be configured to: (i) read, from the persistent storage, the portion of the data and the persona; (ii) apply, to the portion of the data and the persona, the set of rules or the ML model to generate one or more recommendations; and (iii) transmit, by way of the web-based user interface and to the user, representations of the one or more recommendations.

It is an objective of the present disclosure to reduce computational complexity for obtaining a network topology configuration capable of efficiently accommodating a plurality of traffic demands assumed between nodes present in a network.

A device of the present disclosure includes: a computation traffic generation unit that creates a traffic demand matrix whose elements are values individually obtained by demands between nodes present in a target network area from a plurality of traffic demand matrices created based on predicted traffic demands between the nodes; and a network topology generation unit that generates a network topology based on the traffic demand matrix generated by the computation traffic generation unit and port information of the nodes in the network area.