A device, comprising a packet data interface port; a microcontroller, configured to control the packet data interface port, receive a input control signal through the packet data interface port, transmit a status report through the packet data interface port, and in dependence on the input control signal, produce an output control signal; and a radio frequency modification device, configured to modify a received radio frequency signal over a range selectively in dependence on the output control signal. A control processor, communicating through the packet data interface port with the microcontroller, may generate a plurality of the input control signals for a plurality of respective devices comprising the microcontroller and the radio frequency signal control device. The input control signals may be dynamically changed over time to emulate radio frequency conditions resulting from mobility of nodes in a mobile ad hoc radio frequency communication network.

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.

Optimum software update transmission parameters are determined and used for transmitting a software update from a host to servers of a computer network. The software update is transmitted while the servers are live and required to meet certain quality of service requirements for tenants of the computer network. Transmission parameters for transmitting the software update are adjusted and updated based on service performance data. Based on iterative adjustments, optimum transmission parameters may be determined. Additionally or alternatively, machine learning is used to generate a model that determines predicted optimum transmission parameters. The predicted optimum transmission parameters may be initially used for transmitting a software update, while the transmission parameters continue to be adjusted throughout transmission.

A novel design of a gateway that handles traffic in and out of a network by using a datapath pipeline is provided. The datapath pipeline includes multiple stages for performing various data-plane packet-processing operations at the edge of the network. The processing stages include centralized routing stages and distributed routing stages. The processing stages can include service-providing stages such as NAT and firewall. The gateway caches the result previous packet operations and reapplies the result to subsequent packets that meet certain criteria. For packets that do not have applicable or valid result from previous packet processing operations, the gateway datapath daemon executes the pipelined packet processing stages and records a set of data from each stage of the pipeline and synthesizes those data into a cache entry for subsequent packets.

Techniques for user behavior anomaly detection. At least one low-variance characteristic is compared to an expected result for the corresponding low-variance characteristics to determine if the low-variance characteristic(s) is/are within a pre-selected range of the expected results. A security response action is taken in response to the low-variance characteristic not being within the first pre-selected range of the expected results. At least one high-variance characteristic is compared to an expected result for the corresponding high-variance characteristics to determine if the high-variance characteristic(s) is/are within a pre-selected range of the expected results. A security response action is taken in response to the high-variance characteristic not being within the first pre-selected range of the expected results. Access is provided if the low-variance and the high-variance characteristics are within the respective expected ranges.

A device-communication system may receive, from a user device via a first network, communication data originating from a first device connected to the user device via a second network having a type different from that of the first network. The device-communication system may process the communication data to determine a corresponding device-management system, and may communicate further with the user device for additional identification information, if necessary. The device-communication system determines which of a plurality of device-management systems should receive the communication data and sends the data to the appropriate system.

In an example, a system specifies a first configuration of the physical transport network that models a plurality of devices as a corresponding first plurality of nodes having a tree topology. Each node of the first plurality of nodes has at least one first device identifier and at least one first connection identifier to other nodes in the tree topology. The system specifies a second configuration of the logical transport network that models the plurality of devices as the first plurality of nodes having a non-tree topology. Each node of the first plurality of nodes has at least one second device identifier, at least one second connection identifier to other nodes in the non-tree topology, the at least one first device identifier, and the at least one first connection identifier of the tree topology. The system folds the logical transport network over the physical transport network using the at least one second device identifier, at least one second connection identifier to other nodes in the non-tree topology, the at least one first device identifier, and the at least one first connection identifier of the tree topology.

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.

A fault recovery method and apparatus, and a storage medium are provided, and belong to the field of Internet technologies. In the method, network composition information and abnormal event information of a target network are obtained, where the network composition information includes a network topology of the target network and device information of a plurality of network devices on the target network, and the device information includes one or more of interface configuration information, protocol configuration information, and service configuration information; and then a possible root cause of a fault of the target network is determined based on the network composition information and the abnormal event information, where the possible root cause of the fault is used to determine a corresponding fault recovery plan.