Embodiments include methods for managed machine learning (ML) in a communication network, such as by one or more first network functions (NFs) of the communication network. Such methods include determining whether processing of an ML model in the communication network should be distributed to one or more user equipment (UEs) operating in the communication network, based on characteristics of the respective UEs. Such methods also include, based on determining that the processing of the ML model should be distributed to the one or more UEs, establishing trusted execution environments (TEEs) in the respective UEs and distributing the ML model for processing in the respective TEEs. Other embodiments include complementary methods for UEs, as well as UEs and NFs (or communication networks) configured to perform such methods.

A data gateway system and a data intercommunication method are provided. The data gateway system includes a client system and a cloud server. The client system includes a first connector module and a listener module. The cloud server includes a second connector module and an authentication management module. The listener module performs a command listening for the cloud server. When the listener module obtains a connection configuration information, the client system sends a connection request command to the cloud server through the first connector module, so that the cloud server receives the connection request command through the second connector module, and issues a gateway code. The cloud server sends the gateway code to the first connector module of the client system through the second connector module, so that the client system establishes a connection between the client system and the cloud server based on the gateway code.

Embodiments include methods for managed machine learning (ML) in a communication network, such as by one or more first network functions (NFs) of the communication network. Such methods include determining whether processing of an ML model in the communication network should be distributed to one or more user equipment (UEs) operating in the communication network, based on characteristics of the respective UEs. Such methods also include, based on determining that the processing of the ML model should be distributed to the one or more UEs, establishing trusted execution environments (TEEs) in the respective UEs and distributing the ML model for processing in the respective TEEs. Other embodiments include complementary methods for UEs, as well as UEs and NFs (or communication networks) configured to perform such methods.

An example embodiment includes transmitting first connection information that the user device is connected to a plurality of servers to a first server, and receiving a list of candidate servers from the first server, the candidate servers being determined by the first server at least based on the first connection information. The embodiment further includes selecting a second server from the list to establish a connection.

Described embodiments provide systems and methods for measuring user experience with virtual or hosted desktops or applications, with scores calculated based on weights determined during a supervisory learning process. The scores are multivariate across a number of factors that affect user experience, enabling administrators to easily and efficiently identify trends and degradations or improvements to a system. This allows the administrator to take mitigating actions, fully implement temporary adjustments, or perform other such functions to improve the working of the system based on the real-time measurement and analysis of user experience.

Systems and methods are provided for a network appliance comprising a plurality of virtual private network nodes operating on the network appliance, each virtual private network node being configurable to connect to selectable virtual private network end points in an on-demand computing network. A web interface is configured to connect a client device to the network appliance and to identify a selected virtual private network end point, where the client device is connected to a particular one of the virtual private network nodes and the particular virtual private network node is connected to the selected virtual private network end point based on interactions with the web interface. The on-demand computing network includes a first provisioned resource assigned as a hub device; and one or more second provisioned resources assigned as rim devices, where a particular rim device comprises a bridge device, wherein the bridge device repackages data received from the on-demand computing network prior to forwarding that data such that the data received from the on-demand computing network appears to terminate at the bridge device to an observer viewing the data between the hub device and the bridge device.

In a source computing system having a source management infrastructure, at least one source infrastructure management component is discovered. A description of a target cloud infrastructure having a target management infrastructure is obtained. The description includes at least one mandatory target infrastructure management component. The at least one source infrastructure management component is analyzed to determine whether at least one conflict exists with the at least one mandatory target infrastructure management component.

Various embodiments provide a system for modifying a channel binding in order to relay packets between a relay client and a peer in a peer-to-peer (P2P) communication event across a network. A relay server receives a request to bind a channel in order to relay the packets for the communication event. The relay server creates requirements for a communication path. The relay server sends the requirements to a Software Defined Networking (SDN) controller. The SDN controller in turn creates and installs flows and flow tables in SDN switches to relay the packets across the network for the communication event.

Techniques for committing back end computing resources to an online stream of requests for data from client devices are described herein. A polling schedule server (e.g., a reservation management system), may receive polling reservation requests from a plurality of client devices, may evaluate each client device's need for “fresh” data based on a number of input signals, and may assign the client device a polling slot (e.g., a reservation for a future polling time). The polling scheduler server may subsequently receive a polling request from a client device and, upon validating a token received from the client device as well as a difference between an assigned polling time and the polling request timestamp, may grant the polling request by transmitting a request to one or more communication system servers, receiving data from the communication system servers, and providing the data to the client device.

Examples of device-driven management are described. A management service can transmit a device-driven management workflow to a number of client devices. The device-driven management workflow can include workflow objects that define a branching sequence of instructions. The client devices can provide a corresponding plurality of completion statuses for a step of the device-driven management workflow. The management service can identify a failure of the step according to a set of failure rules, and visually emphasize the failure within a representation of the device-driven management workflow.