A method and a system for providing one or more services to one or more user devices [202] in an IoT network in a scalable M2M (Machine to Machine) framework. The method comprises receiving a connection request from the one or more user devices [202] at a load balance of the IoT network, the connection request comprises at least a username comprising a cluster identifier. The load balancer [204] determines a cluster identifier based on the connection request and identifies at least one target cluster from the one or more clusters [206], said target cluster being associated with the identifier cluster identifier. The load balancer [204] routes the connection request to the at least one target cluster to provide the one or more services to the one or more user devices [202].

Methods, apparatus, systems, and articles of manufacture to monitor mobile Internet usage are disclosed. Example apparatus disclosed herein to monitor application usage associated with a device means for differentiating between foreground requests and background requests included in a burst of logged requests. Disclosed example apparatus further include means for calculating a presentation duration for a first application executed by the device based on at least a subset of the foreground requests identified in the burst of logged requests.

Methods, apparatus, systems, and articles of manufacture to monitor mobile Internet usage are disclosed. Example apparatus disclosed herein to monitor application usage associated with a device means for differentiating between foreground requests and background requests included in a burst of logged requests. Disclosed example apparatus further include means for calculating a presentation duration for a first application executed by the device based on at least a subset of the foreground requests identified in the burst of logged requests.

A method and system are provided for monitoring a protected network. The method includes, in a scoring phase, receiving a learned model having clusters of learning requests of learning network traffic observed during non-strain operation of the protected network, wherein each cluster has an associated characteristic learning response time. The method further includes receiving a score request to score a network service request of the network traffic, classifying the network service request with one of the clusters by comparing fields of the network service request to fields used for clustering the learning requests with the cluster, calculating a score based on the characteristic learning response times generated for the learned cluster to which the network service request is classified, and adjusting supportive handling of the network service request based on the score.

A method and system are provided for monitoring a protected network. The method includes, in a scoring phase, receiving a learned model having clusters of learning requests of learning network traffic observed during non-strain operation of the protected network, wherein each cluster has an associated characteristic learning response time. The method further includes receiving a score request to score a network service request of the network traffic, classifying the network service request with one of the clusters by comparing fields of the network service request to fields used for clustering the learning requests with the cluster, calculating a score based on the characteristic learning response times generated for the learned cluster to which the network service request is classified, and adjusting supportive handling of the network service request based on the score.

A method includes creating a package of an application, registering the package with an orchestrator, triggering instantiation of the application in the orchestrator, generating initial configuration files for the instantiation of the application, changing an application state of the application to a planned state, changing the application state to a Keycloak state in response to obtaining client identification of the Keycloak information for the application, changing the application state to an instantiated state in response to deploying the application on the set of target servers, changing the application state to a configured state in response applying daily configuration files to perform daily configuration of the application in the set of target servers, and changing the application state to a monitored state in response to monitoring the application in the set of target servers by an observability framework tool.

A method includes creating a package of an application, registering the package with an orchestrator, triggering instantiation of the application in the orchestrator, generating initial configuration files for the instantiation of the application, changing an application state of the application to a planned state, changing the application state to a Keycloak state in response to obtaining client identification of the Keycloak information for the application, changing the application state to an instantiated state in response to deploying the application on the set of target servers, changing the application state to a configured state in response applying daily configuration files to perform daily configuration of the application in the set of target servers, and changing the application state to a monitored state in response to monitoring the application in the set of target servers by an observability framework tool.

This application relates to a fog node scheduling method performed by a computer device, and a storage medium. The method includes: searching for candidate fog nodes storing a resource requested by a fog node scheduling request initiated by a client; performing effectiveness filtration on the candidate fog nodes to obtain effective fog nodes having predefined connectivity with the client; acquiring collected load information of the effective fog nodes; performing scheduling in the effective fog nodes based on the load information to obtain a scheduling result, where the scheduling result includes an identification of a target fog node obtained through scheduling and service flow allocated to the target fog node; and returning the scheduling result to the client so that the client can acquire the resource from the target fog node according to the identification and the service flow.

This application relates to a fog node scheduling method performed by a computer device, and a storage medium. The method includes: searching for candidate fog nodes storing a resource requested by a fog node scheduling request initiated by a client; performing effectiveness filtration on the candidate fog nodes to obtain effective fog nodes having predefined connectivity with the client; acquiring collected load information of the effective fog nodes; performing scheduling in the effective fog nodes based on the load information to obtain a scheduling result, where the scheduling result includes an identification of a target fog node obtained through scheduling and service flow allocated to the target fog node; and returning the scheduling result to the client so that the client can acquire the resource from the target fog node according to the identification and the service flow.

A wireless communication apparatus mounted on a mobile body includes a transmitter and a receiver. The transmitter is configured to transmit position information indicating a position to which the mobile body is scheduled to move, to a server apparatus. The receiver is configured to receive communication quality information indicating a communication quality corresponding to the position, from the server apparatus. The transmitter is configured to transmit the date at a communication position specified based on the communication quality information.