An object of the present disclosure is to provide a management apparatus, a communication apparatus, a system, a method, and a program capable of providing a service that meets a targeted KPI. A management apparatus according to the present disclosure includes: KPI management means for acquiring a target Key Performance Indicator (KPI) of a service that is provided to a communication terminal by a business operator; operation state acquisition means for acquiring element data regarding a component necessary for the service, the element data indicating a feature of a control system of the service; KPI prediction means for calculating a predicted KPI which is a predicted value of the KPI of the service based on the element data; and communication performance calculation means for, when the KPI is defined so that a value becomes lower as performance becomes better, detecting the component in which the predicted KPI is equal to or greater than the target KPI.

A data processing method and apparatus are disclosed. The data processing method includes: receiving, by a first edge computing node in an edge computing cluster, a container image update instruction; obtaining, by the first edge computing node, a to-be-updated container image; and sending, by the first edge computing node, the to-be-updated container image to another edge computing node in the edge computing cluster. The first edge computing node is an edge computing node in the edge computing cluster, the edge computing cluster includes at least two edge computing nodes, and the container image update instruction is used to instruct the at least two edge computing nodes in the edge computing cluster to update a container image. In this way, the time required for downloading the container image is reduced.

A data processing method and apparatus are disclosed. The data processing method includes: receiving, by a first edge computing node in an edge computing cluster, a container image update instruction; obtaining, by the first edge computing node, a to-be-updated container image; and sending, by the first edge computing node, the to-be-updated container image to another edge computing node in the edge computing cluster. The first edge computing node is an edge computing node in the edge computing cluster, the edge computing cluster includes at least two edge computing nodes, and the container image update instruction is used to instruct the at least two edge computing nodes in the edge computing cluster to update a container image. In this way, the time required for downloading the container image is reduced.

A method performed by a network computing device in a telecommunications network for adaptively deploying an aggregated machine learning model and an output parameter in the telecommunications network to control an operation in the telecommunications network. The network computing device can aggregate client machine learning models and an output performance metric the client machine learning models to obtain an aggregated machine learning model and an aggregated output performance metric. The network computing device can train a network machine learning model with the aggregated output performance metric and at least one measurement of a network parameter to obtain an output parameter. The network computing device can send to the client computing devices the aggregated machine learning model and the output parameter of the network machine learning model. A method performed by a client computing device is also provided.

A system (10) for monitoring and/or controlling one or more chemical plant(s) (12) including at least one processing layer (14, 16, 32, 34), wherein the at least one processing layer (14, 16, 32, 34) is associated with a secure network (20) and communicatively coupled to an interface (26) for providing process or asset specific data or process applications to an external processing layer (30), wherein the at least one processing layer (14, 16, 32, 34) is configured to add a transfer tag to the process or asset specific data or to the process application and to provide the process or asset specific data or the process application based on the transfer tag.

A device receives network data associated with a network that includes network devices interconnected by links at an Internet protocol (IP) layer and an optical layer of the network. The device receives constraints associated with determining a network plan for the network, where the constraints include a constraint indicating a particular time period associated with determining potential network plans for the network. The device identifies variables and values of the variables for the network plan based on the network data, and determines, within the particular time period, the potential network plans for the network based on the constraints and the values of the variables. The device identifies a potential network plan, of the potential network plans, that minimizes costs associated with operating the network, and causes the identified potential network plan to be implemented in the network by the network devices.

Techniques for automated incident triage and diagnosis are described. A method of automated incident triage and diagnosis may include receiving incident data associated with an incident, identifying one or more mitigation actions to resolve the incident using at least one machine learning model based at least on the incident data, and automatically executing the one or more mitigation actions to mitigate the incident.

Techniques for automated incident triage and diagnosis are described. A method of automated incident triage and diagnosis may include receiving incident data associated with an incident, identifying one or more mitigation actions to resolve the incident using at least one machine learning model based at least on the incident data, and automatically executing the one or more mitigation actions to mitigate the incident.

A computer-implemented method upgrades an edge network based on analysis by a learning model. The method includes identifying, in a network, a plurality of devices, where each device in the network is configured to provide data on at least one other device in the network. The method also includes determining capabilities of each device of the plurality of devices. The method further includes monitoring, for each device, capacity information and tasks performed during operation of the network. The method includes analyzing, based on the monitoring, each use of each device. The method also includes recommending, in response to the analyzing and by a learning model, a first upgrade to the network. The method further includes implementing the first upgrade.

A computer-implemented method upgrades an edge network based on analysis by a learning model. The method includes identifying, in a network, a plurality of devices, where each device in the network is configured to provide data on at least one other device in the network. The method also includes determining capabilities of each device of the plurality of devices. The method further includes monitoring, for each device, capacity information and tasks performed during operation of the network. The method includes analyzing, based on the monitoring, each use of each device. The method also includes recommending, in response to the analyzing and by a learning model, a first upgrade to the network. The method further includes implementing the first upgrade.