The disclosure provides a method and a device for efficiently operating network slicing. According to the disclosure, a method of operating a first node configured to manage a network slice of a communication system includes: transmitting a service level agreement (SLA) range for each network slice subnet and a message requesting a resource according to the SLA range to a second node configured to manage the network slice subnet, receiving SLA arrangement flavor mapping relationship information in the network slice subnet unit from the second node, and identifying the SLA arrangement flavor mapping relationship in a network slice unit based on the received SLA arrangement flavor mapping relationship information in a network slice subnet unit.

A map file includes two-dimensional or three-dimensional geometric data items collectively representing layout of a building. The map file is parsed and the geometric data items are analyzed to identify building elements including rooms, floors, and objects of the building, and to identify containment relationships between the elements. A space model having a space graph is constructed. The space graph includes nodes that correspond to the respective building elements and links forming relationships between nodes that correspond to the identified containment relationships. Each node may include node metadata, rules or code that operate on the metadata, and a node type that corresponds to a type of physical space. Some nodes may include user representations or device representations that represent physical sensors associated therewith. The representations may receive data from the respectively represented sensors, and the sensor data becomes available via the space model.

Systems, methods, and computer-readable media analyzing memory usage in a network node. A network assurance appliance may be configured to query a node in the network fabric for a number of hardware level entries, stored in memory for the node, that are associated with a concrete level network rule. The network assurance appliance may identify a logical level network intent associated with the concrete level network rule, identify a logical level component of the logical level network intent, and attribute the number of hardware level entries to the logical level component.

Disclosed is a 5.sup.th generation (5G) or a pre-5G communication system provided to support a higher data transmission rate than that of post-4.sup.th generation (4G) communication systems, such as long term evolution (LTE). A method of operating a network node in a wireless communication system is provided. The method includes receiving, from a plurality of first network nodes, network data, generating first recommendation operation information for a second network node based on the network data, and transmitting, to the second network node, a first analysis result message including the first recommendation operation information.

A control method includes sending, by a controller, a created context-aware model to a context-aware engine. The context-aware model is used to define a preset control performed when target data meets a trigger condition and to instruct the context-aware engine to send indication information to the controller when the context-aware engine determines that the target data meets the trigger condition. The preset control is used to implement a context-aware function. The indication information is used to indicate that the target data meets the trigger condition. The method also includes receiving, by the controller, the indication information. The method further includes performing, by the controller, the preset control based on the indication information.

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 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.

Identifying Internet of Things (IoT) devices with packet flow behavior including by using machine learning models is disclosed. Information associated with a network communication of an IoT device is received. A determination of whether the IoT device has previously been classified is made. In response to determining that the IoT device has not previously been classified, a determination is made that a probability match for the IoT device against a behavior signature exceeds a threshold. Based at least in part on the probability match, a classification of the IoT device is provided to a security appliance configured to apply a policy to the IoT device.

Approaches presented herein enable deploying a distributed machine learning framework in an edge computing environment. More specifically, a status of a connection between a computing system and an edge node of a plurality of edge nodes is monitored. At least one server node and a group of worker nodes from the plurality of edge nodes are identified based on the status. A path for distributing the training data to the worker nodes is determined based on the status. The training data from the edge node to the worker nodes is distributed via the path.