The present disclosure relates to the technical field of cloud computing, and to a cloud system migration method and device, and a hybrid cloud system. The method includes: dividing each service system in a first cloud system into a plurality of modules; determining the relation between a module to be migrated among the plurality of modules and other modules; copying said module among the plurality of modules to a second cloud system, the type of the first cloud system being different from that of the second cloud system; and establishing a relation in the second cloud system to complete the migration of the first cloud system.

The present disclosure relates to a communication method and 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). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. The support of a virtual network group according to an embodiment of the present disclosure enables efficient communication of a terminal by using an external network, configured via an existing short-range communication network, or a private network between terminals based on 5G. Accordingly, a network operator or an entity in charge thereof enables terminals to interwork with equipment through communication via a virtual private network without changes in an existing communication method or a network structure, or without adding additional equipment.

The present disclosure relates to a communication method and 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). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. The support of a virtual network group according to an embodiment of the present disclosure enables efficient communication of a terminal by using an external network, configured via an existing short-range communication network, or a private network between terminals based on 5G. Accordingly, a network operator or an entity in charge thereof enables terminals to interwork with equipment through communication via a virtual private network without changes in an existing communication method or a network structure, or without adding additional equipment.

The present application relates to traffic routing for overlay paths in a public cloud network. A path orchestrator receives a configuration of a set of overlay paths for a wide area network virtualization from a client, each overlay path including virtual routing nodes associated with respective geographic regions and at least one policy for a link between the virtual routing nodes. The path orchestrator is configured to instantiate a plurality of virtual routers on computing resources of the public cloud network located within the respective geographic regions based on the configuration, each virtual router configured to route traffic according to the policy for each link associated with the virtual routing node corresponding to the virtual router. The path orchestrator is configured to scale the plurality of virtual routers based on traffic for the client on the set of overlay paths.

A computer-readable medium contains cybersecurity configuration settings (CCS) generating file(s) including instructions when executed cause a processor of a computer located at a node in a networked system having computers including at least one computer system class to generate CCS. The CCS generating file includes group policy objects (GPOs) applicable to all computers, policy setting scripts that are applicable to <all the computer s, and group policy definition files which provide a policy setting library for the computer class. Execution of the CCS generating file at the node automatically generates the CCS for cybersecurity protection of the node. The computer class can include computer classes that include ≥2 different operating systems, and there can be a CCS generating file for each computer class. The CCS generating file can be a single multi-class CCS generating file that includes a plurality of CCS generating files.

A computer-readable medium contains cybersecurity configuration settings (CCS) generating file(s) including instructions when executed cause a processor of a computer located at a node in a networked system having computers including at least one computer system class to generate CCS. The CCS generating file includes group policy objects (GPOs) applicable to all computers, policy setting scripts that are applicable to <all the computer s, and group policy definition files which provide a policy setting library for the computer class. Execution of the CCS generating file at the node automatically generates the CCS for cybersecurity protection of the node. The computer class can include computer classes that include ≥2 different operating systems, and there can be a CCS generating file for each computer class. The CCS generating file can be a single multi-class CCS generating file that includes a plurality of CCS generating files.

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.

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.

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.