Abstract: Consistency in data that are replicated across a group of management appliances is preserved even when one of the management appliances is reverted to a snapshot thereof. A method of preserving consistence in such data includes: detecting that a first management appliance in the group has reverted to a snapshot thereof; and in response to such detecting, updating a desired state document for the group to remove data items in the desired state document that have been designated as local to the first management appliance, and instructing each of the management appliances in the group to apply the desired state document.

Abstract: Inventory data of software-defined data centers (SDDCs) are replicated across a group of management appliances of the SDDCs according to a desired state of the inventory data. The method of replication includes the steps of: comparing a timestamp of a first change item, which contains a change to a first item of the desired state, against a timestamp of the first item of the desired state; determining that the timestamp of the first change item has a time value that is after a time value of the timestamp of the first item; updating the first item of the desired state by applying the change to the first item included in the first change item; and issuing an instruction to one or more of the management appliances in the group to apply the desired state with the updated first item.

Abstract: Aspects of the subject disclosure may include, for example, a device comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising: obtaining a list of a plurality of known cell sites, the list comprising for each known cell site of the plurality of known cell sites a respective set of known geospatial coordinates; obtaining for each set of known geospatial coordinates, a corresponding aerial image; applying each corresponding aerial image to an automated process to generate a model, the model being usable to determine whether a particular test aerial image depicts a cell site; obtaining from a database an identification of an asserted cell site, the identification of the asserted cell site comprising a set of asserted geospatial coordinates; obtaining for the set of asserted geospatial coordinates, a test aerial image; applying the test aerial image to the mo

Abstract: Aspects of the subject disclosure may include, for example, a device having a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of receiving network configuration data from a plurality of location-based services nodes in a network; transforming the network configuration data into reference audit values; comparing the reference audit values with the network configuration data received from each location-based services node in the plurality of location-based services nodes to generate corrections; and sending the corrections to each location-based services node in the plurality of location-based services nodes responsive to the network configuration data deviating from the reference audit values. Other embodiments are disclosed.

Abstract: A system may allow for generic end user mobile devices to be operated using a virtualized personal device framework. The virtualized personal device framework may provide for a collective intelligence network platform that allows offloading of heavy computing processes onto virtual instance in a cloud network and enables crowd sourcing services for smart cities that may provide smart emergency services in which end-users collaborate.

Abstract: Aspects of the subject disclosure may include, for example, receiving network-related information associated with a first RAN that includes a first RIC, obtaining, from an artificial intelligence (AI) model synchronization system associated with a second RAN, data relating to an AI model deployed by a second RIC of the second RAN, determining, based on the data relating to the AI model and the network-related information associated with the first RAN, that the AI model can be leveraged by the first RAN to improve network performance of the first RAN, performing synchronization with the AI model synchronization system to obtain the AI model, responsive to the determining that the AI model can be leveraged by the first RAN to improve the network performance of the first RAN, and causing the first RIC to deploy the AI model in the first RAN after the performing the synchronization. Other embodiments are disclosed.

Abstract: A model-driven system automatically deploys a virtualized service, including multiple service components, on a distributed cloud infrastructure. A master service orchestrator causes a cloud platform orchestrator to retrieve a cloud services archive file, extract a cloud resource configuration template and create cloud resources at appropriate data centers as specified. The master service orchestrator also causes a software defined network controller to retrieve the cloud services archive file, to extract a cloud network configuration template and to configure layer 1 through layer 3 virtual network functions and to set up routes between them. Additionally, the master service orchestrator causes an application controller to retrieve the cloud services archive file, to extract a deployment orchestration plan and to configure and start layer 4 through layer 7 application components and bring them to a state of operational readiness.

Abstract: A method may include a processing system having at least one processor for receiving a first machine learning model, the first machine learning model in a first format associated with a first development environment, adapting the first machine learning model to a containerized environment, validating the first machine learning model according to at least one validation criterion associated with a repository, and publishing the first machine learning model to the repository.

Abstract: Aspects of the subject disclosure may include, for example, receiving network-related information associated with a first RAN that includes a first RIC, obtaining, from an artificial intelligence (AI) model synchronization system associated with a second RAN, data relating to an AI model deployed by a second RIC of the second RAN, determining, based on the data relating to the AI model and the network-related information associated with the first RAN, that the AI model can be leveraged by the first RAN to improve network performance of the first RAN, performing synchronization with the AI model synchronization system to obtain the AI model, responsive to the determining that the AI model can be leveraged by the first RAN to improve the network performance of the first RAN, and causing the first RIC to deploy the AI model in the first RAN after the performing the synchronization. Other embodiments are disclosed.

Abstract: A cloud services composition system allows customers to interactively create service constructs from network function virtualization resources. The network function virtualization primitives are modeled using a standard modeling language. An expert system suggests network function virtualization resources for use in the service construct, based on an expert system learning algorithm. The customer uses a graphical user interface to interconnect the resources and create the service construct. The process may involve collaboration with the network provider. The resulting construct is validated for use in a communications network.

Abstract: A model-driven system automatically deploys a virtualized service, including multiple service components, on a distributed cloud infrastructure. A master service orchestrator causes a cloud platform orchestrator to retrieve a cloud services archive file, extract a cloud resource configuration template and create cloud resources at appropriate data centers as specified. The master service orchestrator also causes a software defined network controller to retrieve the cloud services archive file, to extract a cloud network configuration template and to configure layer 1 through layer 3 virtual network functions and to set up routes between them. Additionally, the master service orchestrator causes an application controller to retrieve the cloud services archive file, to extract a deployment orchestration plan and to configure and start layer 4 through layer 7 application components and bring them to a state of operational readiness.

Abstract: System and computer-implemented method for license management of virtual appliances in a computing system uses an activated virtual appliance in the computing system to forward an activation license from a license server on behalf of an unactivated virtual appliance in the computing system.

Abstract: A method may include a processing system having at least one processor for receiving a first machine learning model, the first machine learning model in a first format associated with a first development environment, adapting the first machine learning model to a containerized environment, validating the first machine learning model according to at least one validation criterion associated with a repository, and publishing the first machine learning model to the repository.

Abstract: Aspects of the subject disclosure may include, for example, receiving network-related information associated with a first RAN that includes a first RIC, obtaining, from an artificial intelligence (AI) model synchronization system associated with a second RAN, data relating to an AI model deployed by a second RIC of the second RAN, determining, based on the data relating to the AI model and the network-related information associated with the first RAN, that the AI model can be leveraged by the first RAN to improve network performance of the first RAN, performing synchronization with the AI model synchronization system to obtain the AI model, responsive to the determining that the AI model can be leveraged by the first RAN to improve the network performance of the first RAN, and causing the first RIC to deploy the AI model in the first RAN after the performing the synchronization. Other embodiments are disclosed.

Abstract: A system may allow for generic end user mobile devices to be operated using a virtualized personal device framework. The virtualized personal device framework may provide for a collective intelligence network platform that allows offloading of heavy computing processes onto virtual instance in a cloud network and enables crowd sourcing services for smart cities that may provide smart emergency services in which end-users collaborate.

Abstract: A system may allow for generic end user mobile devices to be operated using a virtualized personal device framework. The virtualized personal device framework may provide for a collective intelligence network platform that allows offloading of heavy computing processes onto virtual instance in a cloud network and enables crowd sourcing services for smart cities that may provide smart emergency services in which end-users collaborate.

Abstract: A cloud services composition system allows customers to interactively create service constructs from network function virtualization resources. The network function virtualization primitives are modeled using a standard modeling language. An expert system suggests network function virtualization resources for use in the service construct, based on an expert system learning algorithm. The customer uses a graphical user interface to interconnect the resources and create the service construct. The process may involve collaboration with the network provider. The resulting construct is validated for use in a communications network.

Abstract: A model-driven system automatically deploys a virtualized service, including multiple service components, on a distributed cloud infrastructure. A master service orchestrator causes a cloud platform orchestrator to retrieve a cloud services archive file, extract a cloud resource configuration template and create cloud resources at appropriate data centers as specified. The master service orchestrator also causes a software defined network controller to retrieve the cloud services archive file, to extract a cloud network configuration template and to configure layer 1 through layer 3 virtual network functions and to set up routes between them. Additionally, the master service orchestrator causes an application controller to retrieve the cloud services archive file, to extract a deployment orchestration plan and to configure and start layer 4 through layer 7 application components and bring them to a state of operational readiness.

Abstract: Concepts and technologies are disclosed herein for a virtualized services discovery and recommendation engine. A request for a service can be received from a requestor. The request can include feature data that can define two or more features. Two or more features can be determined based on the feature data and requestor data associated with the requestor can be obtained. Two or more services that satisfy the request can be identified and a recommendation that represents the two or more services can be generated. The recommendation can include a data representation of a custom product that can include the two or more services. The recommendation can be tested to determine if the custom product functions, and the recommendation can be provided to the requestor.

Abstract: A model-driven system automatically deploys a virtualized service, including multiple service components, on a distributed cloud infrastructure. A master service orchestrator causes a cloud platform orchestrator to retrieve a cloud services archive file, extract a cloud resource configuration template and create cloud resources at appropriate data centers as specified. The master service orchestrator also causes a software defined network controller to retrieve the cloud services archive file, to extract a cloud network configuration template and to configure layer 1 through layer 3 virtual network functions and to set up routes between them. Additionally, the master service orchestrator causes an application controller to retrieve the cloud services archive file, to extract a deployment orchestration plan and to configure and start layer 4 through layer 7 application components and bring them to a state of operational readiness.