Abstract: Aspects of the subject disclosure may include, for example, a device with a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitates performance of operations, including: automatically detecting an intrusion of a secure communication channel that provides communication services to a node in a network; implementing a generative artificial intelligence that finds an alternative secure communication channel; and migrating the communication services to the alternative secure communication channel responsive to finding the alternative secure communication channel. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, receiving artificial intelligence (AI) analytics from a generative AI system, and based on the AI analytics, causing a software defined network (SDN) controller to identify a signaling path over a quantum signaling network, thereby enabling selection of a determined suitable path for quantum entanglement distribution. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a system, including: a plurality of drones; 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: deploying one or more drones of the plurality of drones; and distributing entangled qubits via the plurality of drones to one or more ground stations. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device with a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitates performance of operations, including: automatically detecting an intrusion of a secure communication channel that provides communication services to a node in a network; implementing a generative artificial intelligence that finds an alternative secure communication channel; and migrating the communication services to the alternative secure communication channel responsive to finding the alternative secure communication channel. Other embodiments are disclosed.

Abstract: An automated system provides end-to-end (E2E) orchestration for machine learning (ML)-enabled and software-defined network (SDN)-enabled reactive and predictive (e.g., 5G) cell-on-drone (COD) dispatch and deployment. The system may be configured to detect or predict traffic surges (e.g., relative to typical or baseline levels) in a given cell site and orchestrate provisioning of resources, including COD deployment, to address the surge. COD deployment may involve one or more clusters or colonies of drones equipped with low-powered cellular radio access (or small cell) nodes, where each cluster includes (or is led by) one or more SDN equipped drones that provide intelligent SDN-on-drone (SOD) functionality.

Abstract: Aspects of the subject disclosure may include, for example, deploying, via a model managing platform, a first model, where the deploying causes the first model to operate as part of a microservice and to consume operational data, and where the microservice provides functionality to an entity according to operational data; accessing training data; training a second model utilizing the training data; and replacing the first model with the second model, where the replacing the first model occurs without interrupting the functionality for the entity. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a system, including: a plurality of drones; 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: deploying one or more drones of the plurality of drones; and distributing entangled qubits via the plurality of drones to one or more ground stations. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a system including a cross-segment slice controller (CSSC) configured to interface with a software-defined network (SDN) controller and a software-defined radio (SDR) controller. The SDN controller may be associated with a core network and the SDR controller may be associated with a radio access network (RAN). The system further includes a machine learning (ML) component configured to obtain and analyze data regarding the core network and the RAN, and an intelligent end-to-end (E2E) orchestration platform (IEOP) configured to coordinate with the SDN controller and the SDR controller via the CSSC based on outputs of the ML component to provide dynamic cross-segment network slice management. Other embodiments are disclosed.

Abstract: An automated system provides end-to-end (E2E) orchestration for machine learning (ML)-enabled and software-defined network (SDN)-enabled reactive and predictive (e.g., 5G) cell-on-drone (COD) dispatch and deployment. The system may be configured to detect or predict traffic surges (e.g., relative to typical or baseline levels) in a given cell site and orchestrate provisioning of resources, including COD deployment, to address the surge. COD deployment may involve one or more clusters or colonies of drones equipped with low-powered cellular radio access (or small cell) nodes, where each cluster includes (or is led by) one or more SDN-equipped drones that provide intelligent SDN-on-drone (SOD) functionality.

Abstract: Concepts and technologies are disclosed for deploying a virtual network function (VNF) that provides a service in a cellular network. A processor allocates hardware resources at one or more sites to host the VNF based on a redundancy policy that defines a three-layer redundancy profile comprising intra-site, inter-site, and internal redundancy. The allocation includes assigning a geographic location, a pod, and a rack for the hardware resources. The processor deploys the VNF to the allocated hardware resources based on a placement policy that defines affinity and anti-affinity rules for distributing virtual machines associated with the VNF. The disclosed technologies improve fault tolerance and service continuity in virtualized cellular network environments.

Abstract: Aspects of the subject disclosure may include, for example, a device with a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitates performance of operations, including: automatically detecting an intrusion of a secure communication channel that provides communication services to a node in a network; implementing a generative artificial intelligence that finds an alternative secure communication channel; and migrating the communication services to the alternative secure communication channel responsive to finding the alternative secure communication channel. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, monitoring status of a network configured to provide end-to-end delivery of services to priority user equipment authorized according to a prioritized class of service and other user equipment not authorized for the prioritized class of service. The prioritized class of service extends a reserved network capacity for end-to-end delivery of a service via first domain including first network slices and a second domain including second network slices. A network interruption compromising the reserved capacity is determined based on the monitored status and integration of an alternate group of slices into one of the first network domain, the second network domain, or both to replace the compromised group of slices, is orchestrated. Integration of the alternate group of slices restores reserved network capacity according to the prioritized class of service. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a system including a cross-segment slice controller (CSSC) configured to interface with a software-defined network (SDN) controller and a software-defined radio (SDR) controller. The SDN controller may be associated with a core network and the SDR controller may be associated with a radio access network (RAN). The system further includes a machine learning (ML) component configured to obtain and analyze data regarding the core network and the RAN, and an intelligent end-to-end (E2E) orchestration platform (IEOP) configured to coordinate with the SDN controller and the SDR controller via the CSSC based on outputs of the ML component to provide dynamic cross-segment network slice management. Other embodiments are disclosed.

Abstract: Concepts and technologies are disclosed related to allocation of hardware resources for virtual network function (VNF) deployment at sites based on templates. A design template defines hardware resources to allocate for a service, and a deployment policy defines deployment of the virtual network functions (VNFs) associated with the service to the hardware resources. Hardware resources are allocated at one or more sites based on the design template to host VNFs for the service, and VNFs are deployed to the hardware resources based upon the deployment policy.

Abstract: Aspects of the subject disclosure may include, for example, receiving artificial intelligence (AI) analytics from a generative AI system, and based on the AI analytics, causing a software defined network (SDN) controller to identify a signaling path over a quantum signaling network, thereby enabling selection of a determined suitable path for quantum entanglement distribution. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, monitoring status of a network configured to provide end-to-end delivery of services to priority user equipment authorized according to a prioritized class of service and other user equipment not authorized for the prioritized class of service. The prioritized class of service extends a reserved network capacity for end-to-end delivery of a service via first domain including first network slices and a second domain including second network slices. A network interruption compromising the reserved capacity is determined based on the monitored status and integration of an alternate group of slices into one of the first network domain, the second network domain, or both to replace the compromised group of slices, is orchestrated. Integration of the alternate group of slices restores reserved network capacity according to the prioritized class of service. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, combining a plurality of machine learning (ML) models to form a composite model, the plurality of ML models including a first ML model trained on first local data received at a first network location and added models including a second ML model through an nth ML model, respective added models of the added models each being respectively trained on respective training data at a respective network location remote from the first network location; receiving input data at the first network location; providing the input data to the composite model; receiving, from the composite model, a conclusion about a status of the input data; receiving an indication to update one or more models of the plurality of ML models; and updating the one or more models according to the indication. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, identifying a first edge network including a compute resource configured according to a requirement of a network slice to obtain a primary network slice adapted to provide a network service to equipment of a mobile consumer according to a service level requirement. A second edge network at a different location is identified including a first reserve compute resource available for configuration. Remote configuration of the first reserve compute resource is initiated according to the requirement of the network slice to obtain a first backup network slice adapted to provide the network service. Operation of the primary network slice is monitored, and a first performance issue is identified. Responsive to the first performance issue, a providing of the network service is reconfigured from the primary network slice to the first backup network slice. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, receiving workload requests for tenant devices providing services to user devices; partitioning node resources to facilitate network slicing in a software defined network to provide the services; determining that the workload requests exceed available resources of the node; and outsourcing a portion of a load to one or more neighboring nodes, where the outsourcing is performed in coordination with a regional orchestrator and a SDN intelligence module. Other embodiments are disclosed.

Abstract: Concepts and technologies are disclosed herein for providing a network virtualization policy management system. An event relating to a service can be detected. A first policy that defines allocation of hardware resources to host the virtual network functions can be obtained, as can a second policy that defines deployment of the virtual network functions to the hardware resources. The hardware resources can be allocated based upon the first policy and the virtual network functions can be deployed to the hardware resources based upon the second policy.