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.

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.

Abstract: Aspects of the subject disclosure may include, for example, determining a need to offload traffic for an access network based on communications from an access mobility function (AMF), identifying a colony of drones to address the need to offload traffic for the access network, deploying the colony of drones to a coverage area corresponding to the access network, and providing end-to-end (E2E) orchestration across a core network and the colony of drones. 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: 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: 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: 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.

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.

Abstract: Virtual machine server clusters are managed using self-healing and dynamic optimization to achieve closed-loop automation. The technique uses adaptive thresholding to develop actionable quality metrics for benchmarking and anomaly detection. Real-time analytics are used to determine the root cause of KPI violations and to locate impact areas. Self-healing and dynamic optimization rules are able to automatically correct common issues via no-touch automation in which finger-pointing between operations staff is prevalent, resulting in consolidation, flexibility and reduced deployment time.

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.

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.