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, 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, 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, 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.

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: 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: A system that incorporates teachings of the present disclosure may include, for example, identifying a request from a femtocell located at a customer premises to receive a requested service of subscribed services related to a subscriber account that identifies an availability of the requested service to the femtocell. Authentication information is determined, provided by way of the femtocell and a message is provided to a server including the authentication information and an identifier of a customer premises device located at the customer premises. An authentication process to authenticate the femtocell is initiated responsive to the request, wherein the server identifies the subscriber account based on the identifier of the customer premises device, and wherein the femtocell is authenticated based on information associated with the subscriber account. Other embodiments are disclosed.

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: 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, and virtual machines and virtual network functions that provide the service can be identified. A first policy that defines allocation of hardware resources to host the virtual machines and the virtual network functions can be obtained, as can a second policy that defines deployment of the virtual machines and the virtual network functions to the hardware resources. The hardware resources can be allocated based upon the first policy and the virtual machines 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: 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.