A method of deploying a network service across a plurality of data centers, includes the steps of: in response to a request for or relating to a network service, identifying virtual network functions associated with the network service and determining network connectivity requirements of the virtual network functions, issuing commands to provision a first virtual link between at least two of the data centers in which the virtual network functions are to be deployed, and issuing commands to provision a second virtual link to one of the data centers in which the virtual network functions are to be deployed.

A network administration device may include one or more processors to receive operational information regarding a plurality of network devices; receive flow information relating to at least one traffic flow; input the flow information to a model, where the model is generated based on a machine learning technique, and where the model is configured to identify predicted performance information of one or more network devices with regard to the at least one traffic flow based on the operational information; determine path information for the at least one traffic flow with regard to the one or more network devices based on the predicted performance information; and/or configure the one or more network devices to implement the path information for the traffic flow.

A method (200) of configuring a telecommunications network (100), the method comprising the steps 5 of: identifying a User Equipment (UE) (110) utilizing the telecommunications network; ascertaining physical capability information associated with a user of the identified UE (220); determining a network configuration in dependence upon the ascertained physical capability information (240); and configuring a network connection for the identified UE according to the determined network configuration (250).

A device may include a processor configured to determine a plurality of requirements for a Multi-Access Edge Computing (MEC) application requested by a customer; select a solution blueprint for the MEC application, from a set of solution blueprints, based on the determined plurality of requirements, wherein the solution blueprint includes an application deployment blueprint and a connectivity blueprint; and receive approval of the selected solution blueprint from the customer. The processor may be further configured to configure at least one transport network device based on the connectivity blueprint, in response to receiving the approval of the selected solution blueprint from the customer; and deploy at least one component of the MEC application on a MEC device in a MEC network based on the application deployment blueprint, in response to receiving the approval of the selected solution blueprint from the customer.

Methods, systems, apparatuses, and computer program products are described herein that enable a vendor to publish a template to a network-accessible marketplace and that enable a customer to selectively utilize the templates to deploy an appliance to a customer account associated with the customer within a cloud services platform. In an embodiment, deploying the appliance to the customer account comprises deploying resources specified by the template as part of an appliance resource group within the customer account, the specified resources including an appliance management resource that is configured to expose control features of the appliance to the customer. In further accordance with the techniques described herein, access to the appliance resource group is provided to the vendor via a vendor account associated with the vendor within the cloud services platform.

Technologies are disclosed for improving the deployment of a cloud-hosted service. Before being deployed to a particular environment, a cloud-hosted service must be configured for that environment. Configuring a deployment includes determining which components to deploy, determining how to connect with external components, identifying onboarding procedures, etc. A dependency data model defines a hierarchy of components utilized by the cloud-hosted service. For each component in the hierarchy, configuration parameters define how to deploy that component. A list of configuration parameters that do not yet have values for a target environment may be generated and provided to a user. Values for these parameters may then be received. A configuration for the target environment is then generated based on the dependency data model and the received values. The dependency data model may inherit dependencies and configuration properties from ancestors in a hierarchy of dependency data models.

Systems, methods, apparatuses, and computer program products for a service model management request. The method may include receiving an end-to-end network service model for network services. The method may also includes determining a set of domain services that are required to be able to provide an end-to-end network service defined by the end-to-end network service model. The method may further include checking whether at least one management domain is capable of providing the required set of domain services. The method may also include requesting, when the at least one management domain is not capable of providing the required set of domain services, to prepare the at least one management domain to become able to provide the required set of domain services. In addition, the method may include receiving a notification from the at least one management domain indicating an availability to provide the required set of domain services.

Cloud infrastructure planning systems and methods can utilize artificial intelligence/machine learning agents for developing a plan of demand, plan of record, plan of execution, and plan of availability for developing cloud infrastructure plans that are more precise and accurate, and that learn from previous planning and deployments. Some agents include one or more of supervised, unsupervised, and reinforcement machine learning to develop accurate predictions and perform self-tuning alone or in conjunction with other agents.

A technique involves modular storage of network service plan components and provisioning of same. A subset of the capabilities of a service design system can be granted to a sandbox system to enable customization of service plan offerings or other controls.

Cloud infrastructure planning systems and methods can utilize artificial intelligence/machine learning agents for developing a plan of demand, plan of record, plan of execution, and plan of availability for developing cloud infrastructure plans that are more precise and accurate, and that learn from previous planning and deployments. Some agents include one or more of supervised, unsupervised, and reinforcement machine learning to develop accurate predictions and perform self-tuning alone or in conjunction with other agents.