Some embodiments of the invention provide a method for deploying software-implemented resources in a software defined datacenter (SDDC). The method initially receives a hierarchical API command that, in a declarative format, specifies several operation requests for several software-defined (SD) resources at several resource levels of a resource hierarchy in the SDDC. The method parses the API command to identify the SD resources at the plurality of resource levels. Based on the parsed API command, the method deploys the SD resources by using a deployment process that ensures that any first SD resource on which a second SD resource depends is deployed before the second resource. In some embodiments, a second SD resource depends on a first SD resource when the second SD resource is a child of the first SD resource. Alternatively, or conjunctively, a second SD resource can also depend on a first SD resource in some embodiments when the second SD resource has some operational dependency on the first SD resource. In some embodiments, the method parses the API command by identifying several sets of SD resources, with each set having one or more SD resources at one resource level. The deployment in some embodiments deploys the identified SD resource sets at higher resource levels before deploying SD resources at lower resource levels.

A digital twin of an IT infrastructure is created to identify a group of critical servers (called “base servers”) needed to replicate the IT infrastructure in a cloud-computing environment. To identify the correct base servers and their actual server configurations, the IT infrastructure is crawled and various telemetry, connection, and network data is analyzed against data sets of other known servers. The digital twin is created to include these base servers and their particular configurations. Then, the digital twin may be deployed on demand in the cloud-computing environment using executable scripts that mimic the base servers and their particular configurations, creating a replication of the IT infrastructure for various purposes (e.g., redundancy, testing, etc.).

A digital twin of an IT infrastructure is created to identify a group of critical servers (called “base servers”) needed to replicate the IT infrastructure in a cloud-computing environment. To identify the correct base servers and their actual server configurations, the IT infrastructure is crawled and various telemetry, connection, and network data is analyzed against data sets of other known servers. The digital twin is created to include these base servers and their particular configurations. Then, the digital twin may be deployed on demand in the cloud-computing environment using executable scripts that mimic the base servers and their particular configurations, creating a replication of the IT infrastructure for various purposes (e.g., redundancy, testing, etc.).

Methods, systems, and non-transitory computer-readable media are provided for deploying intent-driving cloud branches. An example method can include obtaining, by one or more controllers in a software-defined network (SDN), a branch network design template for deploying a remote branch in the SDN, wherein the branch network design template defines networking settings for a plurality of services to be provisioned at the remote branch; obtaining, by the one or more controllers, a plurality of software packages for the plurality of services to be provisioned at the remote branch; and based on the branch network design template and the plurality of software packages, provisioning, by the one or more controllers, the plurality of services at the remote branch and a network connectivity of the plurality of services.

A device for configuring a virtualized network function, configured for use in a virtualized communication network, is disclosed. The device is configured to receive a request to configure the virtualized network function. The device is also configured to obtain at least one parameter for implementing an elementary component of the virtualized network function in a virtualized communication network, to be added to a basic virtualized network function. The device is also configured to compose the virtualized network function from at least one elementary component and from the parameter for implementing the at least one obtained elementary component.

Systems and methods for providing mobile services are disclosed. In one implementation, an access point (AP) is provided, which may include a set of one or more base-station functions for use by a user equipment (UE) connected to the AP over a wireless communication interface. The one or more base-station functions may be configured to receive information from the UE. The AP may further include a set of one or more core-network functions configured to receive the information from the set of one or more base-station functions and a distributed portion of a service. The distributed portion of the service may be configured to receive the information from the one or more core-network functions and communicate the information to a corresponding cloud portion of the service running on a cloud platform. The service may be provided by a combination of the distributed portion and the cloud portion of the service. The distributed portion of the service may be further configured to receive a response from the cloud portion of the service based on processing performed by the cloud portion on the cloud platform.

Systems and methods for providing mobile services are disclosed. In one implementation, an access point (AP) is provided, which may include a set of one or more base-station functions for use by a user equipment (UE) connected to the AP over a wireless communication interface. The one or more base-station functions may be configured to receive information from the UE. The AP may further include a set of one or more core-network functions configured to receive the information from the set of one or more base-station functions and a distributed portion of a service. The distributed portion of the service may be configured to receive the information from the one or more core-network functions and communicate the information to a corresponding cloud portion of the service running on a cloud platform. The service may be provided by a combination of the distributed portion and the cloud portion of the service. The distributed portion of the service may be further configured to receive a response from the cloud portion of the service based on processing performed by the cloud portion on the cloud platform.

An approach is provided for managing the provisioning and utilization of resources. A management platform determines a request from a user for execution of one or more data processing tasks by a remote computing service. The management platform also processes and/or facilitates a processing of at least one execution constraint associated with the user, a group associated with the user, or a combination thereof to determine a maximum number of clusters, cluster instances, or a combination thereof of the remote computing service to be provisioned for fulfilling the request. The management platform further causes, at least in part, a provisioning of one or more clusters, one or more cluster instances, or a combination thereof to the user, the group, or a combination thereof to within the maximum number of clusters, cluster instances, or a combination thereof based on the at least one execution constraint.

Techniques discussed herein relate to providing in-memory workflow management at an edge device (e.g., a computing device distinct from and operating remotely with respect to a data center). The edge device can operate as a computing node in a computing cluster of edge devices and implement a hosting environment (e.g., a distributed data plane). A work request can be obtained by an in-memory workflow manager of the edge device. The work request may include an intended state of a data plane resource (e.g., a computing cluster, a virtual machine, etc.). The in-memory workflow manager can determine the work request has not commenced and initialize an in-memory execution thread to execute orchestration tasks to configure a data plane of the computing cluster according to the intended state. Current state data corresponding to the configured data plane may be provided to the user device and eventually displayed.

Described herein are techniques for implementing intelligent network slicing. Such techniques may include receiving data usage information associated with at least one user and generating, using the data usage information, a predicted usage model for the at least one user. Upon identifying an upcoming predicted usage event from the predicted usage model, the techniques may further include determining a current status of a core network and upon determining, based on the current status of the core network, that the core network is inadequate to handle the upcoming predicted usage event, instantiating a new network slice within the core network configured to handle the upcoming predicted usage event. Upon identifying network traffic associated with the upcoming predicted usage event, the techniques comprise routing that network traffic through the new network slice.