Abstract: The present disclosure relates to systems, methods, and computer-readable media for deploying cloud-native services across a plurality of cloud-computing platforms. For example, systems disclosed herein identify resource identifiers associated with cloud-computing services (e.g., types of services) to be deployed on one or more resources capable of executing or otherwise providing cloud-native services. The systems disclosed herein further generate resource bindings including deployment specifications that include data for deploying cloud-native services on corresponding platform resources (e.g., cloud resources, edge resources). Using the resource bindings, the systems disclosed herein can deploy cloud-native services across multiple platforms via control planes configured to manage operation of resources on the different platforms.

Abstract: A computing system includes a capability proxy that receives a semantic description of a requested function capability; determines an execution constraint associated with the requested function capability; queries a function registry based at least in part on the requested function capability; and identifies, based on information returned from the function registry, a select function that provides the requested function capability and that has an execution characteristic satisfying the execution constrain. The capability proxy executes the select function and returns an output of the select function.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for deploying cloud-native services across a plurality of cloud-computing platforms. For example, systems disclosed herein identify resource identifiers associated with cloud-computing services (e.g., types of services) to be deployed on one or more resources capable of executing or otherwise providing cloud-native services. The systems disclosed herein further generate resource bindings including deployment specifications that include data for deploying cloud-native services on corresponding platform resources (e.g., cloud resources, edge resources). Using the resource bindings, the systems disclosed herein can deploy cloud-native services across multiple platforms via control planes configured to manage operation of resources on the different platforms.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for generating a platform-neutral application model that provides a complete and accurate representation of functionality and topology for a cloud-native application. For example, systems disclosed herein analyze application data to identify platform neutral application features including resources, mesh connections, and quality of service (QoS) constraints associated with implementing a cloud-native application via a cloud computing system. The systems disclosed herein further construct a platform-neutral application model including identifiers of the application features. The platform-neutral application model facilitates convenient translation of applications between different platforms and further streamlines development and deployment of cloud-native applications across any number of platforms.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for deploying cloud-native services across a plurality of cloud-computing platforms. For example, systems disclosed herein identify resource identifiers associated with cloud-computing services (e.g., types of services) to be deployed on one or more resources capable of executing or otherwise providing cloud-native services. The systems disclosed herein further generate resource bindings including deployment specifications that include data for deploying cloud-native services on corresponding platform resources (e.g., cloud resources, edge resources). Using the resource bindings, the systems disclosed herein can deploy cloud-native services across multiple platforms via control planes configured to manage operation of resources on the different platforms.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for deploying cloud-native services across a plurality of cloud-computing platforms. For example, systems disclosed herein identify resource identifiers associated with cloud-computing services (e.g., types of services) to be deployed on one or more resources capable of executing or otherwise providing cloud-native services. The systems disclosed herein further generate resource bindings including deployment specifications that include data for deploying cloud-native services on corresponding platform resources (e.g., cloud resources, edge resources). Using the resource bindings, the systems disclosed herein can deploy cloud-native services across multiple platforms via control planes configured to manage operation of resources on the different platforms.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for generating a platform-neutral application model that provides a complete and accurate representation of functionality and topology for a cloud-native application. For example, systems disclosed herein analyze application data to identify platform neutral application features including resources, mesh connections, and quality of service (QoS) constraints associated with implementing a cloud-native application via a cloud computing system. The systems disclosed herein further construct a platform-neutral application model including identifiers of the application features. The platform-neutral application model facilitates convenient translation of applications between different platforms and further streamlines development and deployment of cloud-native applications across any number of platforms.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for generating a platform-neutral application model that provides a complete and accurate representation of functionality and topology for a cloud-native application. For example, systems disclosed herein analyze application data to identify platform neutral application features including resources, mesh connections, and quality of service (QoS) constraints associated with implementing a cloud-native application via a cloud computing system. The systems disclosed herein further construct a platform-neutral application model including identifiers of the application features. The platform-neutral application model facilitates convenient translation of applications between different platforms and further streamlines development and deployment of cloud-native applications across any number of platforms.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for generating a platform-neutral application model that provides a complete and accurate representation of functionality and topology for a cloud-native application. For example, systems disclosed herein analyze application data to identify platform neutral application features including resources, mesh connections, and quality of service (QoS) constraints associated with implementing a cloud-native application via a cloud computing system. The systems disclosed herein further construct a platform-neutral application model including identifiers of the application features. The platform-neutral application model facilitates convenient translation of applications between different platforms and further streamlines development and deployment of cloud-native applications across any number of platforms.

Abstract: According to some embodiments, a machine learning architecture design platform may access a microservice architecture design data store that contains existing microservice architecture designs, and a graphic abstraction computing component may automatically create existing graph models of the existing designs. A pattern recognition computing component may then execute a machine learning algorithm to access the existing graph models and automatically detect existing design patterns. A designer interface computing component may interactively and iteratively exchange information with a designer, including receipt of at least one design requirement from the designer. Based on the at least one received design requirement and the automatically detected existing design patterns, a dynamic recommendation computing component may automatically construct a recommended microservice architecture for the cloud computing environment.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for generating a platform-neutral application model that provides a complete and accurate representation of functionality and topology for a cloud-native application. For example, systems disclosed herein analyze application data to identify platform neutral application features including resources, mesh connections, and quality of service (QoS) constraints associated with implementing a cloud-native application via a cloud computing system. The systems disclosed herein further construct a platform-neutral application model including identifiers of the application features. The platform-neutral application model facilitates convenient translation of applications between different platforms and further streamlines development and deployment of cloud-native applications across any number of platforms.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for deploying cloud-native services across a plurality of cloud-computing platforms. For example, systems disclosed herein identify resource identifiers associated with cloud-computing services (e.g., types of services) to be deployed on one or more resources capable of executing or otherwise providing cloud-native services. The systems disclosed herein further generate resource bindings including deployment specifications that include data for deploying cloud-native services on corresponding platform resources (e.g., cloud resources, edge resources). Using the resource bindings, the systems disclosed herein can deploy cloud-native services across multiple platforms via control planes configured to manage operation of resources on the different platforms.

Abstract: According to some embodiments, a machine learning architecture design platform may access a microservice architecture design data store that contains existing microservice architecture designs, and a graphic abstraction computing component may automatically create existing graph models of the existing designs. A pattern recognition computing component may then execute a machine learning algorithm to access the existing graph models and automatically detect existing design patterns. A designer interface computing component may interactively and iteratively exchange information with a designer, including receipt of at least one design requirement from the designer. Based on the at least one received design requirement and the automatically detected existing design patterns, a dynamic recommendation computing component may automatically construct a recommended microservice architecture for the cloud computing environment.