Abstract: Techniques that facilitate orchestration engine resources and/or blueprint definitions for hybrid cloud composition are provided. In one example, a system includes a blueprint component and a blueprint transformation component. The blueprint component determines one or more abstract resource types for an abstract blueprint associated with a computing platform. The one or more abstract resource types are indicative of information associated with one or more computing resources for the computing platform. The blueprint transformation component transforms the one or more abstract resource types for the abstract blueprint into one or more executable resources for an executable blueprint that is executable by an orchestration engine.

Abstract: Systems, computer-implemented methods, and computer program products that facilitate orchestration engine components for a cloud computing environment are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a ranking component that can rank respective connection components based on one or more attributes of the respective connection components. The computer executable components can further comprise a selection component that can select a connection component based on rank. The computer executable components can also comprise an orchestration engine component that can, based on the selected connection component, operatively couple a provisioning component to a computing resource component t of a cloud-based computing platform.

Abstract: Techniques that facilitate orchestration engine blueprint aspects for hybrid cloud composition are provided. In one example, a system includes a blueprint component and a hybrid cloud composition component. The blueprint component determines one or more blueprint-level aspects for a blueprint associated with a cloud-based computing platform. The one or more blueprint-level aspects are indicative of encoded information for one or more features associated with one or more computing resources for the cloud-based computing platform. The hybrid cloud composition component determines a set of resource definitions for the cloud-based computing platform based on the one or more blueprint-level aspects.

Abstract: A system, computer program product, and method are provided to automate management of a computer architecture as represented in a codified infrastructure with one or more embedded dynamically labeled resources. The codified infrastructure is subject to interpretation within the context of the dynamically labeled resources. The dynamic labels are converted to static labels, after which the corresponding architecture is deployed. The deployment architecture continues to be updated when the meaning of the dynamic labels change.

Abstract: Techniques that facilitate orchestration engine blueprint aspects for hybrid cloud composition are provided. In one example, a system includes a blueprint component and a hybrid cloud composition component. The blueprint component determines one or more blueprint-level aspects for a blueprint associated with a cloud-based computing platform. The one or more blueprint-level aspects are indicative of encoded information for one or more features associated with one or more computing resources for the cloud-based computing platform. The hybrid cloud composition component determines a set of resource definitions for the cloud-based computing platform based on the one or more blueprint-level aspects.

Abstract: Techniques that facilitate orchestration engine blueprint aspects for hybrid cloud composition are provided. In one example, a system includes a blueprint component, a hybrid cloud composition component, and a learning component. The blueprint component determines one or more blueprint-level aspects for a blueprint associated with a cloud-based computing platform. The one or more blueprint-level aspects are indicative of encoded information for one or more features associated with one or more computing resources for the cloud-based computing platform. The hybrid cloud composition component determines a set of resource definitions for the cloud-based computing platform based on the one or more blueprint-level aspects. The learning component monitors the cloud-based computing platform based on the one or more blueprint-level aspects to learn one or more features associated with the cloud-based computing platform.

Abstract: Techniques facilitating service management for the infrastructure of blockchain networks are provided. A system comprises a memory and a processor that executes computer executable components stored in the memory. The computer executable components can comprise an allocation component, a grouping component, and an implementation component. The allocation component can assign, within a blockchain network, a first group of nodes of a first node type to a first set of operation slots and a second group of nodes of a second node type, different than the first node type, to a second set of operation slots. The grouping component can aggregate the second group of nodes assigned to the second set of operation slots with the first group of nodes within the first set of operation slots. The implementation component can execute a service management operation. A consensus algorithm can be satisfied during an execution of the service management operation.

Abstract: Techniques that facilitate orchestration engine blueprint aspects for hybrid cloud composition are provided. In one example, a system includes a learning component and a hybrid cloud composition component. The learning component learns one or more blueprint-level aspects associated with information for one or more computing resources of a cloud-based computing platform based on historical data associated with the cloud-based computing platform. The hybrid cloud composition component generates a set of resource definitions for the cloud-based computing platform based on the one or more blueprint-level aspects. The hybrid cloud composition component also modifies a blueprint associated with the cloud-based computing platform based on the set of resource definitions.

Abstract: A system, computer program product, and method are provided to automate management of a computer architecture as represented in a codified infrastructure with one or more embedded dynamically labeled resources. The codified infrastructure is subject to interpretation within the context of the dynamically labeled resources. The dynamic labels are converted to static labels, after which the corresponding architecture is deployed. The deployment architecture continues to be updated when the meaning of the dynamic labels change.

Abstract: Systems, computer-implemented methods, and computer program products that facilitate orchestration engine components for a cloud computing environment are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a blueprint component that can, based on one or more dependencies between respective resource components of a cloud-based computing platform, declare a steady state action to be executed in response to a steady state event indicative of an event associated with steady state operation of a resource component of the cloud-based computing platform. The computer executable components can further comprise an orchestration engine component that can, based on the blueprint component, execute the steady state action in response to the steady state event.

Abstract: A system and method to perform incremental adaptive modification of a virtual reality image involve obtaining sensor measurements from sensors coupled to an individual who is performing a task while viewing the virtual reality image. The method includes generating a true model of the individual from the sensor measurements, comparing the true model with an expert model obtained from another individual performing the task, and developing a lesson plan based on determining a difference between the true model and the expert model. The developing the lesson plan includes determining a gradient of intermediate models within the difference. A different one of the intermediate models is included iteratively along the gradient from the true model to the expert model in the virtual reality image.

Abstract: An aspect includes the automatic generation of blueprints for orchestration engines from discovered workload representations. A computer-implemented method includes receiving, using a processor, a description of a workload executing in a source computing environment. The description of the workload is mapped, using the processor, to one or more resource descriptors located in a repository of resource descriptors describing resources available in a target computing environment. A declarative representation of the workload in the target computing environment is generated, using the processor, based at least in part on the mapping and the description of the workload. The declarative representation includes a specification of resources required by the workload in the target environment.

Abstract: A system and method to perform incremental adaptive modification of a virtual reality image involve obtaining sensor measurements from sensors coupled to an individual who is performing a task while viewing the virtual reality image. The method includes generating a true model of the individual from the sensor measurements, comparing the true model with an expert model obtained from another individual performing the task, and developing a lesson plan based on determining a difference between the true model and the expert model. The developing the lesson plan includes determining a gradient of intermediate models within the difference. A different one of the intermediate models is included iteratively along the gradient from the true model to the expert model in the virtual reality image.

Abstract: Techniques that facilitate an orchestration engine using a blockchain for a cloud resource digital ledger are provided. In one example, a system includes an orchestration engine component and a blockchain component. The orchestration engine component manages one or more computing resources for a cloud-based computing platform. The blockchain component adds event data indicative of an event associated with the one or more computing resources into a blockchain dataset for the cloud-based computing platform. The blockchain dataset comprises a sequence of data blocks that corresponds to a sequence of events for the cloud-based computing platform.

Abstract: An aspect includes the automatic generation of blueprints for orchestration engines from discovered workload representations. A computer-implemented method includes receiving, using a processor, a description of a workload executing in a source computing environment. The description of the workload is mapped, using the processor, to one or more resource descriptors located in a repository of resource descriptors describing resources available in a target computing environment. A declarative representation of the workload in the target computing environment is generated, using the processor, based at least in part on the mapping and the description of the workload. The declarative representation includes a specification of resources required by the workload in the target environment.

Abstract: Techniques that facilitate orchestration engine resources and/or blueprint definitions for hybrid cloud composition are provided. In one example, a system includes a blueprint component and a blueprint transformation component. The blueprint component determines one or more abstract resource types for an abstract blueprint associated with a computing platform. The one or more abstract resource types are indicative of information associated with one or more computing resources for the computing platform. The blueprint transformation component transforms the one or more abstract resource types for the abstract blueprint into one or more executable resources for an executable blueprint that is executable by an orchestration engine.

Abstract: Systems, computer-implemented methods, and computer program products that facilitate orchestration engine components for a cloud computing environment are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a ranking component that can rank respective connection components based on one or more attributes of the respective connection components. The computer executable components can further comprise a selection component that can select a connection component based on rank. The computer executable components can also comprise an orchestration engine component that can, based on the selected connection component, operatively couple a provisioning component to a computing resource component t of a cloud-based computing platform.

Abstract: Systems, computer-implemented methods, and computer program products that facilitate orchestration engine components for a cloud computing environment are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a blueprint component that can, based on one or more dependencies between respective resource components of a cloud-based computing platform, declare a steady state action to be executed in response to a steady state event indicative of an event associated with steady state operation of a resource component of the cloud-based computing platform. The computer executable components can further comprise an orchestration engine component that can, based on the blueprint component, execute the steady state action in response to the steady state event.

Abstract: Techniques facilitating service management for the infrastructure of blockchain networks are provided. A system comprises a memory and a processor that executes computer executable components stored in the memory. The computer executable components can comprise an allocation component, a grouping component, and an implementation component. The allocation component can assign, within a blockchain network, a first group of nodes of a first node type to a first set of operation slots and a second group of nodes of a second node type, different than the first node type, to a second set of operation slots. The grouping component can aggregate the second group of nodes assigned to the second set of operation slots with the first group of nodes within the first set of operation slots. The implementation component can execute a service management operation. A consensus algorithm can be satisfied during an execution of the service management operation.

Abstract: Techniques that facilitate an orchestration engine using a blockchain for a cloud resource digital ledger are provided. In one example, a system includes an orchestration engine component and a blockchain component. The orchestration engine component manages one or more computing resources for a cloud-based computing platform. The blockchain component adds event data indicative of an event associated with the one or more computing resources into a blockchain dataset for the cloud-based computing platform. The blockchain dataset comprises a sequence of data blocks that corresponds to a sequence of events for the cloud-based computing platform.