Abstract: A computer-implemented method for managing one or more operations of a workload includes selecting a resource type for workload management on a platform. One or more operations of the selected resource to be managed are identified. A reconciliation time for execution of each of the identified operations is determined. A reconciliation period between two consecutive reconciliations is determined for each of the identified operations. A minimum number of processes for workload management of a given set of the operations on resources is calculated, and the determined minimum number of processes is deployed to manage the workload.

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: An artificial intelligence (AI) platform to support workflow version process control. One or more workflows corresponding to one or more workflow engines are monitored. A neural network is employed to capture a relationship associated with a detected change in the monitored workflows. The neural network is leveraged to identify and assess an impact of the detected change to one or more additional workflows. Responsive to the assessment, the impacted workflow engines are optimized. The optimization includes automatically mapping and encoding changes corresponding to the impacted workflow. The one or more workflows containing the encoded changes are then executed.

Abstract: A computer-implemented method for managing one or more operations of a workload includes selecting a resource type for workload management on a platform. One or more operations of the selected resource to be managed are identified. A reconciliation time for execution of each of the identified operations is determined. A reconciliation period between two consecutive reconciliations is determined for each of the identified operations. A minimum number of processes for workload management of a given set of the operations on resources is calculated, and the determined minimum number of processes is deployed to manage the workload.

Abstract: A system, computer program product, and method are provided for supporting risk evaluation and modification of an executable codified infrastructure. The codified infrastructure is analyzed to identify any non-native program instructions. A selection of the identified non-native program instructions are combined and subjected to a risk evaluation by non-native tools. A risk evaluation result is mapped to corresponding lines of the source code, and a risk identifier is assigned to the corresponding lines of the source code. One or more modifications are selectively applied to the codified infrastructure in correspondence with the assigned risk identifier. The applied modification mitigates any defects in the source code.

Abstract: Reconstituting a machine image separates constituent parts of a machine image, and for each part, determines whether an exact version of the part is available on the target machine. If an exact version of the part is not available on the target machine, an inexact part is looked for on the target machine. Whether an inexact part is found may be determined based on attribute policy specification and similarity computation. For the inexact part found on the target machine, any dependencies may be identified and processed as a part to be reconstituted for the machine image. If no exact part and no inexact part are found on the target machine, the part is transferred from a source machine to the target machine. A machine image is created based on parts.

Abstract: A system, computer program product, and method are provided for supporting risk evaluation and modification of an executable codified infrastructure. The codified infrastructure is analyzed to identify any non-native program instructions. A selection of the identified non-native program instructions are combined and subjected to a risk evaluation by non-native tools. A risk evaluation result is mapped to corresponding lines of the source code, and a risk identifier is assigned to the corresponding lines of the source code. One or more modifications are selectively applied to the codified infrastructure in correspondence with the assigned risk identifier. The applied modification mitigates any defects in the source code.

Abstract: A system, computer program product, and method are provided for dynamically orchestrating a prerequisite driven codified infrastructure. A prerequisite defines deployment of one or more resources in the codified infrastructure through the use of a codified logic expression. One or more probes are created and leveraged to monitor a dynamic resource state for one or more prerequisites. A new codified infrastructure is generated based on the change(s) to the monitored dynamic resource state for the one or more prerequisites. The identified one or more changes are applied to the new codified infrastructure and the new codified infrastructure is executed based on the applied change(s).

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: A system, computer program product, and method are provided for dynamically orchestrating a prerequisite driven codified infrastructure. A prerequisite defines deployment of one or more resources in the codified infrastructure through the use of a codified logic expression. One or more probes are created and leveraged to monitor a dynamic resource state for one or more prerequisites. A new codified infrastructure is generated based on the change(s) to the monitored dynamic resource state for the one or more prerequisites. The identified one or more changes are applied to the new codified infrastructure and the new codified infrastructure is executed based on the applied change(s).

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: Managing performing patches on a workload associated with a computing platform comprising: identify the workload associated with the computing platform, wherein the workload can comprise a first workload portion upon which a first subset of patches can be performed offline and a second workload portion upon which a second subset of patches can be performed online; determine, for the first workload portion, a portion of the first subset of patches that can be performed within a maintenance time window while offline based on vulnerability scores of patches of the first subset of patches, and determine, for the second workload portion, the second subset of patches that can be performed while online; and determine the vulnerability scores of the patches of the first subset of patches based on importance levels of the patches.

Abstract: Managing performing patches on a workload associated with a computing platform comprising: identify the workload associated with the computing platform, wherein the workload can comprise a first workload portion upon which a first subset of patches can be performed offline and a second workload portion upon which a second subset of patches can be performed online; determine, for the first workload portion, a portion of the first subset of patches that can be performed within a maintenance time window while offline based on vulnerability scores of patches of the first subset of patches, and determine, for the second workload portion, the second subset of patches that can be performed while online; and determine the vulnerability scores of the patches of the first subset of patches based on importance levels of the patches.