Abstract: A computer-implemented method includes receiving a template for creating a virtual machine (VM) instance; separating the template into a repeated portion and a unique portion; determining whether the repeated portion is stored in a cache; creating based on determining that the repeated portion is stored in the cache, the VM instance using the repeated portion stored in the cache; completing the unique portion of the VM instance to create a completed VM instance; and deploying the completed VM instance.

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 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: A mechanism is provided for implementing operation optimization. Responsive to a request to load data via a input/output (I/O) load operation, prior to loading the data, transformed data in a local memory is searched for a match to the requested data. Responsive to identifying transformed that matches the requested data, the transformed data in the local memory is used to fulfill the request. Responsive to failing to identify transformed data in the local memory that matches the requested data, untransformed data in the local memory is searched for a match to the requested data. Responsive to identifying untransformed data that matches the requested data, the untransformed data in the local memory is used to fulfill the request. Responsive to failing to identify untransformed data in the local memory that matches the requested data, the requested data may be loaded from the storage system via the I/O load operation.

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 mechanism is provided for implementing operation optimization. Responsive to a request to load data via a input/output (I/O) load operation, prior to loading the data, transformed data in a local memory is searched for a match to the requested data. Responsive to identifying transformed that matches the requested data, the transformed data in the local memory is used to fulfill the request. Responsive to failing to identify transformed data in the local memory that matches the requested data, untransformed data in the local memory is searched for a match to the requested data. Responsive to identifying untransformed data that matches the requested data, the untransformed data in the local memory is used to fulfill the request. Responsive to failing to identify untransformed data in the local memory that matches the requested data, the requested data may be loaded from the storage system via the I/O load operation.

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: 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 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: 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: 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: Methods and systems for data transfer include adding a data chunks to a priority queue in an order based on utilization priority. A reducibility score for the data chunks is determined. A data reduction operation is performed on a data chunk having a highest reducibility in the priority queue using a processor if sufficient resources are available. The data chunk having the lowest reducibility score is moved from the priority queue to a transfer queue for transmission if the transfer queue is not full.

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: 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 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: Managing data in a cloud computing environment, including data transfers. File level and block level similarities are identified, including for archive and nested archive files, residing on datacenters and regional repositories. A replication plan is generated based on receiving a replication instruction, and further based on similarity clusters by transferring unique data blocks and files from best available sources including regional repositories.

Abstract: A method and system of assigning computing resources of a cloud by an orchestration engine is provided. A workload request is received via a network. A blueprint is extracted from the workload request. Milestones associated with the blueprint are identified. Business rules associated with the blueprint are determined. A cost of each of the identified milestones is determined. Upon determining that there is interdependence between at least some of the identified milestones, a group of milestones that are interdependent is created. The milestones are ranked based on the determined business rules and determined cost. A deployment plan is executed based on the ranked milestones.