Abstract: An approach for managing replicated data is presented. A current usage of resources in a system and a threshold usage of the resources are determined. Based on inter-replica correlation(s) and inter-data correlation(s) specified by a unified replication metadata model, a proper subset of replicas included in a plurality of replicas is indexed by (i) if the current usage is less than the threshold usage, determining an expected additional resource usage due to performing an indexing task online and based on the expected additional resource usage, determining a resource affinity score for performing the indexing task online, or (ii) if the current usage is greater than or equal to the threshold usage, determining an expected resource usage due to performing the indexing task offline and based on the expected resource usage, determining a resource affinity score for performing the indexing task offline.

Abstract: Embodiments of the disclosure relate to automatic task execution on a mobile device. Aspects include identifying, by a processor, one or more hardware resources on the mobile device, identifying one or more patterns for each of the one or more hardware resources, and generating one or more composite patterns recognizable by the mobile device based on the one or more patterns. Aspects further include identifying one or more operations that can be performed via the mobile device and receiving, via a user-interface, an association between at least one of the one or more patterns and the one or more composite patterns and an execution of at least one of the one or more operations.

Abstract: Embodiments relate to impact-based migration scheduling. An aspect includes identifying, by a migration scheduling module, a plurality of feasible destination storage devices for a migration session. Another aspect includes determining, for each of the identified destination storage devices, an impact of the migration session during the migration session. Another aspect includes determining, for each of the identified destination storage devices, an impact of the migration session after the migration session is completed. Another aspect includes selecting a destination storage device from the identified destination storage devices based on the impact of the migration session during the migration session and the impact of the migration session after the migration session is completed. Another aspect includes determining a start time and transfer rate for the migration session based on the selected destination storage device.

Abstract: A method for scheduling one or more backup jobs among backup clients with a common backup window. The method includes collecting and recording historical information pertaining to the backup clients with the common backup window. An application then estimates the number of open files during the common backup window based on the historical information and infers an impact of skipping a backup of the open files during the common backup window, where the impact is inferred from the historical information according to one or more predetermined criteria. The estimated number of open files and the inferred impact of skipping the backup of the estimated number of open files during the common backup window are then combined according to a predetermined cost function. The order of the backup jobs for the backup clients during the common backup window is then scheduled to reduce the predetermined cost function.

Abstract: Operational characteristic I/O patterns of each storage volume of a storage volume group, and at least one operational characteristic of each storage volume associated with an application, are determined. Unsupervised learning is used to form clusters of storage volumes of similar characteristics. Labels are generated, assigned, and aggregated for each characteristic of each storage volume. Templates are received that includes labels of storage volume characteristics of known application types. An application type associated with the storage volume group is inferred, based on a best-fit match of the aggregate labels of the storage volumes of the storage volume group to the labels included in the templates of storage volume characteristics of known application types.

Abstract: Embodiments relate to container migration and provisioning. An aspect includes receiving a request to migrate a composite application to a container-based environment. Another aspect includes determining a plurality of software components that make up the composite application. Another aspect includes determining communications patterns between the plurality of software components. Another aspect includes determining a containerization plan for the composite application based on the determined communications patterns. Another aspect includes creating a plurality of containers, and communications channels between the plurality of containers, for the software components of the composite application based on the containerization plan.

Abstract: An approach for managing replicated data is presented. Metadata is received specifying inter-data correlation(s), inter-replica correlation(s), and data-replica correlation(s) among replicas generated for a system. A unified replication metadata model specifying the correlations is generated. Based on the inter-replica correlation(s), a proper subset of the replicas is selected. Based on the inter-replica and inter-data correlation(s), the selected proper subset of replicas is indexed to generate a unified content index. Based on a current usage of resources, an expected usage and an affinity score for performing an indexing task online or offline are determined. A query is received to locate a data item in at least one of the replicas. Based on the unified content index, the unified replication metadata model, and the query, candidate replica(s) and confidence score(s) indicating likelihood(s) that the candidate replica(s) include the data item are determined.

Abstract: Methods, systems, and articles of manufacture for networking-assisted I/O order preservation for data replication are provided herein. A method includes identifying, from one or more devices at a primary site, (i) each of multiple data changes and (ii) input/output chronological order information associated with the multiple data changes; determining one or more forwarding rules to be implemented in connection with delivering a command associated with each of said multiple data changes to one or more devices at a secondary site, wherein said forwarding rules are based on said input/output chronological order information; and delivering the command associated with each of said multiple data changes to one or more devices at the secondary site in accordance with said one or more forwarding rules to maintain a chronological order of the multiple data changes from the primary site to the secondary site.

Abstract: Embodiments of the disclosure relate to automatic task execution on a mobile device. Aspects include identifying, by a processor, one or more hardware resources on the mobile device, identifying one or more patterns for each of the one or more hardware resources, and generating one or more composite patterns recognizable by the mobile device based on the one or more patterns. Aspects further include identifying one or more operations that can be performed via the mobile device and receiving, via a user-interface, an association between at least one of the one or more patterns and the one or more composite patterns and an execution of at least one of the one or more operations.

Abstract: Provided are a method, system and program for publishing configuration patterns for storage resources and storage performance models from client systems to share with client systems in a network computing environment. A determination is made of a configuration pattern of storage resources within the client computing environment and storage performance metrics for the configuration pattern of storage resources within the client. At least one storage performance model is created based on the determined storage performance metrics. Information on the at least one storage performance model and the configuration pattern of the storage resources for which the storage performance metrics were determined are published to the service provider.

Abstract: Provided are a computer program product, method, and system for providing information on published configuration patterns of storage resources to client systems in a network computing environment. Published configuration patterns of storage resources and storage performance models for the published configuration patterns are received from the client systems and stored in a catalog. The published storage performance models are based on storage performance and workloads realized at the configuration patterns of the storage resources. A determination is made from the catalog of at least one published configuration pattern that is similar to a requested configuration pattern requested by a client system according to at least one storage attribute. A result set is generated indicating the determined at least one published configuration pattern and the storage performance model and sent to the requesting client system.

Abstract: Provided are a method, system and program for requesting storage performance models for a configuration pattern of storage resources to deploy at a client computing environment. A determination is made of a new configuration pattern of storage resources to deploy. A request is sent to a service provider with information on the new configuration pattern. A result set is received from the service provider having at least one provided configuration pattern having a degree of similarity to the new configuration pattern and a storage performance model for each of the provided configuration patterns. Each of the storage performance models indicate workload and performance characteristics for one of the provided configuration patterns. One of the provided configuration patterns is selected from the result set and the storage performance model for the selected configuration pattern is used to model performance at the client.

Abstract: Provided are a method, system and program for requesting storage performance models for a configuration pattern of storage resources to deploy at a client computing environment. A determination is made of a new configuration pattern of storage resources to deploy. A request is sent to a service provider with information on the new configuration pattern. A result set is received from the service provider having at least one provided configuration pattern having a degree of similarity to the new configuration pattern and a storage performance model for each of the provided configuration patterns. Each of the storage performance models indicate workload and performance characteristics for one of the provided configuration patterns. One of the provided configuration patterns is selected from the result set and the storage performance model for the selected configuration pattern is used to model performance at the client.

Abstract: Provided are a computer program product, method, and system for providing information on published configuration patterns of storage resources to client systems in a network computing environment. Published configuration patterns of storage resources and storage performance models for the published configuration patterns are received from the client systems and stored in a catalog. The published storage performance models are based on storage performance and workloads realized at the configuration patterns of the storage resources. A determination is made from the catalog of at least one published configuration pattern that is similar to a requested configuration pattern requested by a client system according to at least one storage attribute. A result set is generated indicating the determined at least one published configuration pattern and the storage performance model and sent to the requesting client system.

Abstract: Provided are a method, system and program for publishing configuration patterns for storage resources and storage performance models from client systems to share with client systems in a network computing environment. A determination is made of a configuration pattern of storage resources within the client computing environment and storage performance metrics for the configuration pattern of storage resources within the client. At least one storage performance model is created based on the determined storage performance metrics. Information on the at least one storage performance model and the configuration pattern of the storage resources for which the storage performance metrics were determined are published to the service provider.

Abstract: A computer identifies a plurality of data retrieval requests that may be serviced using a plurality of unique data chunks. The computer services the data retrieval requests by utilizing at least one of the unique data chunks. At least one of the unique data chunks can be utilized for servicing two or more of the data retrieval requests. The computer determines a servicing sequence for the plurality of data retrieval requests such that the two or more of the data retrieval requests that can be serviced utilizing the at least one of the unique data chunks are serviced consecutively. The computer services the plurality of data retrieval requests according to the servicing sequence.

Abstract: A solution for validating a set of data protection solutions is provided. A validation scenario can be defined, which can include data corresponding to a set of attributes for the validation scenario. The attributes can include a time frame for the validation scenario. The validation scenario also can include a set of backup images to be validated, each of which is generated using one of the set of data protection solutions. The set of backup images can be identified using the time frame. A set of resource requirements for implementing the validation scenario can be determined based on the set of backup images and the set of attributes for the validation scenario.

Abstract: Embodiments relate to impact-based migration scheduling. An aspect includes identifying, by a migration scheduling module, a plurality of feasible destination storage devices for a migration session. Another aspect includes determining, for each of the identified destination storage devices, an impact of the migration session during the migration session. Another aspect includes determining, for each of the identified destination storage devices, an impact of the migration session after the migration session is completed. Another aspect includes selecting a destination storage device from the identified destination storage devices based on the impact of the migration session during the migration session and the impact of the migration session after the migration session is completed. Another aspect includes determining a start time and transfer rate for the migration session based on the selected destination storage device.

Abstract: Embodiments relate to cross-domain service request placement in a software defined environment (SDE). An aspect includes receiving a service request corresponding to a job to be completed in the SDE. Another aspect includes determining a first computer device in a first domain, and a second computer device in a second domain, that are capable of performing the service request. Another aspect includes determining, for the first and second computer devices, first and second pluralities of available service classes. Another aspect includes determining, for the first and second computer devices, a first and second plurality of costs of performing the service request, wherein each of the first and second plurality of costs corresponds to a single respective service class. Yet another aspect includes selecting one of the first computer device and the second computer device to perform the service request based on the first and second plurality of costs.

Abstract: Operational characteristic I/O patterns of each storage volume of a storage volume group, and at least one operational characteristic of each storage volume associated with an application, are determined. Unsupervised learning is used to form clusters of storage volumes of similar characteristics. Labels are generated, assigned, and aggregated for each characteristic of each storage volume. Templates are received that includes labels of storage volume characteristics of known application types. An application type associated with the storage volume group is inferred, based on a best-fit match of the aggregate labels of the storage volumes of the storage volume group to the labels included in the templates of storage volume characteristics of known application types.