Abstract: Embodiments of the present invention provide an approach for providing cluster-aware (storage) resource provisioning in a networked computing environment (e.g., a cloud computing environment) based upon policies, best practices, and/or storage cluster/environment configurations. In a typical embodiment, a set of characteristics (e.g., computing resources/components, etc.) of a storage environment will be determined. A set of requirements for a set of workloads to be processed by the components of the storage environment will then be identified. A set of policies and a set of best practices will then be determined to identify a configuration of the storage environment to optimize the processing of the set of workloads according to the set of requirements. Based on the configuration, a plan will be generated that indicates a data path through the set of computing resources that minimizes a potential for error in processing the set of workloads.

Abstract: A method and associated system for continuously optimizing data archive management scheduling. A flow network is modeled, which creates vertexes organized in multiple levels and creating multiple edges sequentially connecting the vertexes of the multiple levels. The multiple levels consist of N+1 levels denoted as LEVEL0, LEVEL1, . . . , LEVELN, wherein N is at least 2, wherein LEVEL0 includes a source vertex, wherein LEVEL1 includes at least one task vertex associated with at least one archive management task, wherein EDGEK is directed from the vertex of LEVELK-1 to the vertex of LEVELK for K=1, . . . , N, wherein EDGE1 indicates that a first task associated with a first task vertex of LEVEL1 is scheduled by a job scheduler, and wherein EDGE1 has a first weight equal to a time unit to perform the first task.

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: A method and associated system for continuously optimizing data archive management scheduling. A flow network is modeled. The flow network represents data content, software programs, physical devices, and communication capacity of the archive management system in various levels of vertices such that an optimal path in the flow network from a task of at least one archive management task to a worker program of the archive management system represents an optimal initial schedule for the worker program to perform the task.

Abstract: A solution for managing a service engagement is provided. A service delivery model for the service engagement is defined within an engagement framework. The engagement framework, and consequently the service delivery model, can include a hierarchy that comprises a service definition, a set of service elements for the service definition, and a set of element tasks for each service element. The set of element tasks can be selected from a set of base tasks, each of which defines a particular task along with its input(s), output(s), and related asset(s). As a result, service engagements can be managed in a consistent manner using a data structure that promotes reuse and is readily extensible.

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: An approach for managing and optimizing available resources utilized in a networked computing environment (e.g., a cloud computing environment) is provided. In one aspect, a plurality of resources in a plurality of storage pools within the networked computing environment are monitored. Each storage pool is running at least one application workload using a corresponding subset of the plurality of resources. The storage pools are ranked based on the application workloads. In response to an over-utilized storage pool and/or an under-utilized storage pool, the plurality of resources are dynamically reconfigured.

Abstract: Embodiments of the present invention provide a hybrid (e.g., local and remote) approach for data backup in a networked computing environment (e.g., a cloud computing environment). In a typical embodiment, a set of storage configuration parameters corresponding to a set of data to be backed up is received and stored in a computer data structure. The set of storage configuration parameters can comprise at least one of the following: a recovery time objective (RTO), a recovery point objective (RPO), and a desired type of protection for the set of data. Regardless, the set of data is compared to previously stored data to identify at least one of the following: portions of the set of data that have commonality with the previously stored data; and portions of the set of data that are unique to the set of data (i.e., not in common with any of the previously stored data). The above-described process is referred to herein as “de-duplication”.

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. 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. Based on temporal distance(s) and percent change(s) between first and second replica(s), confidence score(s) of the second replica(s) are determined.

Abstract: Embodiments of the present invention provide an integrated host and subsystem port selection methodology that uses performance measurements combined with information about active data paths. This technique also helps in resilient fabric planning by selecting ports from redundant fabrics. In a typical embodiment, host port to storage port pairs that create a path between a host and a storage device will be identified. From these pairs, a set of host port to storage port candidates for communicate data from the host to the storage device will be identified based on a set of resiliency constraints. Then, a specific host port to storage port pair will be selected from the set based on a lowest joint workload measurement. A path will then be created between the specific host port and storage port, and data will be communicated from the host to the storage device via the path.

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: The present invention provides an approach for automatic storage planning and provisioning within a clustered computing environment (e.g., a cloud computing environment). The present invention will receive planning input for a set of storage area network volume controllers (SVCs), the planning input indicating a potential load on the SVCs and its associated components. Configuration data for a set of storage components (i.e., the set of SVCs, a set of managed disk (Mdisk) groups associated with the set of SVCs, and a set of backend storage systems) will also be collected. Based on this configuration data, the set of storage components will be filtered to identify candidate storage components capable of addressing the potential load. Then, performance data for the candidate storage components will be analyzed to identify an SVC and an Mdisk group to address the potential load.

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. 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. Based on temporal distance(s) and percent change(s) between first and second replica(s), confidence score(s) of the second replica(s) are determined.

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 of the present invention provide lifecycle storage management for data within a Cloud computing environment. Specifically, a set of policies can be defined that allow for automatic valuation of the data and migration of the data between a set of storage tiers. Before a policy set is deployed, it can be assessed to determine effects it will have on cost, performance, and data location. Based on data characteristics and access patterns, a set of policy recommendations can be provided that predict the value of the data over time, and offer an improved migration strategy for moving the data between the set of storage tiers as the value of the data changes.

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. 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 corresponding confidence score(s) are determined. The confidence score(s) indicate respective likelihood(s) that the candidate replica(s) include the data item.

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 is 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 are 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: Embodiments of the present invention provide an approach for providing non-disruptive transitioning of application replication configurations and proactive analysis of possible error scenarios. Specifically, under embodiments of the present invention, a common integration model (CIM)-compatible representation of a system replication plan is provided in a computer data structure. Based on the representation, a hierarchical tree data structure having a set of nodes is created. A set of system configuration updates pertaining to the set of nodes are then classified (e.g., based upon the type of configuration update). Once the set of nodes has been classified, the set of nodes may then be analyzed to determine if any nodes of the set are isomorphic. If so, the plan can be modified accordingly. In any event, the replication plan (or modified replication plan) may then be implemented.

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: Embodiments of the present invention provide a hybrid (e.g., local and remote) approach for data backup in a networked computing environment (e.g., a cloud computing environment). In a typical embodiment, a set of storage configuration parameters corresponding to a set of data to be backed up is received and stored in a computer data structure. The set of storage configuration parameters can comprise at least one of the following: a recovery time objective (RTO), a recovery point objective (RPO), and a desired type of protection for the set of data. Regardless, the set of data is compared to previously stored data to identify at least one of the following: portions of the set of data that have commonality with the previously stored data; and portions of the set of data that are unique to the set of data (i.e., not in common with any of the previously stored data). The above-described process is referred to herein as “de-duplication”.