Abstract: Embodiments of the present invention provide an approach for intelligent storage planning and planning within a clustered computing environment (e.g., a cloud computing environment). Specifically, embodiments of the present invention will first determine/identify a set of storage area network volume controllers (SVCs) that is accessible from a host that has submitted a request for access to storage. Thereafter, a set of managed disk (mdisk) groups (i.e., corresponding to the set of SVCs) that are candidates for satisfying the request will be determined. This set of mdisk groups will then be filtered based on available space therein, a set of user/requester preferences, and optionally, a set of performance characteristics. Then, a particular mdisk group will be selected from the set of mdisk groups based on the filtering.

Abstract: A method for determining that first and second virtual machines, that currently execute in first and second host computing systems, respectively, should both execute within a same host computing system. The method includes determining that the first and second virtual machines have accessed same data more often than a third and fourth virtual machines have accessed said same data. Based in part on this determination, the method includes determining that the first and second virtual machines should execute in a same host computing system having a same cache memory for both the first and second virtual machines and that the third and fourth virtual machines should execute on one or more different host computing systems than said same host computing system.

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: Embodiments of the present invention provide an approach that utilizes discrete event simulation to quantitatively analyze the reliability of a modeled de-duplication system in a computer storage environment. In addition, the approach described herein can perform such an analysis on systems having heterogeneous data stored on heterogeneous storage systems in the presence of primary faults and their secondary effects due to de-duplication. In a typical embodiment, data de-duplication parameters and a hardware configuration are received in a computer storage medium. A data de-duplication model is then applied to a set of data and to the data de-duplication parameters, and a hardware reliability model is applied to the hardware configuration. Then a set (at least one) of discrete events is simulated based on the data de-duplication model as applied to the set of data and the data de-duplication parameters, and the hardware reliability model as applied to the hardware configuration.

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

Abstract: An existing data protection environment is analyzed to determine a plurality existing infrastructure containers. A popular infrastructure container is identified from the plurality of existing infrastructure containers. Responsive to determining that the popular infrastructure container does not exist within a central repository, the restore container is created within the central repository to match the popular infrastructure container.

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 discussed in this disclosure provide an integrated provisioning framework that automates the process of provisioning storage resources, end-to-end, for an enterprise storage cloud environment. Such embodiments configure and orchestrate the deployment of a user's workload and, at the same time, provide optimization across a multitude of storage cloud resources. Along these lines, input is received in the form of workload requirements and configuration information for available system resources. Based on the input, a set (at least one) of storage cloud configuration plans is developed that satisfy the workload requirements. A set of scripts is then generated that orchestrate the deployment and configuration of different software and hardware components based on the plans.

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: 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: 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: Embodiments of the present invention provide an approach for intelligent storage planning and planning within a clustered computing environment (e.g., a cloud computing environment). Specifically, embodiments of the present invention will first determine/identify a set of storage area network volume controllers (SVCs) that is accessible from a host that has submitted a request for access to storage. Thereafter, a set of managed disk (mdisk) groups (i.e., corresponding to the set of SVCs) that are candidates for satisfying the request will be determined. This set of mdisk groups will then be filtered based on available space therein, a set of user/requester preferences, and optionally, a set of performance characteristics. Then, a particular mdisk group will be selected from the set of mdisk groups based on the filtering.

Abstract: A system and associated method for continuously optimizing data archive management scheduling. A job scheduler receives, from an archive management system, inputs of task information, replica placement data, infrastructure topology data, and resource performance data. The job scheduler models a flow network that represents data content, software programs, physical devices, and communication capacity of the archive management system in various levels of vertices according to the received inputs. An optimal path in the modeled flow network is computed as an initial schedule, and the archive management system performs tasks according to the initial schedule. The operations of scheduled tasks are monitored and the job scheduler produces a new schedule based on feedbacks of the monitored operations and predefined heuristics.

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: An approach for automatic storage planning and provisioning within a clustered computing environment is provided. Planning input for a set of storage area network volume controllers (SVCs) will be received within the clustered computing environment, the planning input indicating a potential load on the SVCs and its associated components. Analytical models (e.g., from vendors) can be also used that allow for a load to be accurately estimated on the storage 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: Embodiments of the present invention provide an approach for intelligent storage planning and planning within a clustered computing environment (e.g., a cloud computing environment). Specifically, embodiments of the present invention will first determine/identify a set of storage area network volume controllers (SVCs) that is accessible from a host that has submitted a request for access to storage. Thereafter, a set of managed disk (mdisk) groups (i.e., corresponding to the set of SVCs) that are candidates for satisfying the request will be determined. This set of mdisk groups will then be filtered based on available space therein, a set of user/requester preferences, and optionally, a set of performance characteristics. Then, a particular mdisk group will be selected from the set of mdisk groups based on the filtering.

Abstract: Embodiments discussed in this disclosure provide an integrated provisioning framework that automates the process of provisioning storage resources, end-to-end, for an enterprise storage cloud environment. Such embodiments configure and orchestrate the deployment of a user's workload and, at the same time, provide optimization across a multitude of storage cloud resources. Along these lines, input is received in the form of workload requirements and configuration information for available system resources. Based on the input, a set (at least one) of storage cloud configuration plans is developed that satisfy the workload requirements. A set of scripts is then generated that orchestrate the deployment and configuration of different software and hardware components based on the plans.

Abstract: Embodiments of the present invention provide an approach that utilizes discrete event simulation to quantitatively analyze the reliability of a modeled de-duplication system in a computer storage environment. In addition, the approach described herein can perform such an analysis on systems having heterogeneous data stored on heterogeneous storage systems in the presence of primary faults and their secondary effects due to de-duplication. In a typical embodiment, data de-duplication parameters and a hardware configuration are received in a computer storage medium. A data de-duplication model is then applied to a set of data and to the data de-duplication parameters, and a hardware reliability model is applied to the hardware configuration. Then a set (at least one) of discrete events is simulated based on the data de-duplication model as applied to the set of data and the data de-duplication parameters, and the hardware reliability model as applied to the hardware configuration.