Abstract: Protecting and restoring data within a networked (e.g. cloud) storage computing environment through asynchronous replication and remote backup of data and its associated metadata. Data backup and recovery functionality provides data backups by detecting incremental updates to the data and its associated metadata at specific points in time determined by policies. The policies are configurable based on user requirements. Multiple copies of the data backups can be made and stored in separate compressed files at backup/disaster recovery locations. The backups of data and its associated metadata, which includes file system configuration information can be used to restore the state of a computer file system to that of a given point-in-time. Accordingly, a data protection approach is disclosed for protecting data at both the file system level and application level.

Abstract: Embodiments of the present invention provide approaches (e.g., online methods) to analyze end-to-end performance issues in a multi-tier enterprise storage system (ESS), such as a storage cloud, where data may be distributed across multiple storage components. Specifically, performance and configuration data from different storage components (e.g., nodes) is collected and analyzed to identify nodes that are becoming (or may become) performance bottlenecks. In a typical embodiment, a set of components distributed among a set of tiers of an ESS is identified. For each component, a total capacity and a current load are determined. Based on these values, a utilization of each component is determined. Comparison of the utilization with a predetermined threshold and/or analysis of historical data allows one or more components causing a bottleneck to be identified.

Abstract: Embodiments of the present invention provide an approach for protecting and restoring data within a networked (e.g. cloud) storage computing environment through asynchronous replication and remote backup of data and its associated metadata. Under embodiments of the present invention, data backup and recovery functionality provides data backups by detecting incremental updates to the data and its associated metadata at specific points in time determined by policies. The policies are configurable based on user requirements. Multiple copies of the data backups can be made and stored in separate compressed files at backup/disaster recovery locations. The backups of data and its associated metadata, which includes file system configuration information can be used to restore the state of a computer file system to that of a given point-in-time. Accordingly, a data protection approach is disclosed for protecting data at both the file system level and application level.

Abstract: Embodiments of the present invention provide an approach for protecting and restoring data within a networked (e.g. cloud) storage computing environment through asynchronous replication and remote backup of data and its associated metadata. Under embodiments of the present invention, data backup and recovery functionality provides data backups by detecting incremental updates to the data and its associated metadata at specific points in time determined by policies. The policies are configurable based on user requirements. Multiple copies of the data backups can be made and stored in separate compressed files at backup/disaster recovery locations. The backups of data and its associated metadata, which includes file system configuration information can be used to restore the state of a computer file system to that of a given point-in-time. Accordingly, a data protection approach is disclosed for protecting data at both the file system level and application level.

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: Embodiments of the present invention provide approaches (e.g., online methods) to analyze end-to-end performance issues in a multi-tier enterprise storage system (ESS), such as a storage cloud, where data may be distributed across multiple storage components. Specifically, performance and configuration data from different storage components (e.g., nodes) is collected and analyzed to identify nodes that are becoming (or may become) performance bottlenecks. In a typical embodiment, a set of components distributed among a set of tiers of an ESS is identified. For each component, a total capacity and a current load are determined. Based on these values, a utilization of each component is determined. Comparison of the utilization with a predetermined threshold and/or analysis of historical data allows one or more components causing a bottleneck to be identified.

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 of the present invention provide approaches (e.g., online methods) to analyze end-to-end performance issues in a multi-tier enterprise storage system (ESS), such as a storage cloud, where data may be distributed across multiple storage components. Specifically, performance and configuration data from different storage components (e.g., nodes) is collected and analyzed to identify nodes that are becoming (or may become) performance bottlenecks. In a typical embodiment, a set of components distributed among a set of tiers of an ESS is identified. For each component, a total capacity and a current load are determined. Based on these values, a utilization of each component is determined. Comparison of the utilization with a predetermined threshold and/or analysis of historical data allows one or more components causing a bottleneck to be identified.

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: Embodiments of the present invention provide an approach for protecting and restoring data within a networked (e.g. cloud) storage computing environment through asynchronous replication and remote backup of data and its associated metadata. Under embodiments of the present invention, data backup and recovery functionality provides data backups by detecting incremental updates to the data and its associated metadata at specific points in time determined by policies. The policies are configurable based on user requirements. Multiple copies of the data backups can be made and stored in separate compressed files at backup/disaster recovery locations. The backups of data and its associated metadata, which includes file system configuration information can be used to restore the state of a computer file system to that of a given point-in-time. Accordingly, a data protection approach is disclosed for protecting data at both the file system level and application level.

Abstract: A method, system, and program product are provided for protecting and restoring data within a networked (e.g. cloud) storage computing environment through asynchronous replication and remote backup of data and its associated metadata. Data backup and recovery functionality provides data backups by detecting incremental updates to the data and its associated metadata at specific points in time determined by policies. The policies are configurable based on user requirements. Multiple copies of the data backups can be made and stored in separate compressed files at backup/disaster recovery locations. The backups of data and its associated metadata, which includes file system configuration information can be used to restore the state of a computer file system to that of a given point-in-time. Accordingly, a data protection approach is disclosed for protecting data at both the file system level and application level.

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 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: 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 approaches (e.g., online methods) to analyze end-to-end performance issues in a multi-tier enterprise storage system (ESS), such as a storage cloud, where data may be distributed across multiple storage components. Specifically, performance and configuration data from different storage components (e.g., nodes) is collected and analyzed to identify nodes that are becoming (or may become) performance bottlenecks. In a typical embodiment, a set of components distributed among a set of tiers of an ESS is identified. For each component, a total capacity and a current load are determined. Based on these values, a utilization of each component is determined. Comparison of the utilization with a predetermined threshold and/or analysis of historical data allows one or more components causing a bottleneck to be identified.

Abstract: Embodiments of the present invention provide an approach for protecting and restoring data within a networked (e.g. cloud) storage computing environment through asynchronous replication and remote backup of data and its associated metadata. Under embodiments of the present invention, data backup and recovery functionality provides data backups by detecting incremental updates to the data and its associated metadata at specific points in time determined by policies. The policies are configurable based on user requirements. Multiple copies of the data backups can be made and stored in separate compressed files at backup/disaster recovery locations. The backups of data and its associated metadata, which includes file system configuration information can be used to restore the state of a computer file system to that of a given point-in-time. Accordingly, a data protection approach is disclosed for protecting data at both the file system level and application level.

Abstract: Embodiments of the present invention provide an approach for automatic storage planning and provisioning within a clustered computing environment (e.g., a cloud computing environment). Specifically, embodiments of the present invention will receive planning input for a set of storage area network volume controllers (SVCs) within the clustered computing environment, the planning input indicating a potential load on the SVCs and its associated components. Along these lines, analytical models (e.g., from vendors) can be also used that allow for a load to be accurately estimated on the storage components. Regardless, 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.

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 to control access to logical volumes. The method provides a plurality of host computers and an information storage and retrieval system comprising a plurality of logical volumes. The method forms (N) host computer groups, assigns one or more of the host computers to the (i)th host computer group, such that each host computer assigned to (i)th host computer group is not assigned to any other of the (N) host computer groups. The method forms (N) logical volume groups, assigns one or more logical volumes to the (i)th logical volume group, such that each of the logical volumes assigned to the (i)th logical volume group is not assigned to any other of the (N) logical volume groups. The method maintains a database associating the (i)th host computer group with the (i)th logical volume group, and permits a host computer assigned to the (i)th host computer group to access a logical volume assigned to the (i)th logical volume group.

Abstract: Method and apparatus for limiting access to control units channel-connected to host computers through a FICON switch. The control unit port of the FICON switch receives a requests from an attached host to provide configuration information regarding the switch ports. The control unit port deliberately misrepresents the port configuration of the switch by reporting that ports outside the zone of the requesting host are not installed, even though these ports may in fact be installed for use by other hosts that belong to other zones.