Abstract: A secondary storage controller determines one or more tracks of one or more volumes in which data loss has occurred in the secondary storage controller. The secondary storage controller suspends a peer to peer remote copy operation between the secondary storage controller and a primary storage controller. Information on the one or more tracks of the one or more volumes in which the data loss has occurred is transmitted to the primary storage controller.

Abstract: A computer-implemented method according to one embodiment includes identifying a request to migrate data associated with a volume from a first storage pool to a second storage pool; identifying entries in a first table corresponding to rank extents in the first storage pool containing the data; allocating and synchronizing a plurality of second tables for the identified entries of the first table that are located in the volume; transferring the data associated with the volume from the rank extents in the first storage pool containing the data to one or more rank extents in the one or more ranks of the second storage pool; and updating the second tables to correspond to the transferred data in the one or more rank extents in the one or more ranks of the second storage pool.

Abstract: A secondary storage controller determines one or more tracks of one or more volumes in which data loss has occurred in the secondary storage controller. The secondary storage controller suspends a peer to peer remote copy operation between the secondary storage controller and a primary storage controller. Information on the one or more tracks of the one or more volumes in which the data loss has occurred is transmitted to the primary storage controller.

Abstract: A computer-implemented method according to one embodiment includes identifying a request to migrate data associated with a volume from a source storage pool having a first rank extent size to a destination storage pool having a second rank extent size smaller than the first rank extent size, creating a correspondence between logical volume extents of the volume and physical offset locations within rank extents of the source storage pool, and migrating data from one or more ranks of the source storage pool to one or more ranks of the destination storage pool, utilizing the correspondence between the logical volume extents of the volume and the physical offset locations within the rank extents of the source storage pool.

Abstract: A computer-implemented method according to one embodiment includes identifying a request to migrate data associated with a volume from a first storage pool to a second storage pool; identifying entries in a first table corresponding to rank extents in the first storage pool containing the data; allocating and synchronizing a plurality of second tables for the identified entries of the first table that are located in the volume; transferring the data associated with the volume from the rank extents in the first storage pool containing the data to one or more rank extents in the one or more ranks of the second storage pool; and updating the second tables to correspond to the transferred data in the one or more rank extents in the one or more ranks of the second storage pool.

Abstract: A secondary storage controller determines one or more tracks of one or more volumes in which data loss has occurred in the secondary storage controller. The secondary storage controller suspends a peer to peer remote copy operation between the secondary storage controller and a primary storage controller. Information on the one or more tracks of the one or more volumes in which the data loss has occurred is transmitted to the primary storage controller.

Abstract: A method for reducing incidents of data loss in redundant arrays of independent disks (RAIDs) having the same RAID level is disclosed. In one embodiment, such a method identifies, in a data storage environment, a set of RAIDs having a common RAID level. The method also identifies, in the set of RAIDs, higher risk storage drives having a failure risk above a threshold and lower risk storage drives having a failure risk below the threshold. The method swaps, within the RAIDs, higher risk storage drives with lower risk storage drives to more evenly distribute higher risk storage drives across the RAIDs. A corresponding system and computer program product are also disclosed.

Abstract: A method according to one embodiment includes identifying a request to migrate data associated with a volume from a source storage pool to a destination storage pool, identifying volume segment table (VST) entries corresponding to rank extents within the source storage pool containing the data, allocating and synchronizing small VSTs for the identified VST entries within the volume, allocating one or more rank extents within the destination storage pool, transferring the data associated with the volume from the rank extents within the source storage pool containing the data to the one or more rank extents in the one or more ranks of the destination storage pool, updating the small VSTs to correspond to the transferred data in the one or more rank extents in the one or more ranks of the destination storage pool, and freeing the data from the one or more rank extents within the source storage pool.

Abstract: Provided are a computer program product, system, and method for using at least one machine learning module to select a priority queue from which to process an Input/Output (I/O) request. Input I/O statistics are provided on processing of I/O requests at the queues to at least one machine learning module. Output is received from the at least one machine learning module for each of the queues. The output for each queue indicates a likelihood that selection of an I/O request from the queue will maintain desired response time ratios between the queues. The received output for each of the queues is used to select a queue of the queues. An I/O request from the selected queue is processed.

Abstract: Provided are a computer program product, system, and method for using a machine learning module to determine when to perform error checking of a storage unit. Input on attributes of at least one storage device comprising the storage unit are provided to a machine learning module to produce an output value. An error check frequency is determined from the output value. A determination is made as to whether the error check frequency indicates to perform an error checking operation with respect to the storage unit. The error checking operation is performed in response to determining that the error checking frequency indicates to perform the error checking operation.

Abstract: Provided are a computer program product, system, and method in accordance with one embodiment of the present description, in which selected memory resources targeted for secure data cleaning. For example, memory resources which may be targeted for secure data cleaning may include a specific volume (or portion of a volume) of data storage which has been selected for secure erasure of any residual data which may remain on the targeted storage volume before the volume is released for use by others. Other examples of memory resources which may be targeted for secure data cleaning in accordance with the present description may include one or more buffers located in switches or other storage area network components which provide data paths between hosts and storage controllers or storage control units. Other aspects of targeted secure data overwriting in accordance with the present description are described.

Abstract: A method for dynamically altering the performance class of multiple storage drives is disclosed. In one embodiment, such a method monitors, within a storage environment, characteristics (e.g., age, wear, etc.) of multiple storage drives. Each storage drive has a performance class associated therewith. Based on the characteristics, the method periodically modifies the performance class of the storage drives. The method then reorganizes the storage drives within various storage groups (e.g., RAID arrays, storage tiers, workloads, etc.) based on their performance class. For example, the method may place, as much as possible, storage drives of the same performance class within the same storage groups. A corresponding system and computer program product are also disclosed.

Abstract: A background process is configured to periodically scrub a boot storage of a storage controller to ensure operational correctness of the boot storage. One or more foreground processes store a system configuration data of the storage controller in the boot storage of the storage controller. The background process and the one or more foreground processes are executed to meet predetermined performance requirements for the background process and the one or more foreground processes.

Abstract: A method for dynamically altering logical storage capacity within multiple storage drives is disclosed. In one embodiment, such a method monitors, within a storage environment, characteristics (e.g., age, wear, etc.) of multiple storage drives. Each storage drive has an amount of overprovisioning associated therewith. Based on the characteristics, the method periodically modifies a logical storage capacity of the storage drives in order to alter the amount of overprovisioning. The method then reorganizes the storage drives within various storage groups (e.g., RAID arrays, storage tiers, workloads, etc.) based on their logical storage capacity. For example, the method may place, as much as possible, storage drives of the same logical storage capacity within the same storage groups. A corresponding system and computer program product are also disclosed.

Abstract: Provided are a computer program product, system, and method for using a machine learning module to determine when to perform a data integrity check of copies of a data set. Input on storage attributes of a plurality of storage units, each storage unit of the storage units storing a copy of a data set, is provided to a machine learning module to produce an output value. A determination is made as to whether the output value indicates to perform a data integrity check of the copies of the data set. A determination is made as to whether the copies of the data set on different storage units are inconsistent in response to determining to perform the data integrity check. At least one of the copies of the data set is corrected to synchronize all the copies of the data set.

Abstract: Provided are a computer program product, system, and method for using a machine learning module to determine when to perform a data integrity check of copies of a data set. Input on storage attributes of a plurality of storage units, each storage unit of the storage units storing a copy of a data set, is provided to a machine learning module to produce an output value. A determination is made as to whether the output value indicates to perform a data integrity check of the copies of the data set. A determination is made as to whether the copies of the data set on different storage units are inconsistent in response to determining to perform the data integrity check. At least one of the copies of the data set is corrected to synchronize all the copies of the data set.

Abstract: Provided are a computer program product, system, and method for using a machine learning module to determine when to replace a storage device. Input on attributes of the storage device is provided to a machine learning module to produce an output value. A determination is made whether the output value indicates to replace the storage device. Indication is made to replace the storage device in response to determining that the output value indicates to replace the storage device.

Abstract: Provided are a computer program product, system, and method for using a machine learning module to determine when to perform error checking of a storage unit. Input on attributes of at least one storage device comprising the storage unit are provided to a machine learning module to produce an output value. An error check frequency is determined from the output value. A determination is made as to whether the error check frequency indicates to perform an error checking operation with respect to the storage unit. The error checking operation is performed in response to determining that the error checking frequency indicates to perform the error checking operation.

Abstract: Provided are a computer program product, system, and method for using a machine learning module to determine when to perform error checking of a storage unit. Input on attributes of at least one storage device comprising the storage unit are provided to a machine learning module to produce an output value. An error check frequency is determined from the output value. A determination is made as to whether the error check frequency indicates to perform an error checking operation with respect to the storage unit. The error checking operation is performed in response to determining that the error checking frequency indicates to perform the error checking operation.

Abstract: Provided are a computer program product, system, and method for using a machine learning module to determine when to replace a storage device. Input on attributes of the storage device is provided to a machine learning module to produce an output value. A determination is made whether the output value indicates to replace the storage device. Indication is made to replace the storage device in response to determining that the output value indicates to replace the storage device.