Abstract: A first score corresponding to a full stride destage, a second score corresponding to a strip destage, and a third score corresponding to an individual track destage are computed, wherein the first score, the second score, and the third score are computed for a group of Input/Output (I/O) operations based on a first metric and a second metric, wherein the first metric is configured to affect a performance of data transfers, and wherein the second metric is configured to affect a drive life. A determination is made of a type of destage to perform based on the first score, the second score, and the third score.

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 mirror path statistics in recalling extents to a primary storage system and a secondary storage system from a third storage system, A recall request is received to recall extents from the third storage that were migrated from the primary storage or the secondary storage to the third storage. A determination is made as to whether mirror paths used to migrate extents from the primary storage to the secondary storage exceed a degradation criteria. Commands are sent to cause the recall extents to be recalled from the third storage to both the primary storage and the secondary storage in response to determining that the mirror paths exceed the degradation criteria.

Abstract: A computer-implemented method for transferring a reserve to a target host, according to one embodiment, includes granting to a source system, by a control unit, a reserve for a volume of a storage device. A push reserve command is received from the source system. The push reserve command specifies: a transfer of the reserve to a target system, parameter data identifying the target system, and path information specifying the source system. The reserve is transferred only to the target system in response to release of the reserve by the source system.

Abstract: A determination is made that a point-in-time copy of a consistency group of a production volume has to be stored in a backup storage that is configured to store a plurality of point-in-time copies generated at a plurality of time instants. An extent of a thin provisioned volume of a highest storage tier of a tiered storage is allocated to store the point-in-time copy of the consistency group. A process is initiated for storing the point-in-time copy of the consistency group to the extent of the highest storage tier of the tiered storage.

Abstract: A method for improving destage performance to a RAID array is disclosed. In one embodiment, such a method periodically scans a cache for first strides that are ready to be destaged to a RAID array. While scanning the cache, the method identifies second strides that are not currently ready to be destaged to the RAID array, but will likely be ready to be destaged during a subsequent scan of the cache. The method initiates preemptive staging of any missing data of the second strides from the RAID array into the cache in preparation for the subsequent scan. Upon occurrence of the subsequent scan, the method destages, from the cache, the second strides from the cache to the RAID array. A corresponding system and computer program product are also disclosed.

Abstract: A first score corresponding to a full stride destage, a second score corresponding to a strip destage, and a third score corresponding to an individual track destage are computed, wherein the first score, the second score, and the third score are computed for a group of Input/Output (I/O) operations based on a first metric and a second metric, wherein the first metric is configured to affect a performance of data transfers, and wherein the second metric is configured to affect a drive life. A determination is made of a type of destage to perform based on the first score, the second score, and the third score.

Abstract: A storage controller is configured to perform a full stride destage, a strip destage, and an individual track destage. A machine learning module receives a plurality of inputs corresponding to a plurality of factors that affect performance of data transfer operations and preservation of drive life in the storage controller. In response to receiving the inputs, the machine learning module generates a first output, a second output, and a third output that indicate a preference measure for the full stride destage, the strip destage, and the individual track destage respectively.

Abstract: A computer-implemented method, according to one approach, is for converting an existing storage pool into an intended storage pool in place. The computer-implemented method includes: identifying a request to convert the existing storage pool having a first extent size into the intended storage pool having a second extent size, where the first extent size is larger than the second extent size. For each volume in the existing storage pool: a temporary pause/lock is performed on a volume segment table (VST) entry which corresponds to the given volume, and a new structure is applied to the VST entry. The new structure divides existing physical extents associated with the VST entry into two or more smaller physical extents. Furthermore, for each of the ranks in the existing storage pool, the given rank is converted from the first extent size to the second extent size.

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: One general aspect of device reservation state synchronization in accordance with the present description, device reservation management logic ensures synchronization of reservation states of primary and secondary volumes of a mirror relationship in the event of a change in the state of the mirroring relationship such as achieving full data synchronization between the volumes. Other features and aspects may be realized, depending upon the particular application.

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 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 storage access statistics to determine mirrored extents to migrate from a primary storage system and a secondary storage system to a third storage system. A determination is made of access statistics with respect to mirrored extents of data at the primary storage mirrored to the secondary storage to migrate to the third storage. A first set of the mirrored extents associated with access statistics indicating a highest level of access of the mirrored extents are migrated from the secondary storage to the third storage. A second set of the mirrored extents associated with access statistics indicating a lower level of access than the mirrored extents in the first set are migrated from the primary storage to the secondary storage.

Abstract: Provided are a computer program product, system, and method for using mirror path statistics in recalling extents to a primary storage system and a secondary storage system from a third storage system, A recall request is received to recall extents from the third storage that were migrated from the primary storage or the secondary storage to the third storage. A determination is made as to whether mirror paths used to migrate extents from the primary storage to the secondary storage exceed a degradation criteria. Commands are sent to cause the recall extents to be recalled from the third storage to both the primary storage and the secondary storage in response to determining that the mirror paths exceed the degradation criteria.

Abstract: Provided are a computer program product, system, and method for using storage access statistics to determine mirrored extents to migrate from a primary storage system and a secondary storage system to a third storage system. A determination is made of access statistics with respect to mirrored extents of data at the primary storage mirrored to the secondary storage to migrate to the third storage. A first set of the mirrored extents associated with access statistics indicating a highest level of access of the mirrored extents are migrated from the secondary storage to the third storage. A second set of the mirrored extents associated with access statistics indicating a lower level of access than the mirrored extents in the first set are migrated from the primary storage to the third storage.

Abstract: Provided are a computer program product, system, and method for using trap cache segments to detect malicious processes. A trap cache segment to the cache for data in the storage and indicated as a trap cache segment. Cache segments are added to the cache having data from the storage that are not indicated as trap cache segments. A memory function call from a process executing in the computer system reads data from a region of a memory device to output the read data to a buffer of the memory device. A determination is made as to whether the region of the memory device includes the trap cache segment. The memory function call is blocked and the process is treated as a potentially malicious process in response to determining that the region includes the trap cache segment.