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 improving cache hit ratios for selected storage elements within a storage system is disclosed. In one embodiment, such a method includes storing, in a cache of a storage system, non-favored storage elements and favored storage elements. The favored storage elements are retained in the cache longer than the non-favored storage elements. The method maintains a first LRU list containing entries associated with non-favored storage elements and designating an order in which the non-favored storage elements are evicted from the cache, and a second LRU list containing entries associated with favored storage elements and designating an order in which the favored storage elements are evicted from the cache. The method moves entries between the first LRU list and the second LRU list as favored storage elements are changed to non-favored storage elements and vice versa. A corresponding system and computer program product are also disclosed.

Abstract: A method for pinning selected volumes within a heterogeneous cache is disclosed. The method maintains a heterogeneous cache made up of a higher performance portion and a lower performance portion. A list of pinned volumes is received that are provided higher priority than other volumes within the heterogeneous cache. The method dedicates, within the lower performance portion, a storage area to accommodate the pinned volumes and prestages the pinned volumes within the storage area. In certain embodiments, an LRU list is maintained that indicates an order in which storage elements of the pinned volumes are demoted from the storage area. A corresponding system and computer program product are also disclosed.

Abstract: Provided are a computer program product, system, and method for using track locks and stride group locks to manage cache operations. A group of tracks from the storage devices are stored in a cache. Exclusive track locks for tracks in the group in the cache are granted for writes to the tracks in the group in the cache, wherein exclusive track locks can be simultaneously held for writes to different tracks in the cache. An exclusive group lock for the group of tracks in the cache is granted to destage the tracks in the group from the cache to the storage devices. The exclusive group lock is released in response to completing the destage of the tracks in the group in the cache to the storage devices.

Abstract: Provided are a computer program product, system, and method for determining whether to destage write data in cache to storage based on whether the write data has malicious data. Write data for a storage is cached in a cache. A determination is made as to whether the write data in the cache comprises random data according to a randomness criteria. The write data in the cache to the storage in response to determining that the write data does not comprise random data according to the randomness criteria. The write data is processed as malicious data after determining that the write data comprises random data according to the randomness criteria.

Abstract: A computer program product, system, and method for managing adding of accessed tracks in cache to a most recently used end of a cache list. A cache list for the cache has a least recently used (LRU) end and a most recently used (MRU) end. Tracks in the cache are indicated in the cache list. A track in the cache indicated on the cache list is accessed. A determination is made as to whether a track cache residency time since the accessed track was last accessed while in the cache list is within a region of lowest track cache residency times. A flag is set for the accessed track indicating to indicate the track at the MRU end in response to determining that the track cache residency time of the accessed track is within the region of lowest track cache residency times. The accessed track remains at a current position in the cache list before being accessed after setting the flag.

Abstract: In one embodiment, a task control block (TCB) for allocating cache storage such as cache segments in a multi-track cache write operation may be enqueued in a wait queue for a relatively long wait period, the first time the task control block is used, and may be re-enqueued on the wait queue for a relatively short wait period, each time the task control block is used for allocating cache segments for subsequent cache writes of the remaining tracks of the multi-track cache write operation. As a result, time-out suspensions caused by throttling of host input-output operations to facilitate cache draining, may be reduced or eliminated. It is appreciated that wait classification of task control blocks in accordance with the present description may be applied to applications other than draining a cache. Other features and aspects may be realized, depending upon the particular application.

Abstract: A computational device receives an indication that specifies a maximum retention time in cache for a first plurality of tracks, wherein no maximum retention time is specified for a second plurality of tracks. A plurality of insertions points are generated in a least recently used (LRU) list, wherein different insertion points in the LRU list correspond to different amounts of time that a track of the first plurality of tracks is expected to be retained in the cache, wherein the LRU list is configured to demote both tracks of the first plurality of tracks and the second plurality of tracks from the cache.

Abstract: A metadata track stores metadata corresponding to both a first customer data track and a second customer data track. In response to receiving a first request to perform a write on the first customer data track from a two track write process, exclusive access to the first customer data track is provided to the first request, and shared access to the metadata track is provided to the first request. In response to receiving a second request to perform a write on the second customer data track from the two track write process, exclusive access to the second customer data track is provided to the second request, and shared access to the metadata track is provided to the second request prior to providing exclusive access to the metadata track to at least one process that is waiting for exclusive access to the metadata track.

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: A method for dynamically adjusting utilization of I/O processing techniques includes providing functionality to execute a plurality of I/O processing techniques. The I/O processing techniques include a first I/O processing technique that uses a higher performance communication path for transmitting I/O and a second I/O processing technique that uses a lower performance communication path for transmitting I/O. The method automatically increases use of the first I/O processing technique and reduces use of the second I/O processing technique when the set of conditions is satisfied. Similarly, the method automatically increases use of the second I/O processing technique and reduces use of the first I/O processing technique when the set of conditions is not satisfied. 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 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 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 offloading processing of writes to determine malicious data from a first storage system to a second storage system. Write data for a first storage is cached in a first cache in the first storage system and copied to a second cache in the second storage system. The second storage system determines whether the copy of the write data comprises random data according to a randomness criteria. The second storage system returns a message to the first storage system indicating that the write data comprises malicious data if random or not malicious data if not random. The first storage system destages the write data in the first cache if the message indicates not malicious data or processes the write data in the first cache as malicious data if the message indicates malicious data.

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.

Abstract: A method for improving asynchronous data replication between a primary storage system and a secondary storage system is disclosed. In one embodiment, such a method includes monitoring, in a cache of the primary storage system, unmirrored data elements needing to be mirrored, but that have not yet been mirrored, from the primary storage system to the secondary storage system. The method maintains a regular LRU list designating an order in which data elements are demoted from the cache. The method determines whether a data element at an LRU end of the regular LRU list is an unmirrored data element. In the event the data element at the LRU end of the regular LRU list is an unmirrored data element, the method moves the data element to a transfer-pending LRU list dedicated to unmirrored data elements in the cache. A corresponding system and computer program product are also disclosed.

Abstract: Provided are a computer program product, system, and method for determining sectors of a track to stage into cache by training a machine learning module. A machine learning module that receives as input performance attributes of system components affected by staging tracks from the storage to the cache and outputs a staging strategy comprising one of a plurality of staging strategy indicating at least one of a plurality of sectors of a track to stage into the cache. A margin of error is determined based on a current value of a performance attribute and a threshold of the performance attribute. An adjusted staging strategy is determined based on the margin of error. The machine learning module is retrained with current performance attributes to output the adjusted staging strategy.

Abstract: A method for improving asynchronous data replication between a primary storage system and a secondary storage system maintains a cache in the primary storage system. The cache includes a higher performance portion and a lower performance portion. The method monitors, in the cache, unmirrored data elements needing to be mirrored, but that have not yet been mirrored, from the primary storage system to the secondary storage system. The method maintains a regular LRU list designating an order in which data elements are demoted from the cache. The method determines whether a data element at an LRU end of the regular LRU list is an unmirrored data element. In the event the data element at the LRU end is an unmirrored data element, the method moves the data element from the higher performance portion to the lower performance portion. A corresponding system and computer program product are also disclosed.