Abstract: A method for demoting a selected storage element from a cache memory includes storing favored and non-favored storage elements within a higher performance portion and lower performance portion of the cache memory. The favored storage elements are retained in the cache memory longer than the non-favored storage elements. The method maintains a first favored LRU list and a first non-favored LRU list, associated with the favored and non-favored storage elements stored within the higher performance portion of the cache. The method selects a favored or non-favored storage element to be demoted from the higher performance portion of the cache memory according to life expectancy and residency of the oldest favored and non-favored storage elements in the first LRU lists. The method demotes the selected from the higher performance portion of the cache to the lower performance portion of the cache, or to the data storage devices, according to a cache demotion policy.

Abstract: A method for demoting a selected storage element from a cache memory includes storing favored and non-favored storage elements within a higher performance portion and lower performance portion of the cache memory. The method maintains a plurality of favored LRU lists and a non-favored LRU list for the higher and lower performance portions of the cache memory. Each favored LRU list contains entries associated with the favored storage elements that have the same unique residency multiplier. The non-favored LRU list includes entries associated with the non-favored storage elements. The method demotes a selected favored or non-favored storage element from the higher and lower performance portions of the cache memory according to a cache demotion policy that provides a preference to favored storage elements over non-favored storage elements based on a computed cache life expectancy, residency time, and the unique residency multiplier.

Abstract: A first non-volatile dual in-line memory module (NVDIMM) of a first server and a second NVDIMM of a second server are armed during initial program load in a dual-server based storage system to configure the first NVDIMM and the second NVDIMM to retain data on power loss. Prior to initiating a safe data commit scan to destage modified data from the first server to a secondary storage, a determination is made as to whether the first NVDIMM is armed. In response to determining that the first NVDIMM is not armed, a failover is initiated to the second server.

Abstract: A computational device receives a command to activate a time lock for a data set. In response to receiving the command to activate the time lock for the data set, a point in time copy of the data set is generated to allow write operations to be performed even if the time lock is activated.

Abstract: Provided are a computer program product, system, and method for reducing a rate at which data is mirrored from a primary server to a secondary server. A determination is made as to whether a processor utilization at a processor managing access to the secondary storage exceeds a utilization threshold. If so, a determination is made as to whether a specified operation at the processor is in progress. A message is sent to the primary server to cause the primary server to reduce a rate at which data is mirrored from the primary server to the secondary server in response to determining that the specified operation is in progress.

Abstract: Provided are a computer program product, system, and method for using a machine learning module to determine an allocation of stage and destage tasks. Storage performance information related to processing of Input/Output (I/O) requests with respect to the storage unit is provided to a machine learning module. The machine learning module receives a computed number of stage tasks and a computed number of destage tasks. A current number of stage tasks allocated to stage tracks from the storage unit to the cache is adjusted based on the computed number of stage tasks. A current number of destage tasks allocated to destage tracks from the cache to the storage unit is adjusted based on the computed number of destage tasks.

Abstract: Provided are a computer program product, system, and method for recovering storage devices in a storage array having errors. A determination is made to replace a first storage device in a storage array with a second storage device. The storage array is rebuilt by including the second storage device in the storage array and removing the first storage device from the storage array resulting in a rebuilt storage array. The first storage device is recovered from errors that resulted in the determination to replace. Data is copied from the second storage device included in the rebuilt storage array to the first storage device. The recovered first storage device is swapped into the storage array to replace the second storage device in response to copying the data from the second storage device to the first storage device.

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: 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.

Abstract: In response to an end of track access for a track in a cache, a determination is made as to whether the track has modified data and whether the track has one or more holes. In response to determining that the track has modified data and the track has one or more holes, an input on a plurality of attributes of a computing environment in which the track is processed is provided to a machine learning module to produce an output value. A determination is made as to whether the output value indicates whether one or more holes are to be filled in the track. In response to determining that the output value indicates that one or more holes are to be filled in the track, the track is staged to the cache from a storage drive.

Abstract: Aspects of the present disclosure relate to encryption management. A determination can be made whether an encryption algorithm is at-risk. In response to determining that the encryption algorithm is at-risk, data protected by the encryption algorithm can be identified. A security action can then be executed on the data protected by the encryption algorithm.

Abstract: Provided are a computer program product, system, and method for using a machine learning module to select one of multiple cache eviction algorithms to use to evict a track from the cache. A first cache eviction algorithm determines tracks to evict from the cache. A second cache eviction algorithm determines tracks to evict from the cache, wherein the first and second cache eviction algorithms use different eviction schemes. At least one machine learning module is executed to produce output indicating one of the first cache eviction algorithm and the second cache eviction algorithm to use to select a track to evict from the cache. A track is evicted that is selected by one of the first and second cache eviction algorithms indicated in the output from the at least one machine learning module.

Abstract: Provided are a computer program product, system, and method for fixing anomalies in a preserved data structure used to generate a temporary data structure during system initialization. A preserved data structure in persistent storage is used to build a temporary data structure in a memory of the computing system during initialization of the computing system. The temporary data structure represents computational resources in the computing system and is rebuilt from the preserved data structure during the initialization. The preserved data structure and the temporary data structure are processed to determine whether the preserved data structure includes at least one anomaly that would result in rebuilding the temporary data structure with an error. Information on the preserved data structure and the temporary data structure having the anomaly are processed to determine modifications to correct the preserved data structure. The determined modifications are processed to correct the preserved data structure.

Abstract: Provided are a computer program product, system, and method for determining sectors of a track to stage into cache using a machine learning module. Performance attributes of system components affected by staging tracks from the storage to the cache are provided to a machine learning module. An output is received, from the machine learning module having processed the provided performance attributes, indicating a staging strategy indicating sectors of a track to stage into the cache comprising one of a plurality of staging strategies. Sectors of an accessed track that is not in the cache are staged into the cache according to the staging strategy indicated in the output.

Abstract: A portable handheld device receives from a central repository, information on a failed hardware component of a computational device, wherein the information includes an authentication code to permit access to the failed hardware component and a time window in which the failed hardware component is permitted to be accessed. The portable handheld device uses the authentication code to access the failed hardware component for repair or replacement during the time window.

Abstract: Provided are a computer program product, system, and method for using a machine learning module to determine an allocation of stage and destage tasks. Storage performance information related to processing of Input/Output (I/O) requests with respect to the storage unit is provided to a machine learning module. The machine learning module receives a computed number of stage tasks and a computed number of destage tasks. A current number of stage tasks allocated to stage tracks from the storage unit to the cache is adjusted based on the computed number of stage tasks. A current number of destage tasks allocated to destage tracks from the cache to the storage unit is adjusted based on the computed number of destage tasks.

Abstract: A method for maintaining statistics for data elements in a cache is disclosed. The method maintains a heterogeneous cache comprising a higher performance portion and a lower performance portion. The method maintains, within the lower performance portion, a ghost cache containing statistics for data elements that are currently contained in the heterogeneous cache, and data elements that have been demoted from the heterogeneous cache within a specified time interval. The method maintains updates to the statistics in an update area within the higher performance portion. The method determines whether the updates have reached a specified threshold and, in the event the updates have reached the specified threshold, flushes the updates from the update area to the ghost cache to update the statistics. A corresponding system and computer program product are also disclosed.

Abstract: A machine learning module is trained by receiving inputs comprising attributes of a computing environment, where the attributes affect a likelihood of failure in the computing environment. In response to an event occurring in the computing environment, a risk score that indicates a predicted likelihood of failure in the computing environment is generated via forward propagation through a plurality of layers of the machine learning module. A margin of error is calculated based on comparing the generated risk score to an expected risk score, where the expected risk score indicates an expected likelihood of failure in the computing environment corresponding to the event. An adjustment is made of weights of links that interconnect nodes of the plurality of layers via back propagation to reduce the margin of error, to improve the predicted likelihood of failure in the computing environment.

Abstract: Input on a plurality of attributes of a computing environment is provided to a machine learning module to produce an output value that comprises a risk score that indicates a likelihood of a potential malfunctioning occurring within the computing environment. A determination is made as to whether the risk score exceeds a predetermined threshold. In response to determining that the risk score exceeds a predetermined threshold, an indication is transmitted to indicate that potential malfunctioning is likely to occur within the computing environment. A modification is made to the computing environment to prevent the potential malfunctioning from occurring.

Abstract: Methods and systems for cyber-hacking detection are provided. One method includes generating, by a processor, one or more artificial accounts for a type of actual account, learning one or more hacking behaviors for the type of actual account, and detecting cyber-hacks in activity in the one or more artificial accounts based on the one or more hacking behaviors. Systems and computer program products for performing the above method are also provided.