Abstract: A computer-implemented method, according to one approach, includes: causing compressed data to be read from a first compressed drive without decompressing the compressed data. A copy of the compressed data is also received from the first compressed drive. In response to determining a target location for the copy of the compressed data is a second compressed drive having a same configuration as the first compressed drive, the copy of the compressed data is transmitted to the second compressed drive. Moreover, the compressed data is written to the target location in the second compressed drive without compressing the compressed data.

Abstract: A computer-implemented method, according to one approach, includes: causing compressed data to be read from a first compressed drive without decompressing the compressed data. A copy of the compressed data is also received from the first compressed drive. In response to determining a target location for the copy of the compressed data is a second compressed drive having a same configuration as the first compressed drive, the copy of the compressed data is transmitted to the second compressed drive. Moreover, the compressed data is written to the target location in the second compressed drive without compressing the compressed data.

Abstract: A computer-implemented method, according to one approach, includes: in response to a system detecting an initial microcode load, determining whether memory in the system was disarmed during manufacture. The memory in the system is connected to backup power modules. The computer-implemented method also includes monitoring for concurrent code loads in response to determining that the memory was disarmed during manufacture. Moreover, in response to detecting a concurrent code load, the energy levels of the backup power modules are tested. A warning is further issued in response to determining the energy levels of one or more of the backup power modules are outside a predetermined range. Other systems, computer-implemented methods, and computer program products are described in additional approaches.

Abstract: A computer-implemented method, according to one embodiment, includes determining and using a first backup plan, where the first backup plan details a first number of backup copies of source volumes that are to be created and a first time interval that the first number of backup copies are to be created. In response to a determination that a first set of conditions are met during use of the first backup plan in a data storage system, a second backup plan that details a second number of backup copies of the source volumes that are to be created and a second time interval that the second number of backup copies are to be created are determined and used. In response to a determination that a second set of conditions are met during use of the second backup plan, a reversion to use of the first backup plan is caused.

Abstract: Provided are a storage device, system, and method for garbage collection at a storage device based on input/output access patterns at the storage device. One of a first garbage collection rate and a second garbage collection rate based on an Input/Output (I/O) access pattern at the storage device is processed. A determination is made of available free space. In response to determining the first garbage collection rate, initiating garbage collection in response to the available free space below a first threshold. In response to determining the second garbage collection rate, initiating garbage collection when the available free space is below a second threshold and when blocks have more than a threshold number of invalid pages. The first threshold and the second threshold comprise a same value or different values.

Abstract: Provided are a storage device, system, and method for performance enhancing measures for storage in which high-performance volumes reside. Indication is made of a volume configured in the arrays to have high-performance. An array is determined storing the indicated volume. A determination is made of storage devices in which the determined array resides. The storage devices are enabled to perform garbage collection on blocks of data having valid and invalid pages in the storage devices at a first garbage collection rate and a second garbage collection rate. The second garbage collection rate causes a storage device to perform garbage collection with respect to a greater number of blocks than the first garbage collection rate. A command is sent to the determined storage devices to garbage collect at the second garbage collection rate.

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: A computer-implemented method, according to one approach, includes: monitoring performance in NVRAM having a plurality of memory blocks. Moreover, a change in the flow rate of I/O requests received at the NVRAM is identified as a result of the monitoring. In response to identifying the change is an increase in the flow rate of received I/O requests, the percentage of invalid pages in a given block of NVRAM that triggers garbage collection to be performed on the given block is increased. However, in response to identifying the change is a decrease in the flow rate of received I/O requests, the percentage of invalid pages in a given block that triggers garbage collection to be performed on the given block is decreased.

Abstract: A computer-implemented method, according to one approach, includes: monitoring performance in NVRAM having a plurality of memory blocks. Moreover, a change in the flow rate of I/O requests received at the NVRAM is identified as a result of the monitoring. In response to identifying the change is an increase in the flow rate of received I/O requests, the percentage of invalid pages in a given block of NVRAM that triggers garbage collection to be performed on the given block is increased. However, in response to identifying the change is a decrease in the flow rate of received I/O requests, the percentage of invalid pages in a given block that triggers garbage collection to be performed on the given block is decreased.

Abstract: Provided are a method, a system, and a computer program product in which metadata associated with encrypted data is maintained in a cloud computing environment, where the metadata indicates whether reading of information in the encrypted data is restricted geographically. A controller provides a decryption code to a cloud server located in a geographical location. The decryption code is for decrypting the encrypted data. The controller provides the decryption code, based on a determination as to whether the metadata indicates whether the reading of information in the encrypted data is restricted geographically.

Abstract: Provided are a computer program product, system, and method for determining a frequency at which to execute trap code in an execution path of a process executing a program to generate a trap address range to detect potential malicious code. Trap code is executed in response to processing a specified type of command in application code to allocate a trap address range used to detect potentially malicious code. A determination is whether to modify a frequency of executing the trap code in response to processing a specified type of command. The frequency of executing the trap code is modified in response to processing the specified type of command in response to determining to determining to modify the frequency of executing the trap code.

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 determining feature settings for code to deploy to a system by training a machine learning module. A determination is made of an outcome of running system code on a system having configuration settings and feature settings of features in the system to enable or disable in response to the outcome. A machine learning module is trained to produce the feature settings indicating to enable or disable the features in response to input comprising the configuration settings of the system.

Abstract: Aspects of the present disclosure relate to encryption management. An indication of a data set to be tagged with an encryption tag is received. A location for the encryption tag is determined. The encryption tag is stored at the location, where the encryption tag includes an encryption status indicator specifying whether or not the data is encrypted and an encryption algorithm indicator specifying an encryption algorithm used to encrypt the data.

Abstract: Dynamic compression with dynamic multi-stage encryption for a data storage system in accordance with the present description includes, in one aspect of the present description, preserves end-to-end encryption between a host and a storage controller while compressing data which was received from the host in encrypted but uncompressed form, using MIPs and other processing resources of the storage controller instead of the host. In one embodiment, the storage controller decrypts encrypted but uncompressed data received from the host to unencrypted data and compresses the unencrypted data to compressed data. The storage controller then encrypts the compressed data to encrypted, compressed data and stores the encrypted, compressed data in a storage device controlled by the storage controller. Other aspects and advantages may be realized, depending upon the particular application.

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: 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: 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: 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: 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.