International Business Machines Corporation

Micah Robison

Lokesh M Gupta

Matthew G Borlick

Brian Anthony Rinaldi

Kevin J Ash

Karl Allen Nielsen

Trung Ngoc Nguyen

Mark Elliott Hack

John Charles Elliott

Todd Charles Sorenson

James E Olson

Richard P Oubre, Jr

Usman Ahmed

Beth Ann Peterson

Kyler A Anderson

Xue Feng Gao

Steven Edward Klein

Matthew Richard Craig

David Ray Kahler

Ashwani Kumar

Keith Geoffrey Morrison

Richard Harry Hopkins

James Lamar Hood

Roah Jishi

Jeremy Leigh Royall

Matthew J Kalos

Michael T Benhase

Roger Gregory Hathorn

Enrique Q Garcia

Gary William Batchelor

Louis Alonso Rasor

John Norbert McCauley

Herve G P Andre

Scott A Brewer

Veronica S Davila

Markus Navarro

Alistair Leask Symon

Richard H Hopkins

Josue Cazarez Aguilar

Daniel I Ibanez

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.

Garbage collection for storage in which high-performance volumes reside

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.

Perform preemptive identification and reduction of risk of failure in computational systems by training a machine learning module

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.

Adjusting garbage collection based on input/output flow rates

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.

Encryption and decryption of data in a cloud storage based on indications in metadata

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.

Speculatively executing conditional branches of code when detecting potentially malicious activity

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.

Encryption management

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.

Determining feature settings for code to deploy to a system by training a machine learning module

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.

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.

Dynamic compression with dynamic multi-stage encryption for a data storage system

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.

Detection and repair of failed hardware components

Provided are techniques for adjusting a dispatch ratio for dispatching tasks from multiple queues. The dispatch ratio is set for each queue of a plurality of queues. A number of Central Processing Unit (CPU) cycles used by tasks from each of the plurality of queues during the interval is tracked. A CPU high percentage is determined that indicates a percentage of CPU cycles used by high priority tasks. In response to determining that the CPU high percentage is below a high threshold, a new dispatch ratio is calculated that indicates an increased number of high priority tasks are to be dispatched, and the new dispatch ratio is based on the CPU high percentage, the high threshold, and a current dispatches high value. The increased number of high priority tasks are dispatched from the high priority queue based on the new dispatch ratio during a new interval.

Adjusting a dispatch ratio for multiple queues

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.

Using a machine learning module to perform preemptive identification and reduction of risk of failure in computational systems

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.

Dynamic performance-class adjustment for storage drives

Growing community of inventors

Tucson, AZ, United States of America

Micah Robison