International Business Machines Corporation

Kyndryl, Inc.

John Jason Auvenshine

Stanley C Wood

Ramani Ranjan Routray

Justin Christopher Blackburn

Per Lutkemeyer

John Thomas Olson

Mu Qiao

James E Olson

Laura A Richardson

Perry J Gallo

Christopher Joel Jacoby

Bernhard Julius Klingenberg

Sunhwan Lee

Mikael H Lindstrom

Henry C Will, Iv

Erik Bartholomy

Bent Braum Holst

Nicolas Edward Fosdick

David Schustek

Richard Albert Welp

Rakesh Jain

Anthony J Ciaravella

Donald Charles Laing

Christian Sonder

Nicolas Andre Druet

Dominic Thibodeau

Andrew Gary Hourselt

Neeta Garimella

Nedzad Taljanovic

Thomas K Clark

Dave Aime Desire Kodjo

Martin Proulx

Scott W Hughes

Edward Fosdick Nicolas

Imran Ahmed

Marci Devorah Formato

Lisa M Chavez

Basheer Neuhru Bristow

Victor L Walter

Khwaja Jawahar Jahangir Shaik

Harley D Puckett, Iii

Jason LaScola

Michael Flores

Joseph Dawson Davis, Iii

Dave Kodjo

Marcel Pop

Paul Cloutier

Thomas E Haddock

Kenneth D Paquette

Ramesh Babu Kothamasu

Michael L Camarco, Iii

Carlin M Kartchner

Vidula M Patel

Mauricio Perez Hernandez

Tron H Pryor

Luis Ignacio Callero

Robert Ong Sio

David Lutz

A media exfiltration authorization system is provided. A computer device receives a request from an application on a remote device, wherein the request is to store data on an external storage device. The computing device validates that the application is running in protected space on the remote device and includes an established unique identifier. The computing device generates an encryption key for the external storage device based, at least in part, on the validating. The computing device sends the encryption key to the application with authorization for the application to reformat the external storage device, store the requested data on the external storage device, and encrypt the external storage device using the encryption key.

Media exfiltration prevention system

A method, system, and program product for selecting software is provided. The method includes retrieving audio data during a call with a subject matter expert (SME). The audio data is converted into a data training set and documents of the SME are converted into a document training set. Canonical documents generated by authors are analyzed and specified code is extracted from the text data training set and document training set. Attributes of individuals are detected. The attributes are compared with specified data and the canonical documents and it is determined that the individuals are requesting information associated with the text data. The information is provided to the individuals via the canonical documents or the documents of the SME and it is determined if a matched set of data exists between the attributes, the specified data, and the canonical documents. A digital action associated with results of the determination is executed.

Interactive selection and modification

A method for verifying that default passwords have been changed without causing a security lockout, is provided, including enabling user identifiers associated with a plurality of devices, prior to an initial security test, identifying, a default password for a user identifier of each device, attempting a login to each device using the default password for the user identifier of each device, wherein: in response to determining that the login is successful, raising an alert against the user identifier as a security concern and maintaining an enabled state of the user identifier, in response to determining that the login is unsuccessful, disabling the user identifier so that the user identifier is in a non-enabled state, until a security lockout interval elapses, and retrying the login only for each user identifier in an enabled state during one or more subsequent security tests initiated after a predetermined alert interval.

Non-disruptive system for verifying default passwords have been changed without causing security lockouts

A method to prevent the inadvertent removal of volumes on a storage system is disclosed. In one embodiment, such a method includes receiving a request to remove (e.g., delete, detach, unmask, etc.) a volume on a storage system. In response to receiving the request, the method initiates at least one process to monitor the volume for I/O activity over a specified period of time. In the event the at least one process does not detect I/O activity to the volume during the specified period of time, the method executes the request by removing the volume. In the event the at least one process detects I/O activity to the volume during the specified period of time, the method denies the request to remove the volume. A corresponding system and computer program product are also disclosed.

Accidental-volume-removal prevention

Disclosed is a computer-implemented method of finding, troubleshooting and auto-remediating problems in storage environments. The method includes guiding a user, by a data processing system of an active storage environment, to select an applicable playbook of troubleshooting logic from among playbooks of different troubleshooting logic to address problem(s) with infrastructure device(s) of the active storage environment, asking the user, by the data processing system, questions from the applicable playbook to identify a possible resolution path for the problem(s), resulting in an identified resolution path, receiving, by the data processing system, answers to the questions from the user, obtaining, by the data processing system, cross-domain information regarding infrastructure device(s) potentially relevant to the problem(s), resulting in obtained cross-domain information, cognitively determining, by the data processing system, possible resolution(s) based on the answers and the obtained cross-domain information, and auto-remediating, by the data processing system, the problem(s).

Finding, troubleshooting and auto-remediating problems in active storage environments

A method for distributing data among storage devices. The method comprising one or more processors receiving a first graph workload that executes within a networked computing environment. The method further includes identifying data from the first graph workload that is utilized during the execution of the first graph workload that includes a plurality of data packets. The method further includes creating a first graph workload model representative of the graph structure of the first graph workload and determining two or more partitions that are representative of a distribution of the identified data utilized by the first graph workload based, at least in part, on the first graph workload model. The method further includes allocating a plurality of network accessible storage devices among the two or more partitions and copying a first set of data packets of the plurality of data packets to a network accessible storage device.

Assigning storage locations based on a graph structure of a workload

A method and associated systems for optimized orchestration of a data-migration project. A data-migration orchestration system represents a hierarchical organization of each dataset to be migrated as a tree, where each leaf node of the tree represents data to be migrated and where a path between the leaf node and the root node represents a hierarchical directory pathname of sensitive data represented by the leaf node. Each tree is assigned a sensitivity signature that is proportional to the relative sensitivity and access frequency of the dataset represented by that tree. The signatures are organized into clusters as a function of the distances between each signature, and each signature is associated with a soft migration cost specific to that signature's cluster. A soft cost for migrating an application that requires multiple datasets may be determined by adding the migration costs associated with each of the multiple datasets.

Optimized orchestration of complex application-migration projects based on data sensitivity

A method and system for improving operation of a memory device is provided. The method includes detecting, via sensors, environmental factors affecting an operation of individual hardware storage devices within an array of hardware storage devices. The environmental factors are analyzed with respect to operational characteristics of the individual hardware storage devices and a resulting expected failure rate for the individual hardware storage devices is determined. Array parameters associated with a hardware configuration for the array of hardware storage devices with respect to each expected failure rate are determined and associated issues are detected. Reliability characteristics and associated risks of the array of hardware storage devices are determined and a functionality of the array of hardware storage devices is modified.

Hardware storage device optimization

Systems and methods for resource provisioning are disclosed. A method includes: generating, by a computer device, a resource provisioning policy for a resource; receiving, by the computer device, a request for an allocation of the resource from an account; applying, by the computer device, the resource provisioning policy to the request based on receiving the request; automatically approving or denying, by the computer device, the request based on the applying the resource provisioning policy to the request; updating, by the computer device, the resource provisioning policy for the account based on the automatic approving or denying the request.

Resource provisioning

A method for predicting a failure of a complex storage environment is provided. An earliest expected incident of resource exhaustion on a data path of interest is predicted. The method includes monitoring a current utilization of one or more resources over a data path of interest, and calculating a maximum utilization threshold for each resource, such that exceeding the maximum utilization threshold adversely impacts one or more performance measures. An expected performance threshold is created that is associated with an expected performance. A maximum values of resource usage over time is analyzed to predict when the expected performance will fall outside of an acceptable overall performance threshold for the data path of interest to determine an earliest expected incident of resource exhaustion.

Analyzing performance and capacity of a complex storage environment for predicting expected incident of resource exhaustion on a data path of interest by analyzing maximum values of resource usage over time

A method and associated systems for a workload-aware thin-provisioning system that allocates physical storage to virtual resources from pools of physical storage volumes. The system receives constraints that limit the amount of storage that can be allocated from each pool and the total workload that can be directed to each pool. It also receives lists of previous workloads and allocations associated with each volume at specific times in the past. The system then predicts future workloads and allocation requirements for each volume by regressing linear equations derived from the received data. If the predicted values indicate that a pool will at a future time violate a received constraint, the system computes the minimum costs to move each volume of the offending pool to a less-burdened pool. It then selects the lowest-cost combination of volume and destination pool and then moves the selected volume to the selected pool.

Workload-aware storage-allocation system

A method and system. A health and compliance check report for a network including two or more devices is received. The report includes a list of health and compliance check findings at a particular time. Each finding indicates a determination of non-compliance of a subject device of the two or more devices with a predetermined requirement and includes a severity value associated with the determination of non-compliance of the subject device. For each finding in the report, a ranking value is determined based on the associated severity value and an importance value of one or more devices connected to the subject device.

Ranking health and compliance check findings in a data storage environment

A computer-implemented method of information lifecycle management is disclosed. The computer-implemented method includes reading, by a data processing system of a storage environment, business rules and policies for managing data in storage volumes of the storage environment, the policies being based on the predetermined business rules, and analyzing, by the data processing system, available storage and capacity in the storage environment. The computer-implemented method further includes dynamically prioritizing, by the data processing system, the policies based, at least in part, on results of the analyzing, resulting in prioritized policies, cognitively translating, by the data processing system, one or more of the predetermined business rules into action(s) against one or more of the storage volumes based, at least in part, on the prioritized policies, and executing, by the data processing system, the action(s).

Dynamic policy prioritization and translation of business rules into actions against storage volumes

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining information of first through Nth storage volumes and based on the examining providing for each storage volume of the first through Nth storage volumes a predicted storage space savings value, the predicted storage space savings value indicating a predicted terabyte volume of storage space savings producible by performance of data compression of data stored on the storage volume; predicting a per terabyte compression cost savings associated with compressing one or more storage volume of the first through Nth storage volumes, and providing a ranking of storage volumes of the first through Nth storage volumes based on the examining and the predicting; and scheduling a compression of storage volumes of the first through Nth storage volumes based on the ranking of storage volumes of the first through Nth storage volumes.

Method for determining selection and ordering of storage volumes to compress

A method, computer system, and computer program product for determining whether data compression should be performed at a storage virtualization layer or a backend layer, within a storage area network, including assessing a volume of data to decide whether the volume of data should be compressed or is already compressed, and in response to deciding that the volume of data should be compressed: checking whether a restriction exists on the volume of data, and in response to deciding that the volume of data is already compressed: determining that the volume of data already compressed should be uncompressed based on at least one metric.

Data compression determination for storage area networks

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Tucson, AZ, United States of America

John Jason Auvenshine