International Business Machines Corporation

Kyndryl, Inc.

Bernhard Julius Klingenberg

Ramani Ranjan Routray

Kurt A Rybczyk

Lisa A Shepard

James E Olson

Marci Devorah Formato

Yang Song

Steven M Reps

John Jason Auvenshine

Ann Corrao

Gabriel Alatorre

David Gregory Van Hise

Bruce Light Hillsberg

Mu Qiao

Sunhwan Lee

Robert Michael Rees

Neeta Garimella

Thomas K Clark

Stephen A Byrd

Joseph Reyes

Steven Czerwinski

J Kristofer Fox

Rajesh Francisco Krishnan

Balaji Thirumalai

Dinesh C Verma

Nagapramod S Mandagere

Jarir Kamel Chaar

Mark Andrew Smith

Hamza Yaswi

Radha P Ratnaparkhi

Steven Pantridge

Patrick B Heywood

Billy S Brewer

Daniela Kern Mainieri Trevisan

Andrew S Kronstadt

Steven J Davis

James E Gruener

Alisa Neeman

Emil R Nunez

Nathan L Williams

Carmen P Allen

David Maxwell Cannon

Clea Anne Zolotow

Thomas W Bish

John Valentine Delaney

Michael Allen Kaczmarski

Wayne Curtis Hineman

Laxmikantha Sai Nanduru

William Roy Yonker

Christopher C Bode

Zhengwen He

Anil Kumar Narigapalli

Petra Kopp

Venkateswarlu Basyam

Ryan John Minniear

Alan C Skinner

Srilakshmi Surapaneni

Kristofer Fox

Stephen Byrd

An embodiment for cognitively aligning data protection (DP) and disaster recovery (DR) policies is provided. The embodiment may include ingesting a variety of data associated with one or more applications into a repository. The embodiment may also include executing differential analysis on the data and changes to the data to identify differences between the data and the changes to the data. The changes to the data may be obtained by periodically polling internal and external data sources. The embodiment may also include translating the differences between the data and the changes to the data into an updated SLA. The embodiment may further include in response to determining that the differences between the data and the changes to the data warrant a change in the current DP and DR policies, generating one or more recommendations to modify the current DP and DR policies and/or create new DP and DR policies.

Cognitive data protection and disaster recovery policy management

A user can wear a device which emits a visual and/or audible output. The output changes over time. A system is capable of predicting the output. Thus, the system can analyze a video and determine, based on observed output of the device, whether the video has been modified. The output can be particularly difficult for humans to modify, detect, understand, or recreate, further impeding attempts to disguise edits to the video.

Wearable watermarks

Systems, methods, and computer program products providing network security leveraging analytics and physical separation between computer systems and a network to prevent threats from infecting network devices. A specialized pluggable dongle like security device is inserted between ports of computer system(s) connecting to the network and port(s) of network hardware facilitating connections between the computer system and computer network. The security device uses a combination of onboard analytics and cloud-based analytic services to detect incoming threats from network traffic and whether to allow network traffic to pass through the security device and/or prevent network traffic from entering the computer system. In response to detected network threats, an out of band management network communicating with the security device can open or close a physical gate onboard the security device, which, when opened introduces an air gap between the network and computer system, preventing harmful network traffic from entering the computer system.

In-line cognitive network security plugin device

An approach is provided for providing disaster recovery, active/active and active/standby workload allocation in a networked computing environment according to aspects of the present invention. Risk factor data is obtained for each component of a plurality of components in a global data center. A resiliency score is calculated for each component of the plurality of components based on a set of risk factors that are applicable to the component gathered from the risk factor data. A set of group resiliency scores are computed by aggregating the resiliency scores for each of the plurality of components included in a component group. In response to a determination that an application's performance can be improved, a datacenter is selected for failover protection based on a group resiliency score corresponding to the datacenter. Moreover, the overall enterprise resiliency score can be improved by moving an application between sites in the enterprise.

Workload allocation utilizing real-time enterprise resiliency scoring

Provided are a computer program product, system, and method for determining an optimal storage environment for data sets and for migrating data sets. Metadata for each application indicates storage pools used by the application to store data sets, wherein each storage pool is configured in one of a plurality of storage environments using different organization schemes to store data sets. The metadata for at least one application indicates storage pools to store the data sets for the application that are allocated from different storage environments. The metadata for an application is processed to determine a data set for the application stored in a first storage pool implemented in a first storage environment that should be stored in a second storage environment. The determined data set from the first storage pool to a second storage pool implemented in the second storage environment.

Determining an optimal storage environment for data sets and for migrating data sets

A method, apparatus, system, and computer program product for deduplicating data. Data is stored by a computer system in storage domains in a storage system. Deduplication is performed by the computer system within each storage domain in the storage domains using a hash table that includes entries for all of the storage domains. Cross-deduplication between the storage domains is avoided in the storage system.

Deduplicated storage with multiple storage domains

Backup schedule optimization is provided. Data describing a first backup schedule for a first backup server is identified. Performance metric data and workload data for a first backup server and a second backup server is collected. If a count of backup windows represented by workload data for the first backup server exceeds a data collection threshold, an updated backup schedule that offloads a pending job from the first backup server to the second backup server can be generated. The updated backup schedule is generated if a maximum average I/O threshold for the first backup server was exceeded while a minimum average I/O threshold for the second backup server was deceeded. The maximum and minimum average I/O thresholds are calculated based on respective target average I/O rates for the first and second backup servers, the target average I/O rates derived from respective perform metric data.

Optimizing data backup schedules

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 for detecting ransomware. A set of control files in a set of locations in a file system is checked, by a computer system, in response to an event, wherein the set of control files has a set of extensions targeted by the ransomware. In response to detecting a change in any of the set of control files, a file copying process for the file system is disabled by the computer system.

Data recovery enhancement system

A method, apparatus, system, and computer program product for dynamically directing data in a deduplication backup system. A computer system identifies a data type for the data. The computer system determines whether the data is suitable for deduplication in the deduplication backup system based on the data type identified for the data and a policy. The computer system directs the data to a deduplication storage pool responsive to the data having the data type that is suitable for deduplication, reducing storage usage issues for backing up the data in the computer system.

Dynamically directing data in a deduplicated backup system

A backup and archival policy method, system, and non-transitory computer readable medium, includes performing correlation analytics to determine identification of a backup policy aligned with a criticality of operational data and backup data including identifying low value backup data having a value less than a predetermined low value threshold, creating a one-time archival of the operational data and the backup data including the low value backup data, and removing the low value backup data from a future data protection policy.

System, method, and recording medium for alignment of backup and archival policy

A method and associated systems for backing up a target computer application that comprises identifying and backing up databases and other information repositories upon which the target application depends. The target application is identified and related to a backup requirement that specifies a minimum application-backup frequency. The application's 'dependencies' are automatically identified from sources that include configuration files, where each dependency identifies one or more databases or other information repositories that store information upon which the application directly or indirectly depends. If any of these databases or repositories is associated with a backup frequency less than the application's minimum backup frequency, the database or repository is flagged. Each flagged entity's backup schedule is then revised so that the flagged entity's backup frequency is equal to or greater than the application's minimum backup frequency.

Enhancing robustness of a database application

Embodiments of the present invention provide methods, program products, and systems for determining an amount of usable storage in a backup environment. Embodiments of the present invention can be used to receive system information pertaining to a storage backup environment. Embodiments of the present invention can calculate a compression ratio of the storage on a backup server and a backup ratio between an amount of data on a client computer system and an amount of data on the backup server and calculate an average amount of storage consumed on the backup server. Embodiments of the present invention can determine an existing backup capacity for the storage backup environment by identifying an amount representing the actual capacity of data capable of being stored on the backup server and reducing the amount representing the actual capacity according to the calculated average amount of storage consumed on the backup server.

Capacity management

Optimized orchestration of data-migration projects with soft migration costs based on file-specific migration feasibilities

Backup schedule optimization is provided. Backup environment data is identified. Data describing a first backup schedule is identified. Performance metric data that includes data for a data storage array of computer data storage device(s) of a backup server is collected. Data describing backup log(s) that associated with jobs backed up to the data storage array and respectively executed during backup window(s) is received. A target average I/O rate is determined based on the performance metric data of the data storage array. A second backup schedule is generated based on the target average I/O rate, average I/O rate(s) of the data storage array, the backup environment data, and the data describing one or more backup logs. A start time of a job is scheduled in the second backup schedule for a different time in a backup window compared to a start time of the job in the first backup schedule.

Growing community of inventors

Grover Beach, CA, United States of America

Bernhard Julius Klingenberg