International Business Machines Corporation

Globalfoundries Inc.

Karl Allen Nielsen

Lokesh M Gupta

Matthew G Borlick

Clint Alan Hardy

Matthew J Kalos

Michael T Benhase

Brian Anthony Rinaldi

David Ray Kahler

Kevin J Ash

Richard B Stelmach

David Frank Mannenbach

Chao Li

Todd Charles Sorenson

Robert Akira Kubo

Qiang Xie

Xue Feng Gao

Jacob Lee Sheppard

Roger Gregory Hathorn

Carl Evan Jones

James Lee Hafner

Micah Robison

Kurt Allen Lovrien

Evangelos Stavros Eleftheriou

Roman Alexander Pletka

Ioannis Koltsidas

Robert Haas

Thomas Charles Jarvis

Michael Howard Hartung

Stephen LaRoux Blinick

Xiao-Yu Hu

Ellen Joanne Grusy

Jeremy Michael Pinson

John Charles Elliott

Trung Ngoc Nguyen

Brian James Cagno

David Montgomery

Michael P Vageline

Krishnakumar Rao Surugucchi

Steven Robert Lowe

Yao Peng

Tang Ren Yao

James A O'Conner

Pui Fun Lau

Beth Ann Peterson

Matthew Sanchez

Daniel Frank Moertl

Gail Andrea Spear

Carol Santich Mellgren

Richard Anthony Ripberger

James A O'Connor

Ian David Judd

Cheng-Chung Song

Matthew D Carson

Richard P Oubre, Jr

Colleen Renee Stouffer

Keith Geoffrey Morrison

Krishnakumar S Rao

Roah Jishi

Jeremy Leigh Royall

Daniel James Winarski

Nils Haustein

Craig Anthony Klein

Theresa Mary Brown

William Frank Micka

Robert Edward Galbraith

Larry Juarez

Kenneth Wayne Todd

Lee Jason Sanders

Ulf Troppens

Matthew John Fairhurst

Thomas J Fleischman

Louis Alonso Rasor

Suguang Li

David Blair Whitworth

Sam Clark Werner

Richard Hutzler

Michael Steven Faunce

Joseph R Edwards

Michael John Jones

Benjamin Jay Donie

William Henry Travis

Seamus Burke

Sean Patrick Riley

William King Morse

Kevin C Lin

Hai Bo Qian

Mitchell Montanez

Amine M Hajji

Scott Alan Bauman

Bradley A Bernhardt

Jeffrey R Steffan

Roderick G C Moore

Jeremy Andrew Cohn

Michael Thomas Benhase, Jr

Anjul Mathur

Samantha A Utter

Michael J Anderson

YuCheng Hsu

Robert John Kolvick, Jr

Michael T Roble

Provided are a computer program product, system, and method for determining to migrate data between compressed storage devices and non-compressed storage devices in a storage system. The storage system provides compressed storage devices having native data compression and a non-compressed storage devices not configured to have native compression. A determination is made of storage usage factors for the compressed storage devices and the non-compressed storage devices. A determination is made whether to move data between the compressed storage devices and the non-compressed storage devices based on the storage usage factors. Data is moved between the compressed storage devices and the non-compressed storage devices in response to determining to move the data between the compressed storage devices and the non-compressed storage devices.

Determining to migrate data between compressed storage devices and non-compressed storage devices in a storage system

Provided are a computer program product, system, and method for determining a storage device type on which to store extents of data in a storage system. The storage system provides a first storage device type that has native data compression at a first compression ratio and a second storage device type that has native data compression at a second compression ratio. The second compression ratio is higher than the first compression ratio. A determination is made for an extent to store the extent on the first storage device type or the second storage device type based on access frequency to data in the extent and an Input/Output (I/O) priority level for the extent. The extent is stored on the determined first or second storage device type.

Determining a storage device type on which to store extents of data in a storage system

A secondary storage controller determines one or more tracks of one or more volumes in which data loss has occurred in the secondary storage controller. The secondary storage controller suspends a peer to peer remote copy operation between the secondary storage controller and a primary storage controller. Information on the one or more tracks of the one or more volumes in which the data loss has occurred is transmitted to the primary storage controller.

Data loss recovery in a secondary storage controller from a primary storage controller

A computer-implemented method according to one embodiment includes identifying a request to migrate data associated with a volume from a first storage pool to a second storage pool; identifying entries in a first table corresponding to rank extents in the first storage pool containing the data; allocating and synchronizing a plurality of second tables for the identified entries of the first table that are located in the volume; transferring the data associated with the volume from the rank extents in the first storage pool containing the data to one or more rank extents in the one or more ranks of the second storage pool; and updating the second tables to correspond to the transferred data in the one or more rank extents in the one or more ranks of the second storage pool.

Migrating data from a large extent pool to a small extent pool

A computer-implemented method according to one embodiment includes identifying a request to migrate data associated with a volume from a source storage pool having a first rank extent size to a destination storage pool having a second rank extent size smaller than the first rank extent size, creating a correspondence between logical volume extents of the volume and physical offset locations within rank extents of the source storage pool, and migrating data from one or more ranks of the source storage pool to one or more ranks of the destination storage pool, utilizing the correspondence between the logical volume extents of the volume and the physical offset locations within the rank extents of the source storage pool.

A method for improving destage performance to a RAID array is disclosed. In one embodiment, such a method periodically scans a cache for first strides that are ready to be destaged to a RAID array. While scanning the cache, the method identifies second strides that are not currently ready to be destaged to the RAID array, but will likely be ready to be destaged during a subsequent scan of the cache. The method initiates preemptive staging of any missing data of the second strides from the RAID array into the cache in preparation for the subsequent scan. Upon occurrence of the subsequent scan, the method destages, from the cache, the second strides from the cache to the RAID array. A corresponding system and computer program product are also disclosed.

Preemptive staging for full-stride destage

A method according to one embodiment includes identifying a request to migrate data associated with a volume from a source storage pool to a destination storage pool, identifying volume segment table (VST) entries corresponding to rank extents within the source storage pool containing the data, allocating and synchronizing small VSTs for the identified VST entries within the volume, allocating one or more rank extents within the destination storage pool, transferring the data associated with the volume from the rank extents within the source storage pool containing the data to the one or more rank extents in the one or more ranks of the destination storage pool, updating the small VSTs to correspond to the transferred data in the one or more rank extents in the one or more ranks of the destination storage pool, and freeing the data from the one or more rank extents within the source storage pool.

A method for reducing incidents of data loss in redundant arrays of independent disks (RAIDs) having the same RAID level is disclosed. In one embodiment, such a method identifies, in a data storage environment, a set of RAIDs having a common RAID level. The method also identifies, in the set of RAIDs, higher risk storage drives having a failure risk above a threshold and lower risk storage drives having a failure risk below the threshold. The method swaps, within the RAIDs, higher risk storage drives with lower risk storage drives to more evenly distribute higher risk storage drives across the RAIDs. A corresponding system and computer program product are also disclosed.

Reducing incidents of data loss in raid arrays having the same raid level

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.

Selecting a priority queue from which to process an input/output (I/O) request using a machine learning module

A computer-implemented method, according to one approach, is for converting an existing storage pool into an intended storage pool in place. The computer-implemented method includes: reordering volume extents in the existing storage pool such that physical rank extents of the volumes align with boundaries between rank extents of the intended storage pool. The ranks in the existing storage pool are also converted from first rank extent sizes to second rank extent sizes. Moreover, in response to converting the ranks in the existing storage pool, for each of the volumes: a temporary pause/lock is performed on a volume segment table (VST) entry which corresponds to the given volume, and the VST entry is populated with a new structure which corresponds to the respective second rank extent size.

Converting small extent storage pools into large extent storage pools in place

A storage controller is configured to perform a full stride destage, a strip destage, and an individual track destage. A machine learning module receives a plurality of inputs corresponding to a plurality of factors that affect performance of data transfer operations and preservation of drive life in the storage controller. In response to receiving the inputs, the machine learning module generates a first output, a second output, and a third output that indicate a preference measure for the full stride destage, the strip destage, and the individual track destage respectively.

Determination of a type of destage to perform based on preference between performance of operations and preservation of drive life using a machine learning module

A first score corresponding to a full stride destage, a second score corresponding to a strip destage, and a third score corresponding to an individual track destage are computed, wherein the first score, the second score, and the third score are computed for a group of Input/Output (I/O) operations based on a first metric and a second metric, wherein the first metric is configured to affect a performance of data transfers, and wherein the second metric is configured to affect a drive life. A determination is made of a type of destage to perform based on the first score, the second score, and the third score.

Determination of a type of destage to perform based on preference between performance of operations and preservation of drive life

Provided are a computer program product, system, and method for using a machine learning module to determine when to perform error checking of a storage unit. Input on attributes of at least one storage device comprising the storage unit are provided to a machine learning module to produce an output value. An error check frequency is determined from the output value. A determination is made as to whether the error check frequency indicates to perform an error checking operation with respect to the storage unit. The error checking operation is performed in response to determining that the error checking frequency indicates to perform the error checking operation.

Using a machine learning module to determine when to perform error checking of a storage unit

Provided are a computer program product, system, and method in accordance with one embodiment of the present description, in which selected memory resources targeted for secure data cleaning. For example, memory resources which may be targeted for secure data cleaning may include a specific volume (or portion of a volume) of data storage which has been selected for secure erasure of any residual data which may remain on the targeted storage volume before the volume is released for use by others. Other examples of memory resources which may be targeted for secure data cleaning in accordance with the present description may include one or more buffers located in switches or other storage area network components which provide data paths between hosts and storage controllers or storage control units. Other aspects of targeted secure data overwriting in accordance with the present description are described.

Targeted secure data overwrite

Growing community of inventors

Tucson, AZ, United States of America

Karl Allen Nielsen