Pure Storage, Inc.

Ethan L Miller

John Colgrove

John Martin Hayes

Bo Hong

Cary A Sandvig

Feng Wang

Ronald S Karr

Christopher Golden

Craig Harmer

Andrew R Bernat

Jianting Cao

Joseph S Hasbani

Lydia Mai Do

Benjamin P Borowiec

Steve Hodgson

Neil A Vachharajani

Terence W Noonan

Robert H Lee

Malcolm Sharpe

Constantine P Sapuntzakis

Grigori Inozemtsev

Naveen Neelakantam

Par Botes

Taher Vohra

Marco Sanvido

John D Davis

Daquan Zuo

Jimmy T Hu

Ganesh Ramanarayanan

Timothy W Brennan

Mark L McAuliffe

Alan Stuart Driscoll

Wei Zhang

Igor Ostrovsky

Brian Gold

Peter Vajgel

Christopher R Lumb

Joern W Engel

Jonas R Irwin

Alex Ho

Yuhong Mao

Benjamin Scholbrock

Patrick D Lee

Andrew Kleinerman

Peter E Kirkpatrick

Damian Pablo Yurzola

Vinay Perneti

David Whitlock

Arun Rokade

Rajesh Kanungo

Alexandre Xavier Duchâteau

Nicholas George Psaki

Intra-block error correction including generating reconstructed sub-portions of a portion of a dataset that is distributed into a plurality of portions that are stored on different storage devices of the storage system, wherein the dataset indicates a first number of errors that is greater than a second number of parity data portions of the dataset, determining that a sub-portion of the dataset does not match a corresponding reconstructed sub-portion of the dataset; and based on the determination, replacing the sub-portion of the dataset with the reconstructed sub-portion of the dataset.

Using intra-block redundancy calculations

Deduplicating data based on recently reading the data, including: determining whether a calculated signature for write data matches a particular signature corresponding to data that was recently read from the storage device, wherein the signature is calculated using the write data as input; and after determining that the calculated signature for the write data matches the particular signature, obtaining the data that was recently read and comparing the data that was recently read to the write data.

Identifying duplicate data based on recently read data

A computer system comprising: a data storage medium comprising a plurality of storage devices configured to store data; and a data storage controller coupled to the data storage medium; wherein the data storage controller is configured to: determine a target number of storage devices in a group of storage devices in a storage system that may exhibit variable I/O response times; detect one or more conditions associated with the group of storage devices; and after detecting the one or more conditions, change the target number of storage devices that may exhibit variable I/O response times.

Balancing the number of read operations and write operations that may be simultaneously serviced by a storage system

Self-tuning clusters for resilient microservices, including: receiving, by a services orchestrator within a cloud-computing environment and from a plurality of cloud computing instances, respective latency measurements corresponding to respective control plane operations directed to a shared resource of the plurality of cloud computing instances; determining, based on a current timeout value and on the respective latency measurements from the plurality of cloud computing instances, an updated timeout value for the shared resource; and providing, to each of the plurality of cloud computing instances, the updated timeout value for the shared resource.

Orchestrating self-tuning for cloud storage

Systems and method for implementing deduplication process based on performance analyses. The system may include a processing device to determine a first performance metric associated with retrieving a second stored data block that is within a specified range of a duplicate of the first data block and a second performance metric associated with retrieving a hash value corresponding to the second stored data block. The processing device further to retrieve the second stored data block within a specified range of the duplicate of the first data block in response to the first performance metric not exceeding the second performance metric.

Deduplication selection and optimization

A system and method for managing tables in a storage system is described.

Flash system having multiple fingerprint tables

In one implementation, a method includes identifying a first content-dependent feature associated with a data sector. The method further includes determining a baseline data sector associated with the data sector. The method further includes determining, by a processing device, a content-dependent delta between the first content-dependent feature and a second content-dependent feature of the baseline data sector. The method further includes providing the content-dependent delta and an indicator to the baseline data sector for storage on a plurality of storage devices.

Similarity data for reduced data usage

Data protection in a storage system that includes a plurality of Non-Volatile Memory Express ('NVMe') Solid State Drives (‘SSDs’), including: retrieving, from a plurality of NVMe SSDs (‘Non-Volatile Memory Express Solid State Drives’) of a storage system, one or more unencrypted shares of a master secret; reconstructing the master secret using the shares of the master secret; decrypting one or more encrypted device keys using the master secret; and using the decrypted device keys to perform a plurality of accesses to one or more of the NVMe SSDs.

Encrypting data in a storage device

Providing for increased flexibility for large scale parity, the including: writing data to a storage system, including utilizing a first data protection scheme; identifying, for storage media in the storage system, characteristics of the storage media; identifying, in dependence up the characteristics for the storage media, a second data protection scheme to use for the data; and writing the data to the to the storage system utilizing the second data protection scheme.

Data storage based on characteristics of storage media

Systems and methods of deduplication aware scalable content placement are described. A method may include receiving data to be stored on one or more nodes of a storage array and calculating a plurality of hashes corresponding to the data. The method further includes determining a first subset of the plurality of hashes, determining a second subset of the plurality of hashes of the first subset, and generating a node candidate placement list. The method may further include sending the first subset to one or more nodes represented on the node candidate placement list and receiving, from the nodes represented on the node candidate placement list, characteristics corresponding to the nodes represented on the candidate placement list. The method may further include identifying one of the one or more nodes represented on the candidate placement list in view of the characteristic and sending the data to the identified node.

Storage node data placement utilizing similarity

Methods, computer systems, and computer readable medium are described for low latency reads using cached deduplicated data, including: receiving a request to read data from a storage system; query, using a generated hash value associated with the request to read data, one or more deduplication tables that corresponds to the hash value; and responsive to determining that the one or more deduplication tables includes an entry that corresponds to the hash value, using a mapping contained in the entry to perform the requested to read data, wherein the mapping includes a pointer to a physical location where at least a portion of the data is stored.

Low latency reads using cached deduplicated data

Periodically re-encrypting user data stored on a storage device, including: determining that data stored in a first location of a storage device is encrypted with a data encryption key that has been decommissioned; re-encrypting the data utilizing a current data encryption key; and writing the data that is encrypted utilizing the current data encryption key to a second location of the storage device.

Re-keying the contents of a storage device

A system, method, and product for flexible RAID layouts in a storage system, including: determining a reliability of an individual storage device of a plurality of storage devices, the individual storage device containing a plurality of portions of a Redundant Array of Independent Disks (RAID) stripe in a storage system, wherein the RAID stripe includes user data and inter-device parity data; detecting a change in the reliability of the individual storage device that contains the portion corresponding to the RAID stripe; and changing an amount of intra-device protection corresponding to the RAID stripe by decreasing, in the RAID stripe, an amount of space used to store the inter-device protection data.

RAID protection updates based on storage system reliability

A system and method comprising: receiving a request to write data stored at a first range of a first volume to a second range of a second volume, where first metadata for the first range of the first volume is associated with a range of physical addresses where the data is stored in the storage system; and responsive to receiving the request: creating second metadata for the second range of the second volume, wherein the second metadata is associated with the range of physical addresses where the data is stored in the storage system; and associating the second volume with the second metadata.

Lightweight copying of data using metadata references

A method for deleting a set of keys from a storage server is provided. The method includes generating a probabilistic data structure for a first set of keys and for each key in a second set of keys, determining whether a key of the second set of keys is found in the probabilistic data structure. The method includes identifying the key as a candidate for deletion if the key is not found in the probabilistic data structure. A system is also provided.

Probalistic data structure for key management

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Mountain View, CA, United States of America

Ethan L Miller