International Business Machines Corporation

Pure Storage, Inc.

Cleversafe, Inc.

Moshun, LLC

Other

Purage Storage, Inc.

Jason K Resch

Wesley B Leggette

Greg R Dhuse

Andrew D Baptist

Ilya Volvovski

Gary W Grube

Manish Motwani

S Christopher Gladwin

Timothy W Markison

Ravi V Khadiwala

Thomas Franklin Shirley, Jr

Bart Richard Cilfone

Ethan S Wozniak

Zachary J Mark

Asimuddin Kazi

Yogesh Ramesh Vedpathak

Kumar Abhijeet

Thomas Darrel Cocagne

Michael Colin Storm

Adam Michael Gray

Brian Frederick Ober

John Edward Buchalo

Richard Michael Lang

Scott Michael Horan

Timothy John Boundy

Steven Michael Barger

Terence Michael Lydon

Eric McHugh

Praveen Viraraghavan

Kurt A Estes

Richard A Herbst

Darren Michael Boundy

George L Wilson, Iv

Evan Anderson

Wilfredo Gonzalez, Jr

Trevor J Vossberg

Niall John McShane

Daniel J Scholl

Mark Duane Seaborn

Sebastien Vas

David Schuda

Mario Frank DeRango

Ahmad Alnafoosi

John Quigley

Bruno Hennig Cabral

Vance T Thornton

Andrew George Peake

Teague Scott Algie

David J Schuda

Harsha Hegde

Ilir Iljazi

Hugo M Krawczyk

Joseph M Kaczmarek

Dustin M Hendrickson

Renars W Narubin

Robert Clark McCammon

Russell Paul Kennedy

Rohan P Shah

Franco Vincent Borich

Srinivas Palthepu

Bart Richard Clifone

Brennan James Trichardt

Alan E Holmes

Mario F Derango

James L Lester

Eric Gunnar Smith

Patrick Aaron Tamborski

Ram S Narayanan

Alan M Frazier

Thomas Dubucq

Ying Zhao Guo

Tyler K Reid

Steven Mark Hoffman

Gregory A Papadopoulos

Ramin Rouzbeh

Siyuan Ma

Adam D Eggum

Vimalkumar P Gajjar

Gregory Palinckx

James Sherer

Jeremy S Jarczyk

Martin L Schmatz

Michael Edward Factor

Patricia Maria Sagmeister

Vita Bortnikov

Matthew Michael England

Amit H Lamba

Jamie Bellanca

Mingyu Li

Anthony J Baldocchi

Dusty Hendrickson

Shibhani Rai

Dennis A Kalaf

Nataraj Nagaratnam

Roberta Jo Cochrane

Sanjaya Kumar

Erlander Lo

Jesse Louis Young

Kevin Michael Freese

Michael J Niedbala

Methods and systems for use in a storage network to prioritize storage units for data retrieval operations. In various examples, a device obtains resource utilization information and pending resource demand information for a plurality of storage units of a storage network. The device further groups, based on the resource utilization information, the storage units into an underutilized resource group and an overutilized resource group, and further issues one or more high priority read slice access requests to the storage units of the underutilized resource group. The read slice access requests correspond to a pending resource demand for data retrieval. In response to determining that the high priority read access information does not include at least a read threshold number of read slice requests, the device further issues one or more additional read slice requests to one or more storage units of the overutilized resource group.

Prioritizing storage units for data retrieval operations

A processing system of a storage network operates by: sending, to at least one storage unit of the storage network, at least one read request corresponding to at least a read threshold number of a set of encoded data slices to be retrieved, wherein the set of encoded data slices correspond to data, wherein the data is coded in accordance with dispersed error coding parameters that include a write threshold number and the read threshold number, wherein the write threshold number is a number of encoded data slices in the set of encoded data slices and wherein the read threshold number is a number of the set of encoded data slices that is required to decode the data; receiving, at the at least one processing circuit and from the at least one storage unit, a first subset of the set of encoded data slices, wherein at least one missing encoded data slice was not included in the first subset and wherein a number of encoded data slices in the first subset is less than the read threshold number; generating, via the at least one processing circuit, at least one rebuilt encoded data slice corresponding to the at least one missing encoded data slice utilizing locally decodable redundancy data, wherein the locally decodable redundancy data corresponds to a second subset of the set of encoded data slices that includes the at least one missing encoded data slice and wherein the locally decodable redundancy data is stored locally to the processing circuit; and recovering, via the at least one processing circuit, the data based on the at least one rebuilt encoded data slice and the first subset.

Recovering missing data in a storage network via locally decodable redundancy data

A method for a storage network computing device includes receiving a request to store a data object and generating a plurality of write slice requests, where the plurality of write slice requests includes a set of encoded data slices to be stored in the storage network and at least a decode threshold number of encoded data slices of the set of encoded data slices is required to recover a corresponding portion of a data object. The method continues by identifying a set of storage devices (from a plurality) for storage of the set of encoded data slices, where the plurality of storage devices includes one or more first memory devices local to the computing device and one or more second memory devices that are remote from the computing device. The method then continues, by storing the set of encoded data slices in the identified set of storage devices, generating a set of write slice responses relating to the set of encoded data slices and outputting the set of write slice responses.

Virtualization in a dispersed storage network utilizing local and remote memory

A method includes selecting a plurality of groups of storage units from a number of storage units based on a plurality of sets of storage pool traits, where a first group of storage units of the plurality of groups of storage units is based on a first set of storage pool traits of the plurality of sets of storage pool traits. The method further includes selecting a storage unit from each of the plurality of groups of storage units in accordance with a selection approach to produce a storage set of selected storage units. The method further includes utilizing the storage set of selected storage units for storing data in the storage network.

Trait based storage unit groups

A head unit system for controlling an object includes a head unit device that include shear thickening fluid (STF) and a chamber configured to contain the STF. The chamber further includes a gate between a front channel and a back channel. The gate includes a set of bypass openings. The head unit device further includes a piston housed at least partially radially within the chamber. The gate is configured to control flow of the STF between the front channel and the back channel to control contraction of the chamber to provide the controlling of the object.

Telescoping shear thickening fluid based object control mechanism

A storage network operates by: sending a slice verification request to a destination storage unit, wherein the slice verification request includes a nonce and one or more of: a slice identifier, the slice, a revision indicator, or a verification method indicator; receiving an integrity value from the destination storage unit; determining when the integrity value is verified by determining one of: when a hash of the slice and the nonce matches the integrity value; when a decrypted signature of the integrity value matches a hash of the slice and the nonce; or when the decrypted signature of the integrity value matches the slice and the nonce; and when the integrity value is verified, updating a slice assignment corresponding to the slice, wherein updating the slice assignment associates the destination storage unit with the slice and disassociates the source storage unit from the slice.

Verifying slice migration in a storage network

A method for execution by a computing entity includes interpreting an electric response from a set of electric field sensors to produce a piston velocity and a piston position of a piston associated with a head unit device. The head unit device includes a chamber filled with a shear thickening fluid (STF) that includes a multitude of piezoelectric nanoparticles. The method further includes determining a shear force based on the piston velocity and the piston position. The method further includes determining a desired response for the STF based on the shear force, the piston velocity, and the piston position. The method further includes generating an electric activation based on the desired response for the STF and outputting the electric activation to a set of electric field emitters positioned proximal to the chamber.

Dilatant fluid based object movement control mechanism

A method includes determining rebuilding performance parameter values for a storage unit of a set of storage units, where data is dispersed storage error encoded into a plurality of sets of encoded data slices in accordance with error encoding parameters, and a set of encoded data slices are stored or are to be stored in the set of storage units. The method includes updating a rate of internal rebuilding of encoded data slices by the storage unit based on the rebuilding performance parameter values. During internally rebuilding, the method includes correcting detected storage errors for one or more first encoded data slices within the storage unit. The method includes receiving a rebuilt encoded data slice from another computing device of the storage network and when the received rebuilt encoded data slice is not included in the one or more first encoded data slices, storing the received rebuilt encoded data slice.

Dispersed rebuilding of encoded data slices in a storage network

A method for execution by a computing device to adjust efficiency of storing data in a storage network includes processing a data segment based on a storage approach to produce a processed data segment, where the storage approach is based on an estimated update frequency for the data segment. The method continues by storing the data segment in the memory of the storage network in accordance with the storage approach.

Dynamic data storage based on estimated update frequency

Methods and apparatus for preference based selection of storage network memory for data storage. In an example, a computing device receives a data object for storage in memory of the storage network and determines a system level storage efficiency preference associated with the data object. The computing device selects a set of storage nodes of a plurality of sets of storage nodes for storage of the data object based, at least in part, on the system level storage efficiency preference. The computing device further determines dispersed storage error encoding parameters for the data object, encodes the data object in accordance with the dispersed storage error encoding parameters to produce encoded data slices, and generates system addressing information for the encoded data slices.

Preference based selection of storage network memory for data storage

A processor in a storage network operates by: receiving an access request for a data segment, wherein the data segment is encoded utilizing an error correcting information dispersal algorithm as a set of encoded data slices that are stored in a plurality of storage units of the storage network and wherein each encoded data slice of the set of encoded data slices includes a corresponding checksum of a plurality of checksums; retrieving, from the storage network, a subset of encoded data slices that includes a threshold number of encoded data slices of the set of encoded data slices; determining, based on ones of the plurality of checksums corresponding to the subset of encoded data slices, when the subset of encoded data slices includes at least one corrupted encoded data slice; retrieving from at least one of the plurality of storage units an addition number of encoded data slices required to generate a reconstructed data segment based on the subset of encoded data slices; generating the reconstructed data segment in accordance with the error correcting information dispersal algorithm, using the additional number of encoded data slices and at least some of the subset of encoded data slices; providing the reconstructed data segment in response to the access request; forming a reconstructed set of encoded data slices utilizing the error correcting information dispersal algorithm on the reconstructed data segment; and replacing the at least one corrupted encoded data slice with at least one reconstructed encoded data slice of the reconstructed set of encoded data slices.

Data reconstruction in a storage network and methods for use therewith

A method for execution by a computing entity includes interpreting a magnetic response from a set of magnetic field sensors to produce a piston velocity and a piston position of a piston associated with a head unit device. The head unit device includes a chamber filled with a shear thickening fluid (STF) that includes a multitude of magnetic nanoparticles. The method further includes determining a shear force based on the piston velocity and the piston position. The method further includes determining a desired response for the STF based on the shear force, the piston velocity, and the piston position. The method further includes generating a magnetic activation based on the desired response for the STF and outputting the magnetic activation to a set of magnetic field emitters positioned proximal to the chamber.

A method includes storing a plurality of data in a storage system. A plurality of identifiers corresponding to the plurality of data is determined and the plurality of identifiers are stored in the storage system. A first set of integrity information corresponding to a first system storage level is generated for the plurality of data by performing a first set of cyclic redundancy checks and the first set of integrity information is stored in the storage system. A second set of integrity information corresponding to a second system storage level is generated for the plurality of data and the second set of integrity information is stored in the storage system.

Generating multiple sets of integrity information in a vast storage system

A head unit system for controlling motion of an object includes an environment sensor and a head unit that include shear thickening fluid (STF) and a chamber to contain the STF. The chamber further includes front and back channels. The head unit further includes a piston housed radially within the piston compartment and separating the back channel and the front channel. The piston includes a first piston bypass and a second piston bypass to control flow of the STF between opposite sides of the piston. The chamber further includes a set of fluid flow sensors and a set of fluid manipulation emitters to control the flow of the STF to cause selection of one of a variety of shear rates for the STF within the chamber to abate an internal factor of concern associated with an internal environment as sensed by the environment sensor.

Environmental based shear thickening fluid control method and mechanism

A head unit system for controlling motion of an object includes a secondary object sensor and a head unit that includes shear thickening fluid (STF) and a chamber configured to contain the STF. The chamber further includes a front channel and a back channel. The head unit further includes a piston housed at least partially radially within the piston compartment and separating the back channel and the front channel. The piston includes a first piston bypass and a second piston bypass to control flow of the STF between opposite sides of the piston. The chamber further includes a set of fluid flow sensors and a set of fluid manipulation emitters to control the flow of the STF to cause selection of one of a variety of shear rates for the STF within the chamber to control motion of the object with regards to a secondary object.

Pattern based shear thickening fluid object control method and mechanism

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Chicago, IL, United States of America

Jason K Resch