Other

Pure Storage, Inc.

Quantum Corporation

Hitachi Vantara Corporation

Hitachi Data Systems Corporation

Wells Fargo Capital Finance, LLC

Onstor, Inc.

Jeffrey Vincent Tofano

Ying Gao

Hari Kannan

Igor Ostrovsky

Boris Feigin

Shawn Anthony Hall

Jonathan Scott Goldick

Ronald Ray Trimble

Jon Christopher Kennedy

Svitlana Tumanova

Sankara Vaideeswaran

Thomas R Ramsdell

A storage system has a first memory, and a second memory that includes storage memory. The storage system has a processing device. The processing device is to select whether to write data to the first memory and write the data from the first memory to the second memory, or to write the data to the second memory bypassing the first memory. The processing device is to write portions of data for storage according to such selection.

Storage system with multiple write paths

A data segment to be written to a set of storage devices is identified, wherein at least one storage device of the set of storage devices has a first allocation unit size that is larger than a second allocation unit size of other storage devices of the set of storage devices. A first subsegment having a first subsegment height corresponding to the second allocation unit size is mapped across the set of storage devices. A second subsegment having a second subsegment height that is a first difference between the first allocation unit size and the second allocation unit size is mapped across a first subset of the set of storage devices. A third subsegment having a third subsegment height that is a second difference between the second allocation unit size and second subsegment height is mapped across a second subset of the set of storage devices.

Intelligent subsegment formation in a heterogeneous storage system

A storage system has a first memory, a second memory that include solid-state storage memory, and a processing device. The processing device is to select a mode for each portion of data to be written. Selection of the mode is based at least on size of the portion of data. Selection of the mode is from among modes that include a first mode of writing the portion of data in mirrored RAID form to the first memory for later transfer from the first memory to the second memory, a second mode of writing the portion of data in parity-based RAID form to the first memory for later transfer from the first memory to the second memory, and a third mode of writing the portion of data to the second memory, bypassing the first memory. The processing device is to handle portions of data to be written according to such selection.

Storage system with selectable write modes

Selectable write paths with different formatted data

Storage system with selectable write paths

In some examples, one or more computing devices may perform deduplication of data. For instance, a first device may receive, from a second device, a first data-portion identifier corresponding to a first deduplication data portion. The first device may include a first index portion of a deduplication index and the second device may include a second index portion of the deduplication index. Further, the first data-portion identifier may be received based on a first data-portion identifier portion being in a range of values assigned to the first index portion. The first device may locate, in the first index portion of the deduplication index, a second data-portion identifier that matches the first data-portion identifier. The first device may associate the first reference information for the first deduplication data portion with a second deduplication data portion referenced by reference information associated with the second data-portion identifier.

Deduplication index enabling scalability

Example apparatus, methods, and computers control configurable, variable data reduction. One example method includes identifying data reduction controlling attributes in an object to be data reduced by a configurable variable data reducer. The attributes provide information upon which decisions concerning whether and/or how to data reduce the object can be based. The example method also includes controlling a configurable variable data reducer to selectively data reduce the object based, at least in part, on the data reduction controlling attributes. The control exercised can determine whether, where, when, and/or how data reduction will proceed.

Controlling configurable variable data reduction

According to at least one embodiment, a data storage system is provided. The data storage system includes memory, at least one processor in data communication with the memory, and a deduplication director component executable by the at least one processor. The deduplication director component is configured to receive data for storage on the data storage system, analyze the data to determine whether the data is suitable for at least one of summary-based deduplication, content-based deduplication, and no deduplication, and store, in a common object store, at least one of the data and a reference to duplicate data stored in the common object store.

Multi-node hybrid deduplication

Example apparatus, methods, and computers support cloud-based de-duplication with transport layer transparency. One example apparatus includes a processor, a memory, and an interface to connect the processor, memory, and a set of logics. The set of logics includes a hardware identification logic for identifying networking hardware used by or available to the apparatus to interact with a cloud-based computing environment and a protocol identification logic for identifying a protocol used by or available to the apparatus to communicate messages for the cloud-based computing environment through the networking hardware. The set of logics also includes a de-duplication preparation logic for preparing a message to carry data to be de-duplicated and metadata concerning the data to be de-duplicated. The message is to be provided to the cloud-based computing environment through the networking hardware according to the protocol.

Cloud-based de-duplication

Example apparatus, methods, and computers support adaptive, collaborative deduplication. One example method includes controlling a computer to compute a correlation between a processing state associated with collaborative data deduplication performed by the computer and an environmental state associated with a collaborative data deduplication environment with which the computer interacts. With the correlation in hand, the method then includes controlling the computer to adapt how the computer participates in collaborative data deduplication based, at least in part, on the correlation. The adaptations can include changing what processing is performed on the computer and what data is stored on the computer.

Adaptive collaborative de-duplication

Example apparatus, methods, and computers control establishing a shared parse scope between two computers that intend to be involved in a shared de-duplication action. One example method includes, upon determining that a first de-duplication logic and a second de-duplication logic are to participate in a shared de-duplication action for an object, controlling the first de-duplication logic to establish a shared parse scope with the second de-duplication logic. Establishing the shared parse scope may include negotiations between the computers, where the negotiations transfer dialect information. The dialect information may take the form of rules. The method may also include persisting the shared parse scope.

Establishing parse scope

Example apparatus and methods associated with frequency and migration based re-parsing are provided. One example data de-duplication apparatus includes a migration logic and a parsing logic. The migration logic may be configured to perform a data transfer according to an access frequency to the data. The parsing logic may be configured to re-parse the data based on the access frequency to the data. In different examples, parsing the data may be performed in response to migrating the data. In one example, parsing the data may be performed during or after the migration. Additional examples illustrate parsing the data to balance performance against reduction in light of access frequency to the data block.

Frequency and migration based re-parsing

Example methods and apparatus concern creating and/or manipulating a tape-specific layout for a tape based de-duplication repository. One example apparatus include a processor, a memory, a set of logics and an interface to connect the processor, the memory, and the set of logics. The apparatus may include a tape layout logic configured to determine the tape-specific layout for a de-duplication data set. The tape-specific layout may be based on forensic data acquired for the de-duplication data set. The apparatus may also include a write logic configured to write the de-duplication data set to a tape according to the tape-specific layout. The forensic data may identify, for example, the order in which sub-blocks are accessed, reference counts, access frequency, access groups, and other access information.

Controlling tape layout for de-duplication

Example methods and apparatus concern identifying placement and/or erasure data for a flash memory based solid state device that supports de-duplication. One example apparatus include a processor, a memory, a set of logics and an interface to connect the processor, the memory, and the set of logics. The apparatus may include an SSD placement logic configured to determine placement data for a de-duplication data set. The placement data may be based on forensic data acquired for the de-duplication data set. The apparatus may also include a write logic configured to write at least a portion of the de-duplication data set to an SSD as controlled by the placement data. The forensic data may identify, for example, the order in which sub-blocks are accessed, reference counts, access frequency, access groups, and other access information.

Data de-duplication and solid state memory device

Example apparatus, methods, and computers support data de-duplication indexing. One example apparatus includes a processor, a memory, and an interface to connect the processor, memory, and a set of logics. The set of logics includes an establishment logic to instantiate one-to-many de-duplication data structures, a manipulation logic to update the de-dupe data structure(s), a key logic to generate a key from a block of data to be de-duplicated, and a similarity logic to make a similarity determination for the block. The similarity determination identifies the block as a unique block, a duplicate block, or a block that meets a similarity threshold with respect to a stored de-duplicated block accessible through the dedupe data structure. The similarity determination involves comparing the block to be de-duplicated to a stored block available to the apparatus using a byte-by-byte approach, a hash approach, a delta hash approach and/or a sampling sequence approach.

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San Jose, CA, United States of America

Jeffrey Vincent Tofano