WO2016186602A1 - Priorisation de suppression - Google Patents

Priorisation de suppression Download PDF

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Publication number
WO2016186602A1
WO2016186602A1 PCT/US2015/030937 US2015030937W WO2016186602A1 WO 2016186602 A1 WO2016186602 A1 WO 2016186602A1 US 2015030937 W US2015030937 W US 2015030937W WO 2016186602 A1 WO2016186602 A1 WO 2016186602A1
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WO
WIPO (PCT)
Prior art keywords
data entity
subsets
subset
storage locations
information content
Prior art date
Application number
PCT/US2015/030937
Other languages
English (en)
Inventor
Dave DONAGHY
Ben SIMPSON
John Butt
Original Assignee
Hewlett Packard Enterprise Development Lp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hewlett Packard Enterprise Development Lp filed Critical Hewlett Packard Enterprise Development Lp
Priority to PCT/US2015/030937 priority Critical patent/WO2016186602A1/fr
Publication of WO2016186602A1 publication Critical patent/WO2016186602A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10—File systems; File servers
    • G06F16/16—File or folder operations, e.g. details of user interfaces specifically adapted to file systems
    • G06F16/162—Delete operations

Definitions

  • Computer systems include storage devices to store parts of a data entity such as file, database table, memory page, communication stream, etc for subsequent retrieval.
  • a data stream may be written and/or stored as separate parts in multiple storage locations.
  • FIG. 1 is a block diagram of an example deletion prioritization device
  • FIG. 2A is a flowchart of an example of a method for deletion prioritization
  • FIG. 2B is a flowchart of an example of a method for a deletion operation.
  • FIG. 3 is a block diagram of an example system for deletion prioritization.
  • a data stream may be written and/or stored as separate parts in multiple storage locations.
  • Deletion prioritization may be used when a data entity is requested to be deleted. As a data stream is stored, it may be written to multiple physical and/or logical storage locations (e.g., different hard drives or memory segments). Deletion prioritization assigns an order to the storage locations so that if the deletion operation is interrupted, the highest priority of these locations will have been deleted.
  • machine-readable storage medium refers to any electronic, magnetic, optical, or other physical storage device that stores executable instructions or other data (e.g., a hard disk drive, random access memory, flash memory, etc.).
  • FIG. 1 is a block diagram of an example deletion prioritization device 100 consistent with disclosed implementations.
  • Deletion prioritization device 100 may comprise a processor 1 10 and a non-transitory machine-readable storage medium 120.
  • Deletion prioritization device 100 may comprise a computing device such as a server computer, a desktop computer, a laptop computer, a handheld computing device, a smart phone, a tablet computing device, a mobile phone, or the like.
  • Device 100 may further comprise a data storage 150.
  • Processor 1 10 may comprise a central processing unit (CPU), a semiconductor-based microprocessor, or any other hardware device suitable for retrieval and execution of instructions stored in machine-readable storage medium 120.
  • processor 1 10 may fetch, decode, and execute a plurality of receive request instructions 130, assign priority instructions 132, and delete data entity instructions 134 to implement the functionality described in detail below.
  • Device 100 may further comprise a plurality of data storage locations 150(A)-(D).
  • Executable instructions may be stored in any portion and/or component of machine-readable storage medium 120.
  • the machine-readable storage medium 120 may comprise both volatile and/or nonvolatile memory and data storage components. Volatile components are those that do not retain data values upon loss of power. Nonvolatile components are those that retain data upon a loss of power.
  • the machine-readable storage medium 120 and data storage locations 150(A)-(D) may comprise, for example, random access memory (RAM), read-only memory (ROM), hard disk drives, solid-state drives, USB flash drives, memory cards accessed via a memory card reader, floppy disks accessed via an associated floppy disk drive, optical discs accessed via an optical disc drive, magnetic tapes accessed via an appropriate tape drive, and/or other memory components, and/or a combination of any two and/or more of these memory components.
  • the RAM may comprise, for example, static random access memory (SRAM), dynamic random access memory (DRAM), and/or magnetic random access memory (MRAM) and other such devices.
  • the ROM may comprise, for example, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), and/or other like memory device.
  • PROM programmable read-only memory
  • EPROM erasable programmable read-only memory
  • EEPROM electrically erasable programmable read-only memory
  • Data storage locations 150(A)-(D) may comprise physical storage devices, such as a hard disk drive and/or a solid state drive, and/or a logical storage device, such as a database, a user-based logical data storage (e.g., a user's home directory and associated files), a network-attached storage, and a logical partition.
  • a logical storage device may comprise data stored on part of and/or across a plurality of physical storage devices.
  • Receive request instructions 130 may receive a request to delete a data entity stored across a plurality of storage locations.
  • a user, application, service and/or process may request a deletion of a data entity such as a file and/or a group of files.
  • Other data entities may comprise, for example, database entries, memory pages, network streams, application packages, etc.
  • Such data entities may be stored across data storage locations 150(A)-(D) according to a format of the data entity and a type of the storage locations.
  • a large file may be fragmented, with a first part of the file stored at a first memory location on data storage 150(A), a second part of the file stored at a second location on data storage 150(B), a third part of the file stored at a third location on data storage 150(C), and so on.
  • an application may store a data entity in a particular file format that combines content data with template and/or style data stored in different locations, such as a web page file that comprises a link to a stylesheet file.
  • Assign priority instructions 132 may assign a priority to each of the plurality of storage locations according to an information content value associated with each of the plurality of storage locations. For example, the locations may be assigned their priority with the location having the highest information content value being assigned the highest priority.
  • the information content value may comprise a rating of the density of information stored in a particular location.
  • the information content value may be calculated as the data entity is originally written to storage locations 150(A)-(D), as the data entity is updated or refreshed, and/or at the time the request for deletion is received.
  • a data entity may comprise an MPEG video stream.
  • Such a video stream may be broken into subsets stored in different places comprising a series of frame types comprising l-frames, P-frames, and B-frames.
  • An l-frame comprises all of the information needed to decode the data into a single image frame, and has a high information content value.
  • a P-frame relies on data from a previous frame to complete the image frame rather than comprising all of the necessary data, and so comprises a medium information content value.
  • a B- frame further relies on data from previous and forward frames, and comprises the lowest information content value of the frame types.
  • a text document may be stored as subsets comprising primary text content comprising a high information content value, formatting and/or style information comprising a medium information content value, and empty padding comprising a low information content value.
  • the information content value may be calculated according to a compressibility, an update frequency, a last update date/time, a size, and/or a link count of a subset of the data entity stored at each of the plurality of storage locations.
  • a data entity with a high compressibility may be calculated to comprise a low information content value as higher compressibility generally indicates long strings of identical bits (e.g., long series of 0s or 1 s) rather than unique data while data entities comprising frequent and/or recent updates may be calculated to have high information content value.
  • a file comprising a high number of symbolic links and/or incorporated into a high number of other data entities (e.g., a stylesheet) on data storage locations 150(A)-(D) may be calculated to comprise a low information content value due to its amount of duplication and re-use.
  • these factors may be combined to calculate a final information content value according to a weighting and/or scoring algorithm.
  • Delete data entity instructions 134 may delete the data entity stored at each of the plurality of storage locations in order of the assigned priority. For example, a user may execute a delete command on a file, application program, database row or table, a directory, etc. An operating system for device 100 may interpret the delete command as a request to remove the target data entity from wherever it is written and/or stored on data storage locations 150(A)-(D).
  • the request to delete the data entity may comprise a request to perform a secure delete on the data entity.
  • a non-secure delete may, for example, simply remove an entry in a file system providing the location of the data entity to an application and/or operating system.
  • a secure delete may, for example, overwrite the actual memory on the data storage locations 150(A)-(D) for the data entity with other data, such as randomly generated bits, to prevent recovery of the data entity.
  • FIG. 2A is a flowchart of a method 200 for deletion prioritization consistent with disclosed implementations. Although execution of method 200 is described below with reference to the components of deletion prioritization device 100, other suitable components for execution of method 200 may be used.
  • Method 200 may start in stage 205 and proceed to stage 210 where device 100 may receive a request to securely delete a data entity, wherein a plurality of subsets of the data entity are stored at a plurality of storage locations. For example, a user may perform a delete command on a directory comprising a plurality of files. Each file and/or portion of any of the files may comprise subsets of the directory data entity to be securely deleted.
  • receive request instructions 130 may receive a request to delete a data entity stored across a plurality of storage locations.
  • a user, application, service and/or process may request a deletion of a data entity such as a file and/or a group of files.
  • Other data entities may comprise, for example, database entries, memory pages, network streams, application packages, etc. Such data entities may be stored across data storage locations 150(A)-(D) according to a format of the data entity and a type of the storage locations.
  • a large file may be fragmented, with a first part of the file stored at a first memory location on data storage 150(A), a second part of the file stored at a second location on data storage 150(B), a third part of the file stored at a third location on data storage 150(C), and so on.
  • an application may store a data entity in a particular file format that combines content data with template and/or style data stored in different locations, such as a web page file that comprises a link to a stylesheet file.
  • Method 200 may then advance to stage 220 where device 100 may identify an information content value for each of the plurality of subsets of the data entity.
  • the information content value may comprise a rating of the density of information stored in a particular location.
  • the information content value may be calculated as the data entity is originally written to storage locations 150(A)-(D), as the data entity is updated or refreshed, and/or at the time the request for deletion is received.
  • a text document may be stored as subsets comprising primary text content comprising a high information content value, formatting and/or style information comprising a medium information content value, and empty padding comprising a low information content value.
  • a directory data entity may comprise subsets of individual files wherein the information content value of each file is based on the file's size, update frequency, and/or last update date/time.
  • the information content value may be calculated according to a compressibility, an update frequency, a last update date/time, a size, and/or a link count of a subset of the data entity stored at each of the plurality of storage locations.
  • a data entity with a high compressibility may be calculated to comprise a low information content value as higher compressibility generally indicates long strings of identical bits (e.g., long series of 0s or 1 s) rather than unique data while data entities comprising frequent and/or recent updates may be calculated to have high information content value.
  • a file comprising a high number of symbolic links and/or incorporated into a high number of other data entities (e.g., a stylesheet) on data storage locations 150(A)-(D) may be calculated to comprise a low information content value due to its amount of duplication and re-use.
  • these factors may be combined to calculate a final information content value according to a weighting and/or scoring algorithm.
  • Method 200 may then advance to stage 225 where device 100 may assign a priority to each of the plurality of subsets of the data entity according to the respective information content value.
  • assign priority instructions 132 may assign a priority to each of the plurality of storage locations according to an information content value associated with each of the plurality of storage locations. For example, the locations may be assigned their priority with the location having the highest information content value being assigned the highest priority.
  • Method 200 may then advance to stage 225 where device 100 may perform a secure delete of a first subset of the plurality of subsets of the data entity comprising a highest assigned priority.
  • the largest file or least compressible block in a data entity may comprise the highest information content value and so be the first subset of the data entity to be deleted.
  • delete data entity instructions 134 may delete the data entity stored at each of the plurality of storage locations in order of the assigned priority. For example, a user may execute a delete command on a file, application program, database row or table, a directory, etc. An operating system for device 100 may interpret the delete command as a request to remove the target data entity from wherever it is written and/or stored on data storage locations 150(A)-(D).
  • the request to delete the data entity may comprise a request to perform a secure delete on the data entity.
  • a non-secure delete may, for example, simply remove an entry in a file system providing the location of the data entity to an application and/or operating system.
  • a secure delete may, for example, overwrite the actual memory on the data storage locations 150(A)-(D) for the data entity with other data, such as randomly generated bits, to prevent recovery of the data entity.
  • stage 225 may be repeated for each subsequently lower priority subset of data until all subsets of the data entity have been deleted.
  • method 200 may then end at stage 230.
  • FIG. 2B is a flowchart of a method 250 for an example of a method for a deletion operation consistent with disclosed implementations. Although execution of method 200 is described below with reference to the components of deletion prioritization device 100, other suitable components for execution of method 200 may be used. In some implementations, method 250 may be performed by device 100 as part of stage 225 of method 200
  • Method 250 may start in stage 255 and proceed to stage 270 where device 100 may securely delete a first subset of a data entity.
  • a secure delete may, for example, overwrite the addressable memory on the data storage locations 150(A)-(D) for the first subset of the data entity with other data, such as randomly generated bits, to prevent recovery of the first subset of the data entity.
  • stage 225 may be repeated for each subsequently lower priority subset of data until all subsets of the data entity have been deleted.
  • Method 250 may advance to stage 265 where device 100 may determine whether a second secure delete of a second subset of the plurality of subsets of the data entity would impact the secure delete of the first subset of the plurality of subsets of the data entity.
  • first subset may be stored at data storage location 150(A) and second subset may be stored at data storage location 150(B). If locations 150(A) and 150(B) are located on the same physical disk, then performing a secure delete at a second location on the same physical disk may be determined to impact the speed and/or performance of the secure delete at the first location.
  • a performance impact on the first delete may be determined to occur.
  • a second processor is available and/or the second subset is located on a separate physical disk, then no performance impact may be determined to occur.
  • Other performance impacting factors may also be considered, such as network bandwidth availability for network attached storage locations.
  • method 250 may return to stage 260 until the first deletion is completed. Otherwise, method 250 may proceed to stage 270 where device 100 may perform the second secure delete of the second subset of the plurality of subsets of the data entity.
  • the second subset may comprise the next highest assigned priority among the subsets of the data entity to be deleted. Method 250 may then end at stage 275.
  • FIG. 3 is a block diagram of a system 300 for deletion prioritization.
  • System 300 may comprise a computing device 310 comprising an intake engine 315, a content size engine 320, and a deletion engine 325.
  • System 300 may further comprise a plurality of data storage locations 340(A)-(C).
  • Data storage locations 340(A)-(C) may comprise, for example, physical storage devices, such as a hard disk drive and/or a solid state drive, and/or a logical storage device, such as a database, a user-based logical data storage (e.g., a user's home directory and associated files), a network-attached storage, and a logical partition.
  • a logical storage device may comprise data stored on part of and/or across a plurality of physical storage devices.
  • Computing device 310 may comprise, for example, a general and/or special purpose computer, server, mainframe, desktop, laptop, tablet, smart phone, game console, and/or any other system capable of providing computing capability consistent with providing the implementations described herein.
  • Data storage 340(A)-(C) may each comprise a physical storage device, such as a hard disk drive and/or a solid state drive, and/or a logical storage device, such as a database, a user-based logical data storage (e.g., a user's home directory and associated files), a network-attached storage, and a logical partition.
  • a logical storage device may comprise data stored on part of and/or across a plurality of physical storage devices.
  • Each of engines 315, 320, and 325 may comprise any combination of hardware and programming to implement the functionalities of the respective engine.
  • the programming for the engines may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the engines may include a processing resource to execute those instructions.
  • the machine- readable storage medium may store instructions that, when executed by the processing resource, implement engines 315, 320, and 325.
  • system 300 may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine- readable storage medium may be separate but accessible to system 300 and the processing resource.
  • Intake engine 315 may receive a data entity to be stored, allocate a subset of the data entity to each of a plurality of storage locations, and write each subset of the data entity to the respective each of the plurality of storage locations. Intake engine 315 may further receive an update to the data entity and write each updated subset of the data entity to the respective each of the plurality of storage locations. For example, intake engine 315 may receive a document file comprising a content subset, a template subset, and a metadata subset. The content subset may be allocated and written to data storage 340(A), the template subset may be allocated and written to data storage 340(B), and the metadata subset may be written to data storage 340(C). In some implementations, subsets of the data entity need not comprise specific sections such as content and metadata, but may simply comprise chunks of bytes written in separate locations that may be reassembled into the data entity.
  • Content value engine 320 calculate an information content value for each of the subsets of the data entity according to a compressibility of each of the subsets of the data entity and update the information content value for each of the subsets of the data entity according to the update of the data entity.
  • the information content value may be calculated according to a compressibility, an update frequency, a last update date/time, a size, and/or a link count of a subset of the data entity stored at each of the plurality of storage locations.
  • a data entity with a high compressibility may be calculated to comprise a low information content value as higher compressibility generally indicates long strings of identical bits (e.g., long series of 0s or 1 s) rather than unique data while data entities comprising frequent and/or recent updates may be calculated to have high information content value.
  • a file comprising a high number of symbolic links and/or incorporated into a high number of other data entities (e.g., a stylesheet) on data storage locations 340(A)-(C) may be calculated to comprise a low information content value due to its amount of duplication and re-use.
  • these factors may be combined to calculate a final information content value according to a weighting and/or scoring algorithm.
  • assign priority instructions 132 may assign a priority to each of the plurality of storage locations according to an information content value associated with each of the plurality of storage locations. For example, the locations may be assigned their priority with the location having the highest information content value being assigned the highest priority.
  • the information content value may comprise a rating of the density of information stored in a particular location.
  • the information content value may be calculated as the data entity is originally written to storage locations 340(A)-(C), as the data entity is updated or refreshed, and/or at the time the request for deletion is received.
  • Deletion engine 325 may receive a request to securely delete the data entity from the plurality of storage locations, assign a priority to each of the subsets of the data entity according to the updated information content value for each of the subsets of the data entity, and securely delete each of the subsets of the data entity in order of the assigned priority for each of the subsets of the data entity.
  • receive request instructions 130 may receive a request to delete a data entity stored across a plurality of storage locations.
  • a user, application, service and/or process may request a deletion of a data entity such as a file and/or a group of files.
  • Other data entities may comprise, for example, database entries, memory pages, network streams, application packages, etc.
  • Such data entities may be stored across data storage locations 340(A)-(C) according to a format of the data entity and a type of the storage locations.
  • a large file may be fragmented, with a first part of the file stored at a first memory location on data storage 340(A), a second part of the file stored at a second location on data storage 340(B), a third part of the file stored at a third location on data storage 340(C), and so on.
  • an application may store a data entity in a particular file format that combines content data with template and/or style data stored in different locations, such as a web page file that comprises a link to a stylesheet file.
  • Delete data entity instructions 134 may delete the data entity stored at each of the plurality of storage locations in order of the assigned priority. For example, a user may execute a delete command on a file, application program, database row or table, a directory, etc. An operating system for device 100 may interpret the delete command as a request to remove the target data entity from wherever it is written and/or stored on data storage locations 150(A)-(D).
  • the request to delete the data entity may comprise a request to perform a secure delete on the data entity.
  • a non-secure delete may, for example, simply remove an entry in a file system providing the location of the data entity to an application and/or operating system.
  • a secure delete may, for example, overwrite the actual memory on the data storage locations 150(A)-(D) for the data entity with other data, such as randomly generated bits, to prevent recovery of the data entity.
  • the disclosed examples may include systems, devices, computer- readable storage media, and methods for deletion prioritization. For purposes of explanation, certain examples are described with reference to the components illustrated in the Figures. The functionality of the illustrated components may overlap, however, and may be present in a fewer or greater number of elements and components. Further, all or part of the functionality of illustrated elements may coexist or be distributed among several geographically dispersed locations. Moreover, the disclosed examples may be implemented in various environments and are not limited to the illustrated examples.

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Human Computer Interaction (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

Des exemples de la présente invention concernent des instructions de priorisation de suppression comprenant la réception d'une demande de suppression d'une entité de données mémorisée sur une pluralité d'emplacements de mémorisation, l'attribution d'une priorité à chacun des emplacements de mémorisation de la pluralité d'emplacements de mémorisation en fonction d'une valeur de contenu d'informations associée à chacun des emplacements de mémorisation de la pluralité d'emplacements de mémorisation et la suppression de l'entité de données mémorisée au niveau de chacun des emplacements de mémorisation de la pluralité d'emplacements de mémorisation dans l'ordre de la priorité attribuée.
PCT/US2015/030937 2015-05-15 2015-05-15 Priorisation de suppression WO2016186602A1 (fr)

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PCT/US2015/030937 WO2016186602A1 (fr) 2015-05-15 2015-05-15 Priorisation de suppression

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PCT/US2015/030937 WO2016186602A1 (fr) 2015-05-15 2015-05-15 Priorisation de suppression

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11567697B2 (en) 2020-11-12 2023-01-31 International Business Machines Corporation Prioritization of stored data during a delete process in a virtualized storage system
US11853576B2 (en) 2021-09-09 2023-12-26 Hewlett Packard Enterprise Development Lp Deleting data entities and deduplication stores in deduplication systems

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WO2002046887A2 (fr) * 2000-10-23 2002-06-13 Xyron Corporation Processeur a fonctionnement multitache
US20080059692A1 (en) * 2006-09-04 2008-03-06 Sandisk Il Ltd. Device for prioritized erasure of flash memory
US20110113432A1 (en) * 2007-10-31 2011-05-12 Microsoft Corporation Compressed storage management
US20120216006A1 (en) * 2007-12-25 2012-08-23 Canon Kabushiki Kaisha Information processing apparatus and information processing method that selects data to be deleted without a user having to perform a delete operation
US20130018852A1 (en) * 2011-07-15 2013-01-17 International Business Machines Corporation Deleted data recovery in data storage systems

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2002046887A2 (fr) * 2000-10-23 2002-06-13 Xyron Corporation Processeur a fonctionnement multitache
US20080059692A1 (en) * 2006-09-04 2008-03-06 Sandisk Il Ltd. Device for prioritized erasure of flash memory
US20110113432A1 (en) * 2007-10-31 2011-05-12 Microsoft Corporation Compressed storage management
US20120216006A1 (en) * 2007-12-25 2012-08-23 Canon Kabushiki Kaisha Information processing apparatus and information processing method that selects data to be deleted without a user having to perform a delete operation
US20130018852A1 (en) * 2011-07-15 2013-01-17 International Business Machines Corporation Deleted data recovery in data storage systems

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11567697B2 (en) 2020-11-12 2023-01-31 International Business Machines Corporation Prioritization of stored data during a delete process in a virtualized storage system
US11853576B2 (en) 2021-09-09 2023-12-26 Hewlett Packard Enterprise Development Lp Deleting data entities and deduplication stores in deduplication systems

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