US20110184997A1 - Selecting storage facilities in a plurality of dispersed storage networks - Google Patents

Abstract

A method begins by a processing module receiving a data storage request and accessing a file directory to identify dispersed storage network (DSN) systems. The method continues with the processing module identifying dispersed storage (DS) units to produce identified DS units for each of the DSN systems and selecting one of the DSN systems to produce a selected DSN system. The method continues with the processing module selecting a set of DS units from the identified DS units of the selected DSN system to produce selected DS units, wherein the selected DS units store a set of encoded data slices. The method continues with the processing module updating the file directory to include an identity of the selected DS units and an identity of the set of encoded data slices.

Description

CROSS REFERENCE TO RELATED PATENTS
• The present U.S. Utility patent application claims priority pursuant to 35 U.S.C. §119(e) to U.S. Provisional Application Ser. No. 61/299,075, entitled “DISTRIBUTED STORAGE RESOURCE DETERMINATION METHOD,” (Attorney Docket No. CS175), filed Jan. 28, 2010, pending, which is hereby incorporated herein by reference in its entirety and made part of the present U.S. Utility patent application for all purposes.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
• Not Applicable
INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC
• Not Applicable
BACKGROUND OF THE INVENTION
• 1. Technical Field of the Invention
• This invention relates generally to computing systems and more particularly to data storage solutions within such computing systems.
• 2. Description of Related Art
• Computers are known to communicate, process, and store data. Such computers range from wireless smart phones to data centers that support millions of web searches, stock trades, or on-line purchases every day. In general, a computing system generates data and/or manipulates data from one form into another. For instance, an image sensor of the computing system generates raw picture data and, using an image compression program (e.g., JPEG, MPEG, etc.), the computing system manipulates the raw picture data into a standardized compressed image.
• With continued advances in processing speed and communication speed, computers are capable of processing real time multimedia data for applications ranging from simple voice communications to streaming high definition video. As such, general-purpose information appliances are replacing purpose-built communications devices (e.g., a telephone). For example, smart phones can support telephony communications but they are also capable of text messaging and accessing the Internet to perform functions including email, web browsing, remote applications access, and media communications (e.g., telephony voice, image transfer, music files, video files, real time video streaming. etc.).
• Each type of computer is constructed and operates in accordance with one or more communication, processing, and storage standards. As a result of standardization and with advances in technology, more and more information content is being converted into digital formats. For example, more digital cameras are now being sold than film cameras, thus producing more digital pictures. As another example, web-based programming is becoming an alternative to over the air television broadcasts and/or cable broadcasts. As further examples, papers, books, video entertainment, home video, etc. are now being stored digitally, which increases the demand on the storage function of computers.
• A typical computer storage system includes one or more memory devices aligned with the needs of the various operational aspects of the computer's processing and communication functions. Generally, the immediacy of access dictates what type of memory device is used. For example, random access memory (RAM) memory can be accessed in any random order with a constant response time, thus it is typically used for cache memory and main memory. By contrast, memory device technologies that require physical movement such as magnetic disks, tapes, and optical discs, have a variable response time as the physical movement can take longer than the data transfer, thus they are typically used for secondary memory (e.g., hard drive, backup memory, etc.).
• A computer's storage system will be compliant with one or more computer storage standards that include, but are not limited to, network file system (NFS), flash file system (FFS), disk file system (DFS), small computer system interface (SCSI), internet small computer system interface (iSCSI), file transfer protocol (FTP), and web-based distributed authoring and versioning (WebDAV). These standards specify the data storage format (e.g., files, data objects, data blocks, directories, etc.) and interfacing between the computer's processing function and its storage system, which is a primary function of the computer's memory controller.
• Despite the standardization of the computer and its storage system, memory devices fail; especially commercial grade memory devices that utilize technologies incorporating physical movement (e.g., a disc drive). For example, it is fairly common for a disc drive to routinely suffer from bit level corruption and to completely fail after three years of use. One solution is to a higher-grade disc drive, which adds significant cost to a computer.
• Another solution is to utilize multiple levels of redundant disc drives to replicate the data into two or more copies. One such redundant drive approach is called redundant array of independent discs (RAID). In a RAID device, a RAID controller adds parity data to the original data before storing it across the array. The parity data is calculated from the original data such that the failure of a disc will not result in the loss of the original data. For example,RAID 5 uses three discs to protect data from the failure of a single disc. The parity data, and associated redundancy overhead data, reduces the storage capacity of three independent discs by one third (e.g., n−1=capacity).RAID 6 can recover from a loss of two discs and requires a minimum of four discs with a storage capacity of n−2.
• While RAID addresses the memory device failure issue, it is not without its own failures issues that affect its effectiveness, efficiency and security. For instance, as more discs are added to the array, the probability of a disc failure increases, which increases the demand for maintenance. For example, when a disc fails, it needs to be manually replaced before another disc fails and the data stored in the RAID device is lost. To reduce the risk of data loss, data on a RAID device is typically copied on to one or more other RAID devices. While this addresses the loss of data issue, it raises a security issue since multiple copies of data are available, which increases the chances of unauthorized access. Further, as the amount of data being stored grows, the overhead of RAID devices becomes a non-trivial efficiency issue.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)
• FIG. 1 is a schematic block diagram of an embodiment of a computing system in accordance with the invention;
• FIG. 2 is a schematic block diagram of an embodiment of a computing core in accordance with the invention;
• FIG. 3 is a schematic block diagram of an embodiment of a distributed storage processing unit in accordance with the invention;
• FIG. 4 is a schematic block diagram of an embodiment of a grid module in accordance with the invention;
• FIG. 5 is a diagram of an example embodiment of error coded data slice creation in accordance with the invention;
• FIG. 6 is a flowchart illustrating an example of storing data in accordance with the invention;
• FIG. 7 is a flowchart illustrating another example of storing data in accordance with the invention;
• FIG. 8 is a flowchart illustrating another example of storing data in accordance with the invention;
• FIG. 9 is a flowchart illustrating an example of retrieving data in accordance with the invention;
• FIG. 10 is a flowchart illustrating another example of storing data in accordance with the invention;
• FIG. 11 is a flowchart illustrating another example of storing data in accordance with the invention;
• FIG. 12 is a table illustrating an example of a weighted scoring table in accordance with the invention;
• FIG. 13 is a flowchart illustrating another example of storing data in accordance with the invention;
• FIG. 14 is a structure diagram illustrating an example of a dispersed storage network (DSN) directory structure in accordance with the invention; and
• FIG. 15 is a flowchart illustrating another example of storing data in accordance with the invention.
DETAILED DESCRIPTION OF THE INVENTION
• FIG. 1 is a schematic block diagram of acomputing system10 that includes one or more of a first type ofuser devices12, one or more of a second type ofuser devices14, at least one distributed storage (DS)processing unit16, at least oneDS managing unit18, at least one storageintegrity processing unit20, and a distributed storage network (DSN)memory22 coupled via anetwork24. Thenetwork24 may include one or more wireless and/or wire lined communication systems; one or more private intranet systems and/or public internet systems; and/or one or more local area networks (LAN) and/or wide area networks (WAN).
• TheDSN memory22 includes a plurality of distributed storage (DS)units36 for storing data of the system. Each of theDS units36 includes a processing module and memory and may be located at a geographically different site than the other DS units (e.g., one in Chicago, one in Milwaukee, etc.). The processing module may be a single processing device or a plurality of processing devices. Such a processing device may be a microprocessor, micro-controller, digital signal processor, microcomputer, central processing unit, field programmable gate array, programmable logic device, state machine, logic circuitry, analog circuitry, digital circuitry, and/or any device that manipulates signals (analog and/or digital) based on hard coding of the circuitry and/or operational instructions. The processing module may have an associated memory and/or memory element, which may be a single memory device, a plurality of memory devices, and/or embedded circuitry of the processing module. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. Note that if the processing module includes more than one processing device, the processing devices may be centrally located (e.g., directly coupled together via a wired and/or wireless bus structure) or may be distributedly located (e.g., cloud computing via indirect coupling via a local area network and/or a wide area network). Further note that when the processing module implements one or more of its functions via a state machine, analog circuitry, digital circuitry, and/or logic circuitry, the memory and/or memory element storing the corresponding operational instructions may be embedded within, or external to, the circuitry comprising the state machine, analog circuitry, digital circuitry, and/or logic circuitry. Still further note that, the memory element stores, and the processing module executes, hard coded and/or operational instructions corresponding to at least some of the steps and/or functions illustrated inFIGS. 1-15.
• Each of the user devices12-14, theDS processing unit16, theDS managing unit18, and the storageintegrity processing unit20 may be a portable computing device (e.g., a social networking device, a gaming device, a cell phone, a smart phone, a personal digital assistant, a digital music player, a digital video player, a laptop computer, a handheld computer, a video game controller, and/or any other portable device that includes a computing core) and/or a fixed computing device (e.g., a personal computer, a computer server, a cable set-top box, a satellite receiver, a television set, a printer, a fax machine, home entertainment equipment, a video game console, and/or any type of home or office computing equipment). Such a portable or fixed computing device includes acomputing core26 and one ormore interfaces30,32, and/or33. An embodiment of thecomputing core26 will be described with reference toFIG. 2.
• With respect to the interfaces, each of theinterfaces30,32, and33 includes software and/or hardware to support one or more communication links via thenetwork24 and/or directly. For example, interfaces30 support a communication link (wired, wireless, direct, via a LAN, via thenetwork24, etc.) between the first type ofuser device14 and theDS processing unit16. As another example,DSN interface32 supports a plurality of communication links via thenetwork24 between theDSN memory22 and theDS processing unit16, the first type ofuser device12, and/or the storageintegrity processing unit20. As yet another example,interface33 supports a communication link between theDS managing unit18 and any one of the other devices and/orunits12,14,16,20, and/or22 via thenetwork24.
• In general and with respect to data storage, thesystem10 supports three primary functions: distributed network data storage management, distributed data storage and retrieval, and data storage integrity verification. In accordance with these three primary functions, data can be distributedly stored in a plurality of physically different locations and subsequently retrieved in a reliable and secure manner regardless of failures of individual storage devices, failures of network equipment, the duration of storage, the amount of data being stored, attempts at hacking the data, etc.
• TheDS managing unit18 performs distributed network data storage management functions, which include establishing distributed data storage parameters, performing network operations, performing network administration, and/or performing network maintenance. TheDS managing unit18 establishes the distributed data storage parameters (e.g., allocation of virtual DSN memory space, distributed storage parameters, security parameters, billing information, user profile information, etc.) for one or more of the user devices12-14 (e.g., established for individual devices, established for a user group of devices, established for public access by the user devices, etc.). For example, theDS managing unit18 coordinates the creation of a vault (e.g., a virtual memory block) within theDSN memory22 for a user device (for a group of devices, or for public access). TheDS managing unit18 also determines the distributed data storage parameters for the vault. In particular, theDS managing unit18 determines a number of slices (e.g., the number that a data segment of a data file and/or data block is partitioned into for distributed storage) and a read threshold value (e.g., the minimum number of slices required to reconstruct the data segment).
• As another example, theDS managing module18 creates and stores, locally or within theDSN memory22, user profile information. The user profile information includes one or more of authentication information, permissions, and/or the security parameters. The security parameters may include one or more of encryption/decryption scheme, one or more encryption keys, key generation scheme, and data encoding/decoding scheme.
• As yet another example, theDS managing unit18 creates billing information for a particular user, user group, vault access, public vault access, etc. For instance, theDS managing unit18 tracks the number of times user accesses a private vault and/or public vaults, which can be used to generate a per-access bill. In another instance, theDS managing unit18 tracks the amount of data stored and/or retrieved by a user device and/or a user group, which can be used to generate a per-data-amount bill.
• TheDS managing unit18 also performs network operations, network administration, and/or network maintenance. As at least part of performing the network operations and/or administration, theDS managing unit18 monitors performance of the devices and/or units of thesystem10 for potential failures, determines the devices and/or unit's activation status, determines the devices' and/or units' loading, and any other system level operation that affects the performance level of thesystem10. For example, theDS managing unit18 receives and aggregates network management alarms, alerts, errors, status information, performance information, and messages from the devices12-14 and/or theunits16,20,22. For example, theDS managing unit18 receives a simple network management protocol (SNMP) message regarding the status of theDS processing unit16.
• TheDS managing unit18 performs the network maintenance by identifying equipment within thesystem10 that needs replacing, upgrading, repairing, and/or expanding. For example, theDS managing unit18 determines that theDSN memory22 needsmore DS units36 or that one or more of theDS units36 needs updating.
• The second primary function (i.e., distributed data storage and retrieval) begins and ends with a user device12-14. For instance, if a second type ofuser device14 has adata file38 and/or data block40 to store in theDSN memory22, it send the data file38 and/or data block40 to theDS processing unit16 via itsinterface30. As will be described in greater detail with reference toFIG. 2, theinterface30 functions to mimic a conventional operating system (OS) file system interface (e.g., network file system (NFS), flash file system (FFS), disk file system (DFS), file transfer protocol (FTP), web-based distributed authoring and versioning (WebDAV), etc.) and/or a block memory interface (e.g., small computer system interface (SCSI), internet small computer system interface (iSCSI), etc.). In addition, theinterface30 may attach a user identification code (ID) to the data file38 and/ordata block40.
• TheDS processing unit16 receives the data file38 and/or data block40 via itsinterface30 and performs a distributed storage (DS)process34 thereon (e.g., an error coding dispersal storage function). TheDS processing34 begins by partitioning the data file38 and/or data block40 into one or more data segments, which is represented as Y data segments. For example, theDS processing34 may partition the data file38 and/or data block40 into a fixed byte size segment (e.g., 2¹to 2ⁿbytes, where n=>2) or a variable byte size (e.g., change byte size from segment to segment, or from groups of segments to groups of segments, etc.).
• For each of the Y data segments, theDS processing34 error encodes (e.g., forward error correction (FEC), information dispersal algorithm, or error correction coding) and slices (or slices then error encodes) the data segment into a plurality of error coded (EC) data slices42-48, which is represented as X slices per data segment. The number of slices (X) per segment, which corresponds to a number of pillars n, is set in accordance with the distributed data storage parameters and the error coding scheme. For example, if a Reed-Solomon (or other FEC scheme) is used in an n/k system, then a data segment is divided into n slices, where k number of slices is needed to reconstruct the original data (i.e., k is the threshold). As a few specific examples, the n/k factor may be 5/3; 6/4; 8/6; 8/5; 16/10.
• For each slice42-48, theDS processing unit16 creates a unique slice name and appends it to the corresponding slice42-48. The slice name includes universal DSN memory addressing routing information (e.g., virtual memory addresses in the DSN memory22) and user-specific information (e.g., user ID, file name, data block identifier, etc.).
• TheDS processing unit16 transmits the plurality of EC slices42-48 to a plurality ofDS units36 of theDSN memory22 via theDSN interface32 and thenetwork24. TheDSN interface32 formats each of the slices for transmission via thenetwork24. For example, theDSN interface32 may utilize an internet protocol (e.g., TCP/IP, etc.) to packetize the slices42-48 for transmission via thenetwork24.
• The number ofDS units36 receiving the slices42-48 is dependent on the distributed data storage parameters established by theDS managing unit18. For example, theDS managing unit18 may indicate that each slice is to be stored in adifferent DS unit36. As another example, theDS managing unit18 may indicate that like slice numbers of different data segments are to be stored in thesame DS unit36. For example, the first slice of each of the data segments is to be stored in afirst DS unit36, the second slice of each of the data segments is to be stored in asecond DS unit36, etc. In this manner, the data is encoded and distributedly stored at physically diverse locations to improved data storage integrity and security. Further examples of encoding the data segments will be provided with reference to one or more ofFIGS. 2-15.
• EachDS unit36 that receives a slice42-48 for storage translates the virtual DSN memory address of the slice into a local physical address for storage. Accordingly, eachDS unit36 maintains a virtual to physical memory mapping to assist in the storage and retrieval of data.
• The first type ofuser device12 performs a similar function to store data in theDSN memory22 with the exception that it includes the DS processing. As such, thedevice12 encodes and slices the data file and/or data block it has to store. The device then transmits theslices11 to the DSN memory via itsDSN interface32 and thenetwork24.
• For a second type ofuser device14 to retrieve a data file or data block from memory, it issues a read command via itsinterface30 to theDS processing unit16. TheDS processing unit16 performs theDS processing34 to identify theDS units36 storing the slices of the data file and/or data block based on the read command. TheDS processing unit16 may also communicate with theDS managing unit18 to verify that theuser device14 is authorized to access the requested data.
• Assuming that the user device is authorized to access the requested data, theDS processing unit16 issues slice read commands to at least a threshold number of theDS units36 storing the requested data (e.g., to at least 10 DS units for a 16/10 error coding scheme). Each of theDS units36 receiving the slice read command, verifies the command, accesses its virtual to physical memory mapping, retrieves the requested slice, or slices, and transmits it to theDS processing unit16.
• Once theDS processing unit16 has received a read threshold number of slices for a data segment, it performs an error decoding function and de-slicing to reconstruct the data segment. When Y number of data segments has been reconstructed, theDS processing unit16 provides the data file38 and/or data block40 to theuser device14. Note that the first type ofuser device12 performs a similar process to retrieve a data file and/or data block.
• The storageintegrity processing unit20 performs the third primary function of data storage integrity verification. In general, the storageintegrity processing unit20 periodically retrievesslices45, and/or slice names, of a data file or data block of a user device to verify that one or more slices have not been corrupted or lost (e.g., the DS unit failed). The retrieval process mimics the read process previously described.
• If the storageintegrity processing unit20 determines that one or more slices is corrupted or lost, it rebuilds the corrupted or lost slice(s) in accordance with the error coding scheme. The storageintegrity processing unit20 stores the rebuild slice, or slices, in the appropriate DS unit(s)36 in a manner that mimics the write process previously described.
• FIG. 2 is a schematic block diagram of an embodiment of acomputing core26 that includes aprocessing module50, amemory controller52,main memory54, a videographics processing unit55, an input/output (IO)controller56, a peripheral component interconnect (PCI)interface58, at least one IO device interface module62, a read only memory (ROM) basic input output system (BIOS)64, and one or more memory interface modules. The memory interface module(s) includes one or more of a universal serial bus (USB) interface module66, a host bus adapter (HBA)interface module68, anetwork interface module70, aflash interface module72, a harddrive interface module74, and aDSN interface module76. Note theDSN interface module76 and/or thenetwork interface module70 may function as theinterface30 of theuser device14 ofFIG. 1. Further note that the IO device interface module62 and/or the memory interface modules may be collectively or individually referred to as IO ports.
• Theprocessing module50 may be a single processing device or a plurality of processing devices. Such a processing device may be a microprocessor, micro-controller, digital signal processor, microcomputer, central processing unit, field programmable gate array, programmable logic device, state machine, logic circuitry, analog circuitry, digital circuitry, and/or any device that manipulates signals (analog and/or digital) based on hard coding of the circuitry and/or operational instructions. Theprocessing module50 may have an associated memory and/or memory element, which may be a single memory device, a plurality of memory devices, and/or embedded circuitry of theprocessing module50. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. Note that if theprocessing module50 includes more than one processing device, the processing devices may be centrally located (e.g., directly coupled together via a wired and/or wireless bus structure) or may be distributedly located (e.g., cloud computing via indirect coupling via a local area network and/or a wide area network). Further note that when theprocessing module50 implements one or more of its functions via a state machine, analog circuitry, digital circuitry, and/or logic circuitry, the memory and/or memory element storing the corresponding operational instructions may be embedded within, or external to, the circuitry comprising the state machine, analog circuitry, digital circuitry, and/or logic circuitry. Still further note that, the memory element stores, and theprocessing module50 executes, hard coded and/or operational instructions corresponding to at least some of the steps and/or functions illustrated inFIGS. 1-15.
• FIG. 3 is a schematic block diagram of an embodiment of a dispersed storage (DS)processing module34 ofuser device12 and/or of theDS processing unit16. TheDS processing module34 includes agateway module78, anaccess module80, agrid module82, and astorage module84. TheDS processing module34 may also include aninterface30 and theDSnet interface32 or theinterfaces68 and/or70 may be part ofuser12 or of theDS processing unit14. TheDS processing module34 may further include a bypass/feedback path between thestorage module84 to thegateway module78. Note that the modules78-84 of theDS processing module34 may be in a single unit or distributed across multiple units.
• In an example of storing data, thegateway module78 receives an incoming data object that includes a user ID field86, anobject name field88, and thedata field40 and may also receive corresponding information that includes a process identifier (e.g., an internal process/application ID), metadata, a file system directory, a block number, a transaction message, a user device identity (ID), a data object identifier, a source name, and/or user information. Thegateway module78 authenticates the user associated with the data object by verifying the user ID86 with the managingunit18 and/or another authenticating unit.
• When the user is authenticated, thegateway module78 obtains user information from themanagement unit18, the user device, and/or the other authenticating unit. The user information includes a vault identifier, operational parameters, and user attributes (e.g., user data, billing information, etc.). A vault identifier identifies a vault, which is a virtual memory space that maps to a set ofDS storage units36. For example, vault1 (i.e.,user1's DSN memory space) includes eight DS storage units (X=8 wide) and vault2 (i.e.,user2's DSN memory space) includes sixteen DS storage units (X=16 wide). The operational parameters may include an error coding algorithm, the width n (number of pillars X or slices per segment for this vault), a read threshold T, a write threshold, an encryption algorithm, a slicing parameter, a compression algorithm, an integrity check method, caching settings, parallelism settings, and/or other parameters that may be used to access the DSN memory layer.
• Thegateway module78 uses the user information to assign asource name35 to the data. For instance, thegateway module60 determines thesource name35 of the data object40 based on the vault identifier and the data object. For example, the source name may contain a file identifier (ID), a vault generation number, a reserved field, and a vault identifier (ID). As another example, thegateway module78 may generate the file ID based on a hash function of the data object40. Note that thegateway module78 may also perform message conversion, protocol conversion, electrical conversion, optical conversion, access control, user identification, user information retrieval, traffic monitoring, statistics generation, configuration, management, and/or source name determination.
• Theaccess module80 receives the data object40 and creates a series ofdata segments1 through Y90-92 in accordance with a data storage protocol (e.g., file storage system, a block storage system, and/or an aggregated block storage system). The number of segments Y may be chosen or randomly assigned based on a selected segment size and the size of the data object. For example, if the number of segments is chosen to be a fixed number, then the size of the segments varies as a function of the size of the data object. For instance, if the data object is an image file of 4,194,304 eight bit bytes (e.g., 33,554,432 bits) and the number of segments Y=131,072, then each segment is 256 bits or 32 bytes. As another example, if segment sized is fixed, then the number of segments Y varies based on the size of data object. For instance, if the data object is an image file of 4,194,304 bytes and the fixed size of each segment is 4,096 bytes, the then number of segments Y=1,024. Note that each segment is associated with the same source name.
• Thegrid module82 receives the data segments and may manipulate (e.g., compression, encryption, cyclic redundancy check (CRC), etc.) each of the data segments before performing an error coding function of the error coding dispersal storage function to produce a pre-manipulated data segment. After manipulating a data segment, if applicable, thegrid module82 error encodes (e.g., Reed-Solomon, Convolution encoding, Trellis encoding, etc.) the data segment or manipulated data segment into X error coded data slices42-44.
• The value X, or the number of pillars (e.g., X=16), is chosen as a parameter of the error coding dispersal storage function. Other parameters of the error coding dispersal function include a read threshold T, a write threshold W, etc. The read threshold (e.g., T=10, when X=16) corresponds to the minimum number of error-free error coded data slices required to reconstruct the data segment. In other words, theDS processing module34 can compensate for X−T (e.g., 16−10=6) missing error coded data slices per data segment. The write threshold W corresponds to a minimum number of DS storage units that acknowledge proper storage of their respective data slices before the DS processing module indicates proper storage of the encoded data segment. Note that the write threshold is greater than or equal to the read threshold for a given number of pillars (X).
• For each data slice of a data segment, thegrid module82 generates aunique slice name37 and attaches it thereto. Theslice name37 includes a universal routing information field and a vault specific field and may be 48 bytes (e.g., 24 bytes for each of the universal routing information field and the vault specific field). As illustrated, the universal routing information field includes a slice index, a vault ID, a vault generation, and a reserved field. The slice index is based on the pillar number and the vault ID and, as such, is unique for each pillar (e.g., slices of the same pillar for the same vault for any segment will share the same slice index). The vault specific field includes a data name, which includes a file ID and a segment number (e.g., a sequential numbering of data segments1-Y of a simple data object or a data block number).
• Prior to outputting the error coded data slices of a data segment, the grid module may perform post-slice manipulation on the slices. If enabled, the manipulation includes slice level compression, encryption, CRC, addressing, tagging, and/or other manipulation to improve the effectiveness of the computing system.
• When the error coded data slices of a data segment are ready to be outputted, thegrid module82 determines which of theDS storage units36 will store the EC data slices based on a dispersed storage memory mapping associated with the user's vault and/or DS storage unit attributes. The DS storage unit attributes may include availability, self-selection, performance history, link speed, link latency, ownership, available DSN memory, domain, cost, a prioritization scheme, a centralized selection message from another source, a lookup table, data ownership, and/or any other factor to optimize the operation of the computing system. Note that the number ofDS storage units36 is equal to or greater than the number of pillars (e.g., X) so that no more than one error coded data slice of the same data segment is stored on the sameDS storage unit36. Further note that EC data slices of the same pillar number but of different segments (e.g., EC data slice1 ofdata segment1 and EC data slice1 of data segment2) may be stored on the same or differentDS storage units36.
• Thestorage module84 performs an integrity check on the outbound encoded data slices and, when successful, identifies a plurality of DS storage units based on information provided by thegrid module82. Thestorage module84 then outputs the encoded data slices1 through X of eachsegment1 through Y to theDS storage units36. Each of theDS storage units36 stores its EC data slice(s) and maintains a local virtual DSN address to physical location table to convert the virtual DSN address of the EC data slice(s) into physical storage addresses.
• In an example of a read operation, theuser device12 and/or14 sends a read request to theDS processing unit14, which authenticates the request. When the request is authentic, theDS processing unit14 sends a read message to each of theDS storage units36 storing slices of the data object being read. The slices are received via theDSnet interface32 and processed by thestorage module84, which performs a parity check and provides the slices to thegrid module82 when the parity check was successful. Thegrid module82 decodes the slices in accordance with the error coding dispersal storage function to reconstruct the data segment. Theaccess module80 reconstructs the data object from the data segments and thegateway module78 formats the data object for transmission to the user device.
• FIG. 4 is a schematic block diagram of an embodiment of agrid module82 that includes acontrol unit73, apre-slice manipulator75, anencoder77, aslicer79, apost-slice manipulator81, a pre-slice de-manipulator83, adecoder85, a de-slicer87, and/or a post-slice de-manipulator89. Note that thecontrol unit73 may be partially or completely external to thegrid module82. For example, thecontrol unit73 may be part of the computing core at a remote location, part of a user device, part of theDS managing unit18, or distributed amongst one or more DS storage units.
• In an example of write operation, thepre-slice manipulator75 receives a data segment90-92 and a write instruction from an authorized user device. Thepre-slice manipulator75 determines if pre-manipulation of the data segment90-92 is required and, if so, what type. Thepre-slice manipulator75 may make the determination independently or based on instructions from thecontrol unit73, where the determination is based on a computing system-wide predetermination, a table lookup, vault parameters associated with the user identification, the type of data, security requirements, available DSN memory, performance requirements, and/or other metadata.
• Once a positive determination is made, thepre-slice manipulator75 manipulates the data segment90-92 in accordance with the type of manipulation. For example, the type of manipulation may be compression (e.g., Lempel-Ziv-Welch, Huffman, Golomb, fractal, wavelet, etc.), signatures (e.g., Digital Signature Algorithm (DSA), Elliptic Curve DSA, Secure Hash Algorithm, etc.), watermarking, tagging, encryption (e.g., Data Encryption Standard, Advanced Encryption Standard, etc.), adding metadata (e.g., time/date stamping, user information, file type, etc.), cyclic redundancy check (e.g., CRC32), and/or other data manipulations to produce the pre-manipulated data segment.
• Theencoder77 encodes thepre-manipulated data segment92 using a forward error correction (FEC) encoder (and/or other type of erasure coding and/or error coding) to produce an encodeddata segment94. Theencoder77 determines which forward error correction algorithm to use based on a predetermination associated with the user's vault, a time based algorithm, user direction, DS managing unit direction, control unit direction, as a function of the data type, as a function of thedata segment92 metadata, and/or any other factor to determine algorithm type. The forward error correction algorithm may be Golay, Multidimensional parity, Reed-Solomon, Hamming, Bose Ray Chauduri Hocquenghem (BCH), Cauchy-Reed-Solomon, or any other FEC encoder. Note that theencoder77 may use a different encoding algorithm for eachdata segment92, the same encoding algorithm for thedata segments92 of a data object, or a combination thereof.
• The encodeddata segment94 is of greater size than thedata segment92 by the overhead rate of the encoding algorithm by a factor of X/T, where X is the width or number of slices, and T is the read threshold. In this regard, the corresponding decoding process can accommodate at most X-T missing EC data slices and still recreate thedata segment92. For example, if X=16 and T=10, then thedata segment92 will be recoverable as long as 10 or more EC data slices per segment are not corrupted.
• Theslicer79 transforms the encodeddata segment94 into EC data slices in accordance with the slicing parameter from the vault for this user and/ordata segment92. For example, if the slicing parameter is X=16, then theslicer79 slices each encodeddata segment94 into 16 encoded slices.
• Thepost-slice manipulator81 performs, if enabled, post-manipulation on the encoded slices to produce the EC data slices. If enabled, thepost-slice manipulator81 determines the type of post-manipulation, which may be based on a computing system-wide predetermination, parameters in the vault for this user, a table lookup, the user identification, the type of data, security requirements, available DSN memory, performance requirements, control unit directed, and/or other metadata. Note that the type of post-slice manipulation may include slice level compression, signatures, encryption, CRC, addressing, watermarking, tagging, adding metadata, and/or other manipulation to improve the effectiveness of the computing system.
• In an example of a read operation, the post-slice de-manipulator89 receives at least a read threshold number of EC data slices and performs the inverse function of thepost-slice manipulator81 to produce a plurality of encoded slices. The de-slicer87 de-slices the encoded slices to produce an encodeddata segment94. Thedecoder85 performs the inverse function of theencoder77 to recapture the data segment90-92. The pre-slice de-manipulator83 performs the inverse function of thepre-slice manipulator75 to recapture the data segment90-92.
• FIG. 5 is a diagram of an example of slicing an encodeddata segment94 by theslicer79. In this example, the encodeddata segment94 includes thirty-two bits, but may include more or less bits. Theslicer79 disperses the bits of the encodeddata segment94 across the EC data slices in a pattern as shown. As such, each EC data slice does not include consecutive bits of thedata segment94 reducing the impact of consecutive bit failures on data recovery. For example, if EC data slice2 (which includesbits1,5,9,13,17,25, and29) is unavailable (e.g., lost, inaccessible, or corrupted), the data segment can be reconstructed from the other EC data slices (e.g., 1, 3 and 4 for a read threshold of 3 and a width of 4).
• FIG. 6 is a flowchart illustrating an example of storing data. The method begins withstep102 where a processing module receives a store data object message from one of a user device, a dispersed storage (DS) processing unit, a DS managing unit, a storage integrity processing unit, and a DS unit. The store data object message may include one or more of a user device identifier (ID), a store request command, a data object name, a data object, a data type, a data size, a performance indicator, a security indicator, a priority indicator, and metadata input.
• The method continues atstep104 where the processing module determines a first set of operational parameters which may include one or more of a pillar width, a read threshold, a write threshold, and encoding method, a slicing method, an encryption method, and encryption keys. Such a determination may be based on one or more of the user device ID, a user vault lookup, the store request command, the data object name, the data object, the data type, the data size, the performance indicator, a security indicator, the priority indicator, and the metadata input. For example, the processing module determines the first set of operational parameters to include a pillar width of 16, and a read threshold of 10 based on the user vault lookup.
• The method continues atstep106 where the processing module determines a first set of DS units where the first set of DS units includes candidate DS units without consideration of historic metadata (e.g., operational and performance history). Such a determination may be based on one or more of the first set of operational parameters, the user device ID, a user vault lookup, the store request command, the data object name, the data object, the data type, the data size, the performance indicator, a security indicator, the priority indicator, and the metadata input. For example, the processing module may determine the first set of DS units to include DS units1-50 when the processing module determines that DS units1-50 are substantially available. Note that the number of DS units that comprise the first set of DS units may be greater than the pillar width.
• The method continues atstep108 where the processing module determines historical metadata for the first set of DS units where the historical metadata may include one or more of history of uptime, storage bandwidth, retrieval bandwidth, storage latency, retrieval latency, memory capacity, available memory, storage costs, rebuild time, location, and security factors (e.g., a security rating, security breach history). Such a determination may be based on one or more of the first set of DS units, a list, a query of a metadata agent, and a query of the first set of DS units. Note that a metadata agent may be implemented in any one of the system units or modules and may aggregate historical metadata information based on polling and/or receive historical metadata information from DS units from time to time.
• The method continues atstep110 where the processing module determines a second set of DS units which may comprise a subset of the first set of DS units such that the second set of DS units further optimizes the choice of DS units for storage of the data object. Atstep110, the processing module determines a second set of operational parameters where the second set of operational parameters coincide with the second set of DS units. Such a determination of the second set of DS units and the second set of operational parameters may be based on one or more of the first set of DS units, the first set of operational parameters, the historical metadata, a comparison of historical metadata to information received in the store data object message, and information from the user vault. For example, the processing module determines the second set of DS units to include DS units1-5,10-15, and21-26. Note that the number of DS units that comprise the second set of DS units is 16, which was the original pillar width chosen in the first set of operational parameters. In such an instance, the processing module determines that the 16 DS units met the requirements. In another example, the processing module determines the second set of DS units to include DS units1-32 and a second set of operational parameters to include a pillar width of 32 and a read threshold of 24 when the processing module determines that no combination of DS units of the first set of DS units may achieve thepillar width 16 of the first set of operational parameters will meet the requirements. In such an instance, the pillar width in a second set of operational parameters is different than the pillar width of the first set of operational parameters.
• The method continues atstep112 where the processing module dispersed storage error encodes the data object to produce encoded data slices in accordance with the second set of operational parameters for the data object. Atstep114, the processing module sends the encoded data slices with a store command to the second set of DS units for storage therein. In addition, the processing module may save the DS unit IDs in a list and/or in a vault to facilitate subsequent retrieval.
• FIG. 7 is another flowchart illustrating another example of storing data. The method begins withstep116 where the processing module receives a data storage request that includes metadata and data. Note that the metadata includes at least one of a requester identifier (ID) of a requesting device, a data ID, a data type, a data size, a storage requirement, identity of the base-line set of error coding dispersal storage function parameters, a performance indicator, a security indicator, and a priority indicator.
• The method continues atstep118 where the processing module determines a base-line set of error coding dispersal storage function parameters based on the metadata. Such a determination may include at least one of selecting a slicing pillar width and read threshold pair from a plurality of slicing pillar width and read threshold pairings based on a performance indicator (e.g., similar for high efficiency, farther apart for higher reliability), an encryption method from a plurality of encryption methods based on a security indicator, selecting an encryption key based on a requester identifier (ID) of a requesting device, and selecting an error coding method from a plurality of error coding methods based on a data type. For example, the processing module may select an error coding method that is more compatible with video when the data type indicates video. In another example, the processing model selects the slicing pillar width to be 16 and the read threshold to be 10 for higher reliability.
• The method continues atstep120 where the processing module identifies candidate dispersed storage (DS) units based on the base-line set of error coding dispersal storage function parameters. Such a determination may be based on one or more of the base-line set of error coding dispersal storage function parameters, a user device ID, a user vault lookup, a data storage request message, a data object name, a data object, a data type, a data size, a performance indicator, a security indicator, the priority indicator, and the metadata. For example, the processing module identifies DS units1-50 that are known to be substantially available based on a performance indicator. Note that the number of DS units that comprise the candidate DS units may be greater than the slicing pillar width.
• The method continues atstep122 where the processing module determines availability of the candidate DS units where the availability may include one or more of availability status (e.g., available now or not), an availability forecast (e.g., when it will be available in the future), and availability constraints (e.g., not available for files asked bytes in size, or only available for text data type files, or not available for files with the security indicator over a threshold). Such a determination may be based on one or more of the candidate DS units, a list, a query of an availability agent, and a query of the candidate DS units. Note that an availability agent may be implemented in any one of the system units or modules and may aggregate availability information based on polling and/or receive availability information from DS units from time to time.
• The method continues atstep124 where the processing module selects DS units of the candidate DS units based on the metadata to produce selected DS units. Such a determination may be based on one or more of the candidate DS units, the base-line set of error coding dispersal storage function parameters, historical metadata, a comparison of availability information to information received in the data storage request message, and information from a user vault. For example, the processing module determines the selected DS units to include DS units1-5,10-15, and21-26. Note that the number of DS units that comprise the selected DS units is 16, which was the original pillar width chosen in the base-line set of error coding dispersal storage function parameters. In such an instance, the processing module determined that the 16 DS units are available and meet the requirements.
• In another example, the processing module selects DS units1-32, a pillar width of 32 and a read threshold of 24 as the representation of the base-line set of error coding dispersal storage option parameters when there is not a combination of candidate DS units that can meet the base-line slicing pillar width of 16 requirement. Note that in this example the pillar width in the representation of the base-line set of error coding dispersal storage function parameters is different than the pillar width of the base-line set of error coding dispersal storage function parameters.
• The method continues atstep126 where the processing module dispersed storage error encodes the data in accordance with at least a representation of the base-line set of error coding dispersal storage function parameters to produce a set of encoded data slices. The method continues atstep128 where the processing module sends the set of encoded data slices to the selected DS units for storage therein.
• Alternatively, or in addition to, the processing module obtains error coding storage capabilities of the selected DS units and determines a set of error coding dispersal storage function parameters based on the error coding storage capabilities of the selected DS units and the metadata to provide the representation of the base-line set of error coding dispersal storage function parameters. Alternatively, or in addition to, the processing module identifies the candidate DS units based on dispersed error coding storage capabilities compatible with the base-line set of error coding dispersal storage function parameters. Alternatively, or in addition to, the processing module selects a DS unit of the DS units based on dispersed error coding storage performance characteristics being comparable to a desired dispersed error coding storage performance level. Alternatively, or in addition to, the processing module saves a record including the at least the representation of the base-line set of error coding dispersal storage function parameters and identifiers of the selected DS units.
• In another example of a method of operation, the processing module receives a data storage request that includes metadata and data. Note that the metadata includes at least one of a requester identifier (ID) of a requesting device, a data ID, a data type, a data size, a storage requirement, identity of the base-line set of error coding dispersal storage function parameters, a performance indicator, a security indicator, and a priority indicator. The method enters a loop, beginning with the step where the processing module determines a current set of error coding dispersal storage function parameters based on the metadata. Such determining of the current set of error coding dispersal storage function parameters includes at least one of selecting a slicing pillar width and read threshold pair from a plurality of slicing pillar width and read threshold pairings based on a performance indicator, selecting an encryption method from a plurality of encryption methods based on a security indicator, selecting an encryption key based on a requester identifier (ID) of a requesting device, and selecting an error coding method from a plurality of error coding methods based on a data type.
• The method continues with the step where the processing module selects dispersed storage (DS) units based on the current set of error coding dispersal storage function parameters to produce a current set of DS units. Such selecting of the DS units includes at least one of selecting the DS units based on dispersed error coding storage capabilities compatible with the current set of error coding dispersal storage function parameters and selecting a DS unit of the DS units based on dispersed error coding storage performance characteristics being comparable to a desired dispersed error coding storage performance level.
• The method continues with the step where the processing module determines whether the current set of DS units and the current set of error coding dispersal storage function parameters provides a desired dispersed error coding storage performance level. The method repeats the loop for at least one of a new current set of error coding dispersal storage function parameters and a new current set of DS units when the processing module determines that the current set of DS units and the current set of error coding dispersal storage function parameters do not provide the desired dispersed error coding storage performance level. The method exits the loop when the current set of DS units and the current set of error coding dispersal storage function parameters does provides the desired dispersed error coding storage performance level. Next, the processing module dispersed storage error encodes the data in accordance with the current set of error coding dispersal storage function parameters to produce a set of encoded data slices. The method continues with the step where the processing module sends the set of encoded data slices to the current set of DS units for storage therein. The method continues with the step where the processing module saves a record including the current set of error coding dispersal storage function parameters and identifiers of the current set of DS units.
• FIG. 8 is another flowchart illustrating another example of storing data. The method begins withstep130 where a processing module receives a store data object message from one of a user device, a dispersed storage (DS) processing unit, a DS managing unit, a storage integrity processing unit, and the DS unit. The store data object message may include one or more of a user device identifier (ID), a store request command, a data object name, a data object, a data type, a data size, a performance indicator, a security indicator, a priority indicator, and metadata input.
• The method continues atstep132 where the processing module determines preferred storage requirements, which may include requirements that are desired to optimize treatment of a goal (e.g., a performance level goal, a security goal, a cost goal, etc.). Such a determination may be based on one or more of information received in the store data object message (e.g., the user device ID, the store request command, the data object name, the data object, the data type, the data size, the performance indicator, the security indicator, the priority indicator), a vault lookup, a command, a message, and a predetermination. For example, the processing module determines that the preferred storage requirements include a performance requirement of a 10 ms retrieval latency time.
• The method continues atstep134 where the processing module determines minimum storage requirements, which may include the minimum level of requirements that are necessary to achieve a minimal goal (e.g., a minimal performance level goal, a minimal security goal, a cost goal, etc.). Such a determination may be based on one or more of information received in the store data object message (e.g., the user device ID, the store request command, the data object name, the data object, the data type, the data size, the performance indicator, the security indicator, the priority indicator), a vault lookup, a command, a message, and a predetermination. For example, the processing module determines that the minimum storage requirements include a performance requirement of a 100 ms retrieval latency time. In such an example, the preferred storage requirement includes a 10 ms retrieval latency time. In another example, the processing module determines a minimum storage requirement to include a cost goal where the cost is a maximum price allowable.
• The method continues atstep136 where the processing module determines a first set of operational parameters, which may include one or more of a pillar width, a read threshold, a write threshold, and encoding method, a slicing method, an encryption method, and encryption keys. Such a determination may be based on one or more of the preferred storage requirements, the user device ID, a user vault lookup, the store request command, the data object name, the data object, the data type, the data size, the performance indicator, a security indicator, the priority indicator, and the metadata input. For example, the processing module determines that the first set of operational parameters includes a pillar width of 16, and a read threshold of 10 based on a user vault lookup.
• The method continues atstep138 where the processing module determines a first set of DS units where the first set of DS units includes candidate DS units without consideration of storage metrics. Storage metrics may include one or more of history of uptime, storage bandwidth, retrieval bandwidth, storage latency, retrieval latency, memory capacity, available memory, storage costs, rebuild time, location, and security factors (e.g., a security rating, security breach history). Such a determination may be based on one or more of the first set of operational parameters, the user device ID, a user vault lookup, the store request command, the data object name, the data object, the data type, the data size, the performance indicator, a security indicator, the priority indicator, and the metadata input. For example, the processing module may determine the first set of DS units to include DS units1-50 based on a performance indicator. Note that the number of DS units that comprise the first set of DS units may be greater than the pillar width.
• The method continues atstep140 where the processing module determines the storage metrics for the first set of DS units. Such a determination may be based on one or more of the first set of DS units, a list, a query of a storage metrics agent, and a query of the first set of DS units. Note that the storage metrics agent may be implemented in any one of the system units or modules and may aggregate storage metrics information based on polling and/or receive storage metrics information from DS units from time to time.
• The method continues atstep142 where the processing module determines whether the storage metrics for the first set of DS units compares favorably to the preferred storage requirements. Note that a favorable comparison may indicate that the storage metrics meets or exceeds the requirements. The method branches to step152 when the processing module determines that the storage metrics for the first set of DS units does compare favorably to the preferred storage requirements. The method continues to step144 when the processing module determines that the storage metrics for the first set of DS units does not compare favorably to the preferred storage requirements.
• The method continues atstep144 where the processing module determines a second set of DS units, which may comprise a subset of the first set of DS units such that the second set of DS units may fit the minimum storage requirements. Atstep144, the processing module determines a second set of operational parameters where the second set of operational parameters coincide with the second set of DS units. Such a determination of the second set of DS units and the second set of operational parameters may be based on one or more of the first set of DS units, the first set of operational parameters, the storage metrics, a comparison of storage metrics information to information received in the store data object message, a list, a query of the storage metrics agent, a query of potential second set DS units, and information from the user vault.
• For example, the processing module determines the second set of DS units to include DS units1-5,10-15, and21-26. Note that the number of DS units that comprise the second set of DS units is 16, which was the original pillar width chosen in the first set of operational parameters. In such an instance, the processing module determines that the 16 DS units may meet the minimum requirements. In another example, the processing module determines the second set of DS units to include DS units1-32 and a second set of operational parameters to include a pillar width of 32 and a read threshold of 24 when the processing module determines that no combination of DS units of the first set of DS units are available and that achieve thepillar width 16 of the first set of operational parameters and will meet the first set of requirements. Note that in this example the pillar width in a second set of operational parameters is different than the pillar width of the first set of operational parameters.
• The method continues atstep146 where the processing module determines the storage metrics for the second set of DS units. Such a determination may be based on one or more of the second set of DS units, a list, a query of the storage metrics agent, and a query of the second set of DS units. The method continues atstep148 where the processing module determines whether the storage metrics for the second set of DS units compares favorably to the minimum storage requirements. Note that a favorable comparison may indicate that the storage metrics meets or exceeds the minimum requirements. The method branches to step152 when the processing module determines that the storage metrics for the second set of DS units does compare favorably to the preferred storage requirements. The method continues to step150 when the processing module determines that the storage metrics for the second set of DS units does not compare favorably to the minimum storage requirements. Atstep150, the processing module sends an error message (e.g., to a requester and/or a DS managing unit)
• The method continues atstep152 where the processing module dispersed storage error and codes the data object to produce encoded data slices in accordance with the first or second set of operational parameters for the data object. Atstep154, the processing module sends the encoded data slices with a store command to the chosen set of DS units (e.g., first or second set of DS units) for storage therein. In addition, the processing module may save the DS unit IDs in a list and/or in a vault to facilitate subsequent retrieval.
• In an alternative example of operation, preferred requirements may be chained. For instance, if a lower read latency is offered, a new higher price may be paid. In another example, requirements may vary by DS unit location. For instance, the security requirements of a DS unit in a first location may be different than the security requirements for a DS unit in a second location. In yet another example, the Processing module may negotiate with one or more of the DS units directly or through an agent to achieve more preferable storage metrics (e.g., a better price, a better grade of service).
• FIG. 9 is a flowchart illustrating an example of retrieving data. The method begins withstep156 where a processing module receives a retrieve data object message (e.g., from a user device, a dispersed storage (DS) processing unit, a DS managing unit, a storage integrity processing unit, and a DS unit). Such a retrieve data object message may include one or more of a user device identifier (ID), a retrieve request command, a data object name, a data type, a data size, a performance indicator, a security indicator, a priority indicator, and metadata input.
• The method continues atstep158 where the processing module determines operational parameters which may include one or more of a pillar width, a read threshold, a write threshold, and encoding method, a slicing method, an encryption method, slice names, and encryption keys. Such a determination may be based on one or more of the user device ID, a user vault lookup, the store request command, the data object name, the data type, the data size, the performance indicator, a security indicator, the priority indicator, and the metadata input. For example, the processing module determines that the operational parameters include a pillar width of 16, and a read threshold of 10 based on a received data type.
• The method continues atstep160 where the processing module determines DS units where the DS units includes candidate DS units to retrieve slices (e.g., the pillars where the slices were stored) without consideration of storage metrics (e.g., operational and performance history). Such a determination may be based on one or more of the operational parameters, the user device ID, a user vault lookup, a virtual DSN address to physical location table lookup, the retrieve request command, the data object name, the data type, the data size, the performance indicator, a security indicator, the priority indicator, and the metadata input. For example, the processing module may determine the DS units to include DS units1-16 where pillar slices were originally stored based on a lookup of the virtual DSN address to physical location table.
• The method continues atstep162 where the processing module determines the storage metrics for the DS units where the storage metrics may include one or more of history of uptime, storage bandwidth, retrieval bandwidth, storage latency, retrieval latency, memory capacity, available memory, storage costs, rebuild time, location, and security factors (e.g., a security rating, security breach history). Such a determination may be based on one or more of the DS units, a list, a query of a storage metrics agent, and a query of the DS units. Note that a storage metrics agent may be implemented in any one of the system units or modules and may aggregate storage metrics information based on polling and/or receive storage metrics information from DS units from time to time.
• The method continues atstep164 where the processing module determines a first subset of DS units, which may comprise a subset of the DS units such that the first subset of DS units further optimizes the choice of DS units for retrieval of the data object. Such a determination of the first subset of DS units may be based on one or more of the DS units, the operational parameters, the storage metrics, a comparison of the storage metrics to information received in the retrieve data object message, and information from the user vault. For example, the processing module determines the first subset of DS units to include DS units1-5 and11-15 when the first subset of DS units have storage metrics that indicate they have the fastest retrieval times. Note that the number of DS units that comprise the first subset of DS units is 10, which is the read threshold from the operational parameters. In such an instance, the processing module determines that the DS units meet the requirements for the fastest retrieval possible.
• The method continues atstep166 where the processing module sends an encoded data slice retrieval message to the current subset of DS units (e.g., the first subset of DS units on the first pass) where the encoded data slice retrieval message may include one or more of a retrieval command, a DS unit ID, the data object name, and slice names. The method continues atstep168 where the processing module receives encoded data slices from the DS units and determines whether enough pillars (e.g., a read threshold number) have been received to re-create the data segment. The method branches to step172 when the processing module determines that encoded data slices from enough pillars have been received. The method continues to step170 when the processing module determines that encoded data slices from enough pillars have not been received. Atstep170, the processing module determines a different subset of DS units. Such a determination of the different subset of DS units may be based on one or more of which pillars have been successfully received so far, which pillars were not successfully received (e.g., where requested), which pillars have not been tried yet, the DS units, the operational parameters, the storage metrics, a comparison of the storage metrics to information received in the retrieve data object message, and information from the user vault. For example, the processing module determines the different subset of DS units to include theDS units6 and7 when slices fromDS units4 and5 were not successfully received and where the processing module determines thatDS units6 and7 have the next best retrieval latency storage metrics. The method repeats back to step166 where the processing module sends the encoded data slice retrieval message to the current subset of DS units (e.g., the different subset of DS units).
• The method continues atstep172 where the processing module re-creates the data segment in accordance with the operational parameters. The processing module holds each data segment until all of the data segments have been successfully re-created. Atstep174, the processing module determines if all the segments have been re-created. The method branches to step178 when the processing module determines that all of the segments have been re-created. The method continues to step176 when the processing module determines that all of the segments have not been re-created. Atstep176, the processing module goes to the next data segment by selecting the slice names for the next data segment based on which data segments have been retrieved so far. The method repeats back tostep166.
• The method continues atstep178 where the processing module aggregates the data segments in accordance with the operational parameters to produce the data object when the processing module determines that all of the segments have been re-created. The method continues atstep180 where the processing module sends the data object to the requester. In an alternative example of operation, the processing module determines the DS unit subset to retrieve encoded data slices based on storage metrics of the DS units when the data object was originally stored in the DS units.
• FIG. 10 is another flowchart illustrating another example of storing data. The method begins withstep182 where a processing module receives a store data object message (e.g., from a user device, a dispersed storage (DS) processing unit, a DS managing unit, a storage integrity processing unit, and/or a DS unit). Such a store data object message may include one or more of a user device identifier (ID), a store request command, a data object name, a data object, a data type, a data size, a performance indicator, a security indicator, a priority indicator, and metadata input.
• The method continues atstep184 where the processing module determines operational parameters, which may include one or more of a pillar width, a read threshold, a write threshold, and encoding method, a slicing method, an encryption method, and encryption keys. Such a determination may be based on one or more of the user device ID, a user vault lookup, the store request command, the data object name, the data object, the data type, the data size, the performance indicator, a security indicator, the priority indicator, and the metadata input. For example, the processing module determines that the operational parameters include a pillar width of 16, and a read threshold of 10 based on the data size.
• The method continues atstep186 where the processing module determines DS units to optimize and send encoded data slices to for storage. Such a determination may be based on one or more of the operational parameters, the user device ID, a user vault lookup, the store request command, the data object name, the data object, the data type, the data size, the performance indicator, a security indicator, the priority indicator, and the metadata input. For example, the processing module may determine the DS units to include DS units1-16 to accommodate the pillar width of 16 based on the performance indicator.
• The method continues atstep188 where the processing module determines memory utilization for the DS units where the memory utilization may include one or more of memory capacity, available memory, utilized memory, storage costs, rebuild time, location, and security factors (e.g., a security rating, security breach history). Such a determination may be based on one or more of the DS units, a list, a query of a memory utilization agent, and a query of the DS units. Note that a memory utilization agent may be implemented in any one of the system units or modules and may aggregate memory utilization information based on polling and/or receive memory utilization information from DS units from time to time.
• The method continues atstep190 where the processing module determines DS unit memory utilization optimization where a goal of such optimization may include increasing the amount of available memory by moving encoded data slices from the memory of at least one DS unit to the memory of at least one other DS unit. Such a determination may be based on one or more of the memory utilization of DS units, available memory of DS units, memory capacity of DS units, a priority indicator of data stored in DS units, a security indicator of data stored in DS units, a performance indicator of data stored in DS units, a memory threshold, and the operational parameters. In an example, the processing module determines to free up utilized memory by moving encoded data slices from a first memory of a DS unit to a second memory of the same DS unit. In another example, the processing module determines to free up utilized memory by moving encoded data slices from a memory of a first DS unit to a memory of at least a second DS unit. For instance, the processing module moves encoded data slices from a memory of a first DS unit to a memory of a at least a second DS unit when the processing module determines that the memory utilization of the first DS Unit is greater than the memory threshold.
• The method continues atstep192 where the processing module optimizes DS unit memory utilization. In an example, the processing module retrieves encoded data slices from a memory of a first DS unit and sends the slices to a second DS unit for storage. The processing module sends a delete slice command to the first DS unit to delete the previously moved encoded data slices from the memory of the first DS unit. The processing module may update a virtual dispersed storage network (DSN) address to physical location table to indicate that the encoded data slices are now stored in the memory of the second DS unit. Note that the processing module may optimize the DS unit memory utilization as a background task from time to time.
• The method continues atstep194 where the processing module determines the encoded data slices in accordance with the second set of operational parameters for the data object. The method continues atstep196 where the processing module sends the encoded data slices with a store command to the DS units for storage. The processing module may save the DS unit IDs in a list and/or in a vault to facilitate subsequent retrieval.
• FIG. 11 is another flowchart illustrating another example of storing data. The method begins withstep198 where processing module receives a store data object message (e.g., from a user device, a dispersed storage (DS) processing unit, a DS managing unit, a storage integrity processing unit, and/or a DS unit). Such a store data object message may include one or more of a user device identifier (ID), a store request command, a data object name, a data object, a data type, a data size, a performance indicator, a security indicator, a priority indicator, and metadata input.
• The method continues atstep200 where the processing module determines operational parameters, which may include one or more of a pillar width, a read threshold, a write threshold, and encoding method, a slicing method, an encryption method, and encryption keys. Such a determination may be based on one or more of the user device ID, a user vault lookup, the store request command, the data object name, the data object, the data type, the data size, the performance indicator, a security indicator, the priority indicator, and the metadata input. For example, the processing module determines that the operational parameters include a pillar width of 16, and a read threshold of 10 based on the performance indicator.
• The method continues atstep202 where the processing module determines candidate DS units where the candidate DS units are determined at this step without consideration of metadata (e.g., operational and performance history). Such a determination may be based on one or more of the operational parameters, the user device ID, a user vault lookup, the store request command, the data object name, the data object, the data type, the data size, the performance indicator, a security indicator, the priority indicator, and the metadata input. For example, the processing module determines the first set of DS units to include DS units1-50 based on the data type. Note that the number of DS units that comprise the candidate DS units may be greater than the pillar width.
• The method continues atstep204 where the processing module determines metadata for the candidate DS units where the metadata may include one or more of history of uptime, storage bandwidth, retrieval bandwidth, storage latency, retrieval latency, memory capacity, available memory, storage costs, rebuild time, location, and security factors (e.g., a security rating, security breach history). Such a determination may be based on one or more of the candidate DS units, a list, a query of a metadata agent, and a query of the candidate DS units. Note that a metadata agent may be implemented in any one of the system units or modules and may aggregate metadata information based on polling and/or receive metadata information from DS units from time to time. Note that the processing module may determine the metadata of candidate DS units as a background task from time to time.
• The method continues atstep206 where the processing module determines DS unit requirements and weighting factors of the requirements where the weighting factors may indicate the relative importance of each requirement. Such DS unit requirements may include one or more of latency, security, priority, and location requirements. Such a determination may be based on one or more of the operational parameters, a vault lookup, information received in the store data object message, a predetermination, and a command. For example, the processing module determines the DS unit requirements to include the categories of uptime, agency, free space, security, and/or pricing based on indicators received in the store data object message and/or a user vault lookup. The processing module may determine the uptime weighting factor to be 0.30, the latency weighting factor to be 0.25, the free space weighting factor to be 0.10, the security weighting factor to be 0.15, and/or the pricing weighting factor to be 0.20 based on indicators received in the store data object message and/or a user vault lookup.
• The method continues atstep208 where the processing module determines a weighted comparison of the candidate DS units where the comparison compares the sum of each of the products of the metadata requirement value and the weighting factor. An example of a weighted scoring table where the weighted scoring table lists the weighted scores for each of the DS units is discussed in greater detail with reference toFIG. 12.
• The method continues atstep210 where the processing module determines DS units to utilize for storage from the candidate DS units such that the DS units further optimize the choice of DS units for storage of the data object. Such a determination of the DS units may be based on one or more of the DS units, the operational parameters, the DS unit metadata, DS unit requirements, weighting factors other requirements, results of the weighted comparison of the candidate DS units, a weighted scoring table lookup, the number of required DS units (e.g., the pillar width), and information from the user vault. For example, the processing module determines the DS units to include DS units1-5,10-15, and21-26 where those DS units have the best weighted comparison scores amongst the candidate DS units. Note that the number of DS units that comprise the DS units is 16, which is the original pillar width chosen in the operational parameters. In such an instance, the processing module determines that the 16 DS units met the requirements in the best possible way based on the weighted scoring.
• Atstep210, the processing module dispersed storage error encodes the data object to produce encoded data slices in accordance with the operational parameters. The method continues atstep212 for the processing module sends the encoded data slices with a store command to the DS units for storage therein. The processing module may save the DS unit IDs in a list and/or in a vault to facilitate subsequent retrieval.
• FIG. 12 is a table illustrating an example of a weighted scoring table214. Such a table may be utilized by a processing model to compare metadata values on a weighted basis between DS units by metadata categories to facilitate a selection of an optimized set of DS units. As illustrated, the weighted scoring table214 includes afactors section216 and a plurality of DS unit sections218-220 for a plurality of DS units. Thefactors section216 includes acategory field222 and aweighting factor field224 for each of thecategories222.Such categories222 may include requirements important to a requester requesting data storage. Weighting factors stored in theweighting factor field224 may be determined to prioritize requirements against each other to facilitate an optimized selection of DS units.
• As illustrated, the DS unit sections218-220 includes ametadata value field226,230 and aweighted score field228,232. Such metadata values226,230 represents raw metadata corresponding to therequirement category222 of the DS unit identity (e.g.,DS unit1oh field218,DS unit2 offield220. Such aweighted score228,232 may represent themetadata value226,230 adjusted by a scaling function (e.g., multiplied by the weighting factor224). The weighted scoring table to work for may also include one ormore totals 234 where a total sums the weighting factors224, and/or theweighted scores228,232. In an example, the weighting factors224 total to 1.0 and/or theweighted scores228 will have a maximum possible total of 100 points.
• Note that eachcategory222 may have adifferent weighting factor224 to adjust for the range of themetadata value226,230. For example, an uptime category may be measured in a “nines of reliability” fashion. For instance, a metadata value of nine nines produces a score 100 points, eightnines scores 90 points, sevennines scores 80 points, sixnines scores 70 points, fivenines scores 60 points, fournines scores 50 points, threenines scores 40 points, twonines scores 30 points, and one ninescores 20 points. In another example of metadata value scaling, latency may score 100 points for times less than 0.1 ms, 85 points for a latency time between 0.1 ms and 1.0 ms, 60 points for 1 ms to 10 ms, 40 points for 10 ms to 0.1 seconds, 20 points for 0.1 seconds to 1.0 seconds, and zero points for greater than 1 second. In another example of metadata scaling, free space greater than 10 terabytes (TB) may score 100 points, 80 points for 5 to 10 TB, 60 points for 0.5 to 5 TB, 40 points for 0.05 to 0.5 TB, 20 points for 0.005 to 0.05 TB, and zero points for less than 0.005 TB. In another example of metadata scaling, pricing may score 100 points for a price less than $0.001/gigabyte/month, 80 points for $0.001 to $0.01, 60 points for $0.01 to $0.04, 20 points for $0.04 to $0.1, and zero points for greater than $0.1.
• As illustrated,DS unit1scores 18 points for uptime andDS unit2scores 10 points for uptime. This may indicate thatDS unit1 is a superior choice as compared toDS unit2 for the category of uptime. As illustrated,DS unit2scores 10 points for free space andDS unit1scores 6 points for free space. This may indicate thatDS unit2 is a superior choice as compared toDS unit1 for the category of free space.
• As illustrated,DS unit1 has a total weighted score of 46 points inDS unit2 has a total weighted score of 44.5 points. In an example, this may indicate that on an overall basis and considering the weighting factors for each of thecategories DS unit1 may be a superior choice as compared toDS unit2. In another example, the requester may have an absolute minimum score requirement for one or more categories and/or for the total score. For instance, a processing module may choose to utilizeDS unit1 and notDS unit2 if the requester has a minimum overall weighted score requirement of 45 points.
• In another example, the processing module may choose to utilize the required pillar width of DS units where the chosen DS units have the highest overall total weighted scores. In such an instance, the processing module may choose the DS units based on ranking the total weighted scores. In another example, the processing module may choose DS units by chaining the weighted scores of two or more categories. For instance, the processing module may choose a DS unit that has a pricing weighted score above 10 points only if the uptime weighted score is above 20 points.
• FIG. 13 is a flowchart illustrating another example of storing data. The method begins withstep236 were a processing module receives a store data object message (e.g., from a user device, a dispersed storage (DS) processing unit, a DS managing unit, a storage integrity processing unit, and/or a DS unit). Such a store data object message may include one or more of a user device identifier (ID), a store request command, a data object name, a data object, a data type, a data size, a performance indicator, a security indicator, a priority indicator, and metadata input.
• The method continues atstep238 where the processing module determines operational parameters, which may include one or more of a pillar width, a read threshold, a write threshold, and encoding method, a slicing method, an encryption method, and encryption keys. Such a determination may be based on one or more of the user device ID, a user vault lookup, the store request command, the data object name, the data object, the data type, the data size, the performance indicator, a security indicator, the priority indicator, and the metadata input. For example, the processing module determines that the operational parameters include a pillar width of 16, and a read threshold of 10 based on the user vault lookup.
• The method continues atstep240 where the processing module determines candidate DS units where the candidate DS units are determined at this step without consideration of metadata (e.g., operational and performance history). Such a determination may be based on one or more of the operational parameters, the user device ID, a user vault lookup, the store request command, the data object name, the data object, the data type, the data size, the performance indicator, a security indicator, the priority indicator, and the metadata input. For example, the processing module determines the first set of DS units to include DS units1-50 based on the data type. Note that the number of DS units that comprise the candidate DS units may be greater than the pillar width.
• The method continues atstep242 where the processing module determines metadata for the candidate DS units where the metadata may include one or more of history of uptime, storage bandwidth, retrieval bandwidth, storage latency, retrieval latency, memory capacity, available memory, storage costs, rebuild time, location, and security factors (e.g., a security rating, security breach history). Such a determination may be based on one or more of the candidate DS units, a list, a query of a metadata agent, and/or a query of the candidate DS units. Note that a metadata agent may be implemented in any one of the system units or modules and may aggregate metadata information based on polling and/or receive metadata information from DS units from time to time. Note that the processing module may determine the metadata of candidate DS units as a background task from time to time.
• The method continues atstep244 or the processing module determines DS unit requirements and weighting factors of the requirements where the weighting factors may indicate the relative importance of each requirement. Such DS unit requirements may include the latency, security, priority, and/or location requirements. Such a determination may be based on one or more of the operational parameters, a vault lookup, information received in the store data object message, a predetermination, and a command. For example, the processing module determines the DS unit requirements to include the categories of uptime, agency, free space, security, and/or pricing based on indicators received in the store data object message and/or a user vault lookup. For instance, the processing module determines the uptime weighting factor to be 0.30, the latency weighting factor to be 0.25, the free space weighting factor to be 0.10, the security weighting factor to be 0.15, and/or the pricing weighting factor to be 0.20 based on indicators received in the store data object message and/or a user vault lookup.
• The method continues atstep246 were the processing module determines a weighted comparison of the candidate DS units where the comparison compares the sum of each of the products of a metadata requirement value and a weighting factor. An example of a weighted scoring table where the weighted scoring table lists the weighted scores for each of the DS units is discussed in greater detail with reference toFIG. 12.
• The method continues atstep248 where the processing module determines a desired range of weighted comparison scores of the candidate DS units. In such an instance, the processing module determines a minimum and/or a maximum value of a weighted score for each category. Such a determination may be based on one or more of the operational parameters, the candidate DS units, the metadata of the candidate DS units, the DS unit requirements, the weighting factors of the requirements, the weighted comparison scores, a vault lookup, information contained in the store data object message, a predetermination, and command. For instance, the processing module determines that the desired range for the uptime category is a score between 15 points and 25 points and the desired range for the pricing category is a score greater than 10 points.
• The method continues atstep250 where the processing module determines DS units from the candidate DS units that have weighted scores within the desired ranges. Such a determination may be based on one or more of the comparison of the desired ranges of weighted scores to the weighted scores of the DS units, a predetermination, a command, and a lookup. In an example, the processing module determines more DS units than is required by the pillar width of the operational parameters. In another example, the processing module determines fewer DS units as required by the pillar width of the operational parameters. In such an instance, there are not a sufficient number of DS units that have weighted scores within the desired ranges. In such a scenario, the processing module may determine to change the pillar width in an iterative fashion.
• The method continues atstep252 where the processing module determines the pillar width based on one or more of the number of DS units, a vault lookup, a predetermination, a command, and a message. In an example, the processing module may not change the pillar width when the number of DS units is greater than or equal to the pillar width. In another example, the processing module may lower the pillar width when the number of DS units is less than the pillar width. Note that the processing module may also change other elements of the operational parameters when the pillar width is changed. For example, the processing module may determine to change the read threshold to 5 from 10 when the processing model determines to change the pillar width to 8 from 16.
• The method continues atstep254 where the processing module determines a data segment size based on one or more of the pillar width, the size of the data object, a command, a message, a lookup, and data segment size guidance from a vault lookup. Atstep256, the processing module may save the modified operational parameters, which may include a new pillar width, a new data segment size, a new read threshold, new write threshold, and/or other new parameters. The processing module may save the modified operational parameters in a list and/or a vault.
• The method continues atstep258 where the processing module dispersed storage error encodes the data object to produce encoded data slices in accordance with the operational parameters. The method continues atstep260 where the processing module sends the encoded data slices with a store command to the DS units for storage. The processing module may save the DS unit IDs in a list and/or in a vault to facilitate subsequent retrieval.
• FIG. 14 is a structure diagram illustrating an example of a dispersed storage network (DSN) directory structure that includes aroot directory folder278, a plurality of second level intermediate file directory folders280-282, a plurality of third level intermediate file directory folders284-290, and a plurality of DSN systems292-296. Note that any number of intermediate file directory folders may exist in a given level of the structure. Note that any number of levels may exist within the structure.
• The plurality of third level intermediate file directory folders284-290 may be utilized to associate a computer file structure (e.g., depicted as a plurality of levels) to the plurality of DSN systems292-296. In an example, intermediate file directory folder284 includes theroot directory folder278 identifier, the intermediate file directory folder280 identifier, a path, a file name, a DSN system identifier of where encoded data slices are stored, DS unit identifiers of where the encoded data slices are stored within one or more of the plurality of DSN systems292-296, and a list of the plurality of DSN systems292-296.
• In an example of operation, a processing module of a user device utilizes the computer file structure to represent a file system structure and track where data files are stored as encoded data slices within the plurality of DSN systems292-296. For instance, a processing module dispersed storage encodes data with a filename of stuff txt to produce encoded data slices with slice names101-105 and stores the encoded data slices in DS units1-5 ofDSN system296. The processing module stores stuff txt, slice names101-105, an identifier ofDSN system296, identifiers of DS units1-5, an identifier of the second level intermediate file directory folder280, and an identifier of the root directory folder270 in the intermediate file directory folder284. The method of operation of the processing module to utilize the structure is discussed in greater detail with reference toFIG. 15.
• FIG. 15 is another flowchart illustrating another example of storing data. The method begins atstep300 where processing module receives a data storage request. Atstep302, the processing module accesses a file directory to identify dispersed storage network (DSN) systems. Note that the file directory may include one or more of a listing of DSN systems, a root file directory identifier, one or more intermediate file directory identifiers, a file name or data block identifier, a DSN system identifier, DS unit identifiers, and slice names. The method continues atstep304 where the processing module identifies dispersed storage (DS) units based on a desired dispersed error coding performance level to produce identified DS units for each of the DSN systems. Note that the desired dispersed error coding performance level includes at least one of an indication of dispersed error coding storage requirements, an indication of historical dispersed error coding storage performance, an indication of available storage, an indication of processing capabilities, an indication of latency performance, and an indication of bandwidth performance. For example, the processing module identifies DS units1-5 as identified DS units when the processing module determines that the latency performance of DS units1-5 best match the desired dispersed error coding performance level.
• The method continues atstep306 where the processing module selects one of the DSN systems based on a collective dispersed error coding performance level of the identified DS units of the one of the DSN systems to produce a selected DSN system. For example, the processing module selectsDSN system3 when the collective dispersed error coding performance level indicates a lowest latency performance level amongst the DSN systems. The method continues atstep308 or the processing module selects a set of DS units from the identified DS units of the selected DSN system based on the desired dispersed error coding performance level to produce selected DS units wherein the selected DS units store a set of encoded data slices. Atstep310, the processing module encodes data to produce a set of encoded data slices.
• Atstep312, the processing module sends the set of encoded data slices to the selected DS units for storage therein. The method continues atstep314 where the processing module updates the file directory to include an identity of the selected DS units and an identity of the set of encoded data slices. The processing module updates the file directory by one or more of creating a file name associated with the set of encoded data slices, linking the file name to one or more intermediate file directory folders, linking the one or more intermediate file directories folders to a root directory folder, linking the file name to a DSN identifier of the selected DSN system, linking the file name to slice names of the set of encoded data slices, and linking the slice names to DS unit identifiers of the selected DS units. In addition, the processing module may create at least one of the one or more intermediate file directory folders.
• In an alternate example of operation, the method begins atstep300 where the processing module receives a data storage request for a particular type of data storage. Note that the particular type of data storage includes at least one of a data type indication (e.g., video, audio, text files, etc.), a weighted storage requirement (e.g., latency is more important than availability), a data retrieval latency indication, a data bandwidth indication, dispersed error coding storage function parameters, and a date usage indication (e.g., real-time, nearline, archive).
• The alternate example of operation method continues atstep302 where the processing module identifies a dispersed storage network (DSN) system from a plurality of DSN systems based on the particular type of data storage to produce an identified DSN system. For example, the processing module identifiesDSN system4 one the particular type of data storage is video andDSN system4 is optimized to store video. The method continues atstep304 where the processing module identifies dispersed storage (DS) units within the identified DSN system based on the particular type of data storage to produce identified DS units. For example, the processing module identifies DS units10-30 when the particular type of data storage is video and DS units10-30 are optimized to store video. The method continues withstep306 where the processing module selects the identified DSN system as a selected DSN system.
• The alternate example of operation method continues atstep308 where the processing module selects a set of DS units from the identified DS units of the identified DSN system based on the particular type of data storage to produce selected DS units, wherein the selected DS units store a set of encoded data slices. For example, the processing module selects DS units10-26 when the particular type of data storage is video and DS units10-26 is highly optimized for video. Atstep310, the processing module encodes data to produce a set of encoded data slices. Atstep312, the processing module sends the set of encoded data slices to the selected DS units for storage therein.
• The method continues atstep314 where the processing module updates the file directory to include an identity of the selected DS units and an identity of the set of encoded data slices. The processing module updates the file directory by creating a file name associated with the set of encoded data slices, linking the file name to one or more intermediate file directory folders, linking the one or more intermediate file directories folders to a root directory folder, linking the file name to a DSN identifier of the identified DSN system, linking the file name to slice names of the set of encoded data slices, and linking the slice names to DS unit identifiers of the selected DS units. In addition, the processing module may create at least one of the one or more intermediate file directory folders.
• As may be used herein, the terms “substantially” and “approximately” provides an industry-accepted tolerance for its corresponding term and/or relativity between items. Such an industry-accepted tolerance ranges from less than one percent to fifty percent and corresponds to, but is not limited to, component values, integrated circuit process variations, temperature variations, rise and fall times, and/or thermal noise. Such relativity between items ranges from a difference of a few percent to magnitude differences. As may also be used herein, the term(s) “operably coupled to”, “coupled to”, and/or “coupling” includes direct coupling between items and/or indirect coupling between items via an intervening item (e.g., an item includes, but is not limited to, a component, an element, a circuit, and/or a module) where, for indirect coupling, the intervening item does not modify the information of a signal but may adjust its current level, voltage level, and/or power level. As may further be used herein, inferred coupling (i.e., where one element is coupled to another element by inference) includes direct and indirect coupling between two items in the same manner as “coupled to”. As may even further be used herein, the term “operable to” or “operably coupled to” indicates that an item includes one or more of power connections, input(s), output(s), etc., to perform, when activated, one or more its corresponding functions and may further include inferred coupling to one or more other items. As may still further be used herein, the term “associated with”, includes direct and/or indirect coupling of separate items and/or one item being embedded within another item. As may be used herein, the term “compares favorably”, indicates that a comparison between two or more items, signals, etc., provides a desired relationship. For example, when the desired relationship is thatsignal1 has a greater magnitude thansignal2, a favorable comparison may be achieved when the magnitude ofsignal1 is greater than that ofsignal2 or when the magnitude ofsignal2 is less than that ofsignal1.
• The present invention has also been described above with the aid of method steps illustrating the performance of specified functions and relationships thereof. The boundaries and sequence of these functional building blocks and method steps have been arbitrarily defined herein for convenience of description. Alternate boundaries and sequences can be defined so long as the specified functions and relationships are appropriately performed. Any such alternate boundaries or sequences are thus within the scope and spirit of the claimed invention.
• The present invention has been described, at least in part, in terms of one or more embodiments. An embodiment of the present invention is used herein to illustrate the present invention, an aspect thereof, a feature thereof, a concept thereof, and/or an example thereof. A physical embodiment of an apparatus, an article of manufacture, a machine, and/or of a process that embodies the present invention may include one or more of the aspects, features, concepts, examples, etc. described with reference to one or more of the embodiments discussed herein.
• The present invention has been described above with the aid of functional building blocks illustrating the performance of certain significant functions. The boundaries of these functional building blocks have been arbitrarily defined for convenience of description. Alternate boundaries could be defined as long as the certain significant functions are appropriately performed. Similarly, flow diagram blocks may also have been arbitrarily defined herein to illustrate certain significant functionality. To the extent used, the flow diagram block boundaries and sequence could have been defined otherwise and still perform the certain significant functionality. Such alternate definitions of both functional building blocks and flow diagram blocks and sequences are thus within the scope and spirit of the claimed invention. One of average skill in the art will also recognize that the functional building blocks, and other illustrative blocks, modules and components herein, can be implemented as illustrated or by discrete components, application specific integrated circuits, processors executing appropriate software and the like or any combination thereof.

Claims (18)

1. A method comprises:

receiving a data storage request;

accessing a file directory to identify dispersed storage network (DSN) systems;

for each of the DSN systems, identifying dispersed storage (DS) units based on a desired dispersed error coding performance level to produce identified DS units;

selecting one of the DSN systems based on a collective dispersed error coding performance level of the identified DS units of the one of the DSN systems to produce a selected DSN system;

selecting a set of DS units from the identified DS units of the selected DSN system based on the desired dispersed error coding performance level to produce selected DS units, wherein the selected DS units store a set of encoded data slices; and

updating the file directory to include an identity of the selected DS units and an identity of the set of encoded data slices.

2. The method ofclaim 1, wherein the file directory comprises:

a listing of DSN systems;

a root file directory identifier;

one or more intermediate file directory identifiers;

a file name or data block identifier;

a DSN system identifier;

DS unit identifiers; and

slice names.

3. The method ofclaim 1, wherein the updating the file directory comprises:

creating a file name associated with the set of encoded data slices;

linking the file name to one or more intermediate file directory folders;

linking the one or more intermediate file directories folders to a root directory folder;

linking the file name to a DSN identifier of the selected DSN system;

linking the file name to slice names of the set of encoded data slices; and

linking the slice names to DS unit identifiers of the selected DS units.

4. The method ofclaim 3 further comprises:

creating at least one of the one or more intermediate file directory folders.

5. The method ofclaim 1, wherein the desired dispersed error coding performance level comprises at least one of:

an indication of dispersed error coding storage requirements;

an indication of historical dispersed error coding storage performance;

an indication of available storage;

an indication of processing capabilities;

an indication of latency performance; and

an indication of bandwidth performance.

6. A method comprises:

receiving a data storage request for a particular type of data storage;

identifying a dispersed storage network (DSN) system from a plurality of DSN systems based on the particular type of data storage to produce an identified DSN system;

identifying dispersed storage (DS) units within the identified DSN system based on the particular type of data storage to produce identified DS units;

selecting a set of DS units from the identified DS units of the identified DSN system based on the particular type of data storage to produce selected DS units, wherein the selected DS units store a set of encoded data slices; and

updating the file directory to include an identity of the selected DS units and an identity of the set of encoded data slices.

7. The method ofclaim 6, wherein the particular type of data storage comprises at least one of:

a data type indication;

a weighted storage requirement;

a data retrieval latency indication;

a data bandwidth indication;

dispersed error coding storage function parameters; and

a date usage indication.

8. The method ofclaim 6, wherein the updating the file directory comprises:

creating a file name associated with the set of encoded data slices;

linking the file name to one or more intermediate file directory folders;

linking the one or more intermediate file directories folders to a root directory folder;

linking the file name to a DSN identifier of the identified DSN system;

linking the file name to slice names of the set of encoded data slices; and

linking the slice names to DS unit identifiers of the selected DS units.

9. The method ofclaim 8 further comprises:

creating at least one of the one or more intermediate file directory folders.

10. A computer comprises:

an interface; and

a processing module operable to:

receive, via the interface, a data storage request;

access a file directory to identify dispersed storage network (DSN) systems;

identify dispersed storage (DS) units based on a desired dispersed error coding performance level to produce identified DS units for each of the DSN systems;

select one of the DSN systems based on a collective dispersed error coding performance level of the identified DS units of the one of the DSN systems to produce a selected DSN system;

select a set of DS units from the identified DS units of the selected DSN system based on the desired dispersed error coding performance level to produce selected DS units, wherein the selected DS units store a set of encoded data slices; and

update the file directory to include an identity of the selected DS units and an identity of the set of encoded data slices.

11. The computer ofclaim 10, wherein the file directory comprises:

a listing of DSN systems;

a root file directory identifier;

one or more intermediate file directory identifiers;

a file name or data block identifier;

a DSN system identifier;

DS unit identifiers; and

slice names.

12. The computer ofclaim 10, wherein the processing module further functions to update the file directory by:

creating a file name associated with the set of encoded data slices;

linking the file name to one or more intermediate file directory folders;

linking the one or more intermediate file directories folders to a root directory folder;

linking the file name to a DSN identifier of the selected DSN system;

linking the file name to slice names of the set of encoded data slices; and

linking the slice names to DS unit identifiers of the selected DS units.

13. The computer ofclaim 12, wherein the processing module further functions to:

create at least one of the one or more intermediate file directory folders.

14. The computer ofclaim 10, wherein the desired dispersed error coding performance level comprises at least one of:

an indication of dispersed error coding storage requirements;

an indication of historical dispersed error coding storage performance;

an indication of available storage;

an indication of processing capabilities;

an indication of latency performance; and

an indication of bandwidth performance.

15. A computer comprises:

an interface; and

a processing module operable to:

receive, via the interface, a data storage request for a particular type of data storage;

identify a dispersed storage network (DSN) system from a plurality of DSN systems based on the particular type of data storage to produce an identified DSN system;

identify dispersed storage (DS) units within the identified DSN system based on the particular type of data storage to produce identified DS units;

select a set of DS units from the identified DS units of the identified DSN system based on the particular type of data storage to produce selected DS units, wherein the selected DS units store a set of encoded data slices; and

update the file directory to include an identity of the selected DS units and an identity of the set of encoded data slices.

16. The computer ofclaim 15, wherein the particular type of data storage comprises at least one of:

a data type indication;

a weighted storage requirement;

a data retrieval latency indication;

a data bandwidth indication;

dispersed error coding storage function parameters; and

a date usage indication.

17. The computer ofclaim 15, wherein the processing module further functions to update the file directory by:

creating a file name associated with the set of encoded data slices;

linking the file name to one or more intermediate file directory folders;

linking the one or more intermediate file directories folders to a root directory folder;

linking the file name to a DSN identifier of the identified DSN system;

linking the file name to slice names of the set of encoded data slices; and

linking the slice names to DS unit identifiers of the selected DS units.

18. The computer ofclaim 17, wherein the processing module further functions to:

create at least one of the one or more intermediate file directory folders.

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