Abstract:
Adaptive replication of data in a dispersed storage network (DSN) to improve data access performance. In various examples, a DSN storage unit determines that a frequency of slice access of an encoded data slice stored by the storage unit compares unfavorably to a first slice access threshold (e.g., a greater number of accesses than a threshold number of accesses over a given period of time). The storage unit then identifies at least one secondary storage unit and replicates the encoded data slice to generate a replicated encoded data slice. The replicated encoded data slice is then sent to the at least one secondary storage unit for storage therein. In addition, a slice storage location table is updated to associate the at least one secondary storage unit and the replicated encoded data slice such that future access requests for the encoded data slice may be re-directed to a secondary storage unit.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    The present U.S. Utility patent application claims priority pursuant to 35 U.S.C. §120 as a continuation-in-part of U.S. Utility application Ser. No. 14/147,982, entitled “GENERATING A SECURE SIGNATURE UTILIZING A PLURALITY OF KEY SHARES,” filed Jan. 6, 2014, which is a continuation of U.S. Utility application Ser. No. 13/413,232, entitled “GENERATING A SECURE SIGNATURE UTILIZING A PLURALITY OF KEY SHARES,” filed Mar. 6, 2012 and now issued as U.S. Pat. No. 8,627,091, which claims priority pursuant to 35 U.S.C. §119(e) to U.S. Provisional Application No. 61/470,524, entitled “ENCODING DATA STORED IN A DISPERSED STORAGE NETWORK,” filed Apr. 1, 2011, all of which are hereby incorporated herein by reference in their entirety and made part of the present U.S. Utility patent application for all purposes. 
     
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT 
       [0002]    Not applicable. 
       INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC 
       [0003]    Not applicable. 
       BACKGROUND OF THE INVENTION 
       [0004]    Technical Field of the Invention 
         [0005]    This invention relates generally to computer networks, and more particularly to adaptive replication of stored data in a dispersed storage network. 
         [0006]    Description of Related Art 
         [0007]    Computing devices are known to communicate data, process data, and/or store data. Such computing devices range from wireless smart phones, laptops, tablets, personal computers (PC), work stations, and video game devices, to data centers that support millions of web searches, stock trades, or on-line purchases every day. In general, a computing device includes a central processing unit (CPU), a memory system, user input/output interfaces, peripheral device interfaces, and an interconnecting bus structure. 
         [0008]    As is further known, a computer may effectively extend its CPU by using “cloud computing” to perform one or more computing functions (e.g., a service, an application, an algorithm, an arithmetic logic function, etc.) on behalf of the computer. Further, for large services, applications, and/or functions, cloud computing may be performed by multiple cloud computing resources in a distributed manner to improve the response time for completion of the service, application, and/or function. For example, Hadoop is an open source software framework that supports distributed applications enabling application execution by thousands of computers. 
         [0009]    In addition to cloud computing, a computer may use “cloud storage” as part of its memory system. As is known, cloud storage enables a user, via its computer, to store files, applications, etc. on a remote storage system. The remote storage system may include a RAID (redundant array of independent disks) system and/or a dispersed storage system that uses an error correction scheme to encode data for storage. 
         [0010]    In a RAID system, a RAID controller adds parity data to the original data before storing it across an array of disks. The parity data is calculated from the original data such that the failure of a single disk typically will not result in the loss of the original data. While RAID systems can address certain memory device failures, these systems may suffer from effectiveness, efficiency and security issues. For instance, as more disks are added to the array, the probability of a disk failure rises, which may increase maintenance costs. When a disk fails, for example, it needs to be manually replaced before another disk(s) fails and the data stored in the RAID system is lost. To reduce the risk of data loss, data on a RAID device is often copied to one or more other RAID devices. While this may reduce the possibility of data loss, it also raises security issues since multiple copies of data may be available, thereby increasing the chances of unauthorized access. In addition, co-location of some RAID devices may result in a risk of a complete data loss in the event of a natural disaster, fire, power surge/outage, etc. 
     
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S) 
         [0011]      FIG. 1  is a schematic block diagram of an embodiment of a dispersed or distributed storage network (DSN) in accordance with the present disclosure; 
           [0012]      FIG. 2  is a schematic block diagram of an embodiment of a computing core in accordance with the present disclosure; 
           [0013]      FIG. 3  is a schematic block diagram of an example of dispersed storage error encoding of data in accordance with the present disclosure; 
           [0014]      FIG. 4  is a schematic block diagram of a generic example of an error encoding function in accordance with the present disclosure; 
           [0015]      FIG. 5  is a schematic block diagram of a specific example of an error encoding function in accordance with the present disclosure; 
           [0016]      FIG. 6  is a schematic block diagram of an example of slice naming information for an encoded data slice (EDS) in accordance with the present disclosure; 
           [0017]      FIG. 7  is a schematic block diagram of an example of dispersed storage error decoding of data in accordance with the present disclosure; 
           [0018]      FIG. 8  is a schematic block diagram of a generic example of an error decoding function in accordance with the present disclosure; 
           [0019]      FIG. 9  is a schematic block diagram of another embodiment of a DSN performing replication of encoded data slices in accordance with the present disclosure; and 
           [0020]      FIG. 10  is a logic diagram illustrating an example of replicating encoded data slices in accordance with the present disclosure. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0021]      FIG. 1  is a schematic block diagram of an embodiment of a dispersed, or distributed, storage network (DSN)  10  that includes a plurality of computing devices  12 - 16 , a managing unit  18 , an integrity processing unit  20 , and a DSN memory  22 . The components of the DSN  10  are coupled to a network  24 , which may include one or more wireless and/or wire lined communication systems; one or more non-public intranet systems and/or public internet systems; and/or one or more local area networks (LAN) and/or wide area networks (WAN). 
         [0022]    The DSN memory  22  includes a plurality of storage units  36  that may be located at geographically different sites (e.g., one in Chicago, one in Milwaukee, etc.), at a common site, or a combination thereof. For example, if the DSN memory  22  includes eight storage units  36 , each storage unit is located at a different site. As another example, if the DSN memory  22  includes eight storage units  36 , all eight storage units are located at the same site. As yet another example, if the DSN memory  22  includes eight storage units  36 , a first pair of storage units are at a first common site, a second pair of storage units are at a second common site, a third pair of storage units are at a third common site, and a fourth pair of storage units are at a fourth common site. Note that a DSN memory  22  may include more or less than eight storage units  36 . Further note that each storage unit  36  includes a computing core (as shown in  FIG. 2 , or components thereof) and a plurality of memory devices for storing dispersed storage (DS) error encoded data. 
         [0023]    Each of the storage units  36  is operable to store DS error encoded data and/or to execute (e.g., in a distributed manner) maintenance tasks and/or data-related tasks. The tasks may be a simple function (e.g., a mathematical function, a logic function, an identify function, a find function, a search engine function, a replace function, etc.), a complex function (e.g., compression, human and/or computer language translation, text-to-voice conversion, voice-to-text conversion, etc.), multiple simple and/or complex functions, one or more algorithms, one or more applications, maintenance tasks (e.g., rebuilding/replication of data slices, updating hardware, rebooting software, restarting a particular software process, performing an upgrade, installing a software patch, loading a new software revision, performing an off-line test, prioritizing tasks associated with an online test, etc.), etc. 
         [0024]    Each of the computing devices  12 - 16 , the managing unit  18 , integrity processing unit  20  and (in various embodiments) the storage units  36  include a computing core  26 , which includes network interfaces  30 - 33 . Computing devices  12 - 16  may each be a portable computing device and/or a fixed computing device. A portable computing device may be a social networking device, a gaming device, a cell phone, a smart phone, a digital assistant, a digital music player, a digital video player, a laptop computer, a handheld computer, a tablet, a video game controller, and/or any other portable device that includes a computing core. A fixed computing device may be a computer (PC), 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. Note that each of the managing unit  18  and the integrity processing unit  20  may be separate computing devices, may be a common computing device, and/or may be integrated into one or more of the computing devices  12 - 16  and/or into one or more of the storage units  36 . 
         [0025]    Each interface  30 ,  32 , and  33  includes software and hardware to support one or more communication links via the network  24  indirectly and/or directly. For example, interface  30  supports a communication link (e.g., wired, wireless, direct, via a LAN, via the network  24 , etc.) between computing devices  14  and  16 . As another example, interface  32  supports communication links (e.g., a wired connection, a wireless connection, a LAN connection, and/or any other type of connection to/from the network  24 ) between computing devices  12  and  16  and the DSN memory  22 . As yet another example, interface  33  supports a communication link for each of the managing unit  18  and the integrity processing unit  20  to the network  24 . 
         [0026]    Computing devices  12  and  16  include a dispersed storage (DS) client module  34 , which enables the computing device to dispersed storage error encode and decode data (e.g., data object  40 ) as subsequently described with reference to one or more of  FIGS. 3-8 . In this example embodiment, computing device  16  functions as a dispersed storage processing agent for computing device  14 . In this role, computing device  16  dispersed storage error encodes and decodes data on behalf of computing device  14 . With the use of dispersed storage error encoding and decoding, the DSN  10  is tolerant of a significant number of storage unit failures (the number of failures is based on parameters of the dispersed storage error encoding function) without loss of data and without the need for a redundant or backup copies of the data. Further, the DSN  10  stores data for an indefinite period of time without data loss and in a secure manner (e.g., the system is very resistant to unauthorized attempts at accessing the data). 
         [0027]    In operation, the managing unit  18  performs DS management services. For example, the managing unit  18  establishes distributed data storage parameters (e.g., vault creation, distributed storage parameters, security parameters, billing information, user profile information, etc.) for computing devices  12 - 14  individually or as part of a group of user devices. As a specific example, the managing unit  18  coordinates creation of a vault (e.g., a virtual memory block associated with a portion of an overall namespace of the DSN) within the DSN memory  22  for a user device, a group of devices, or for public access and establishes per vault dispersed storage (DS) error encoding parameters for a vault. The managing unit  18  facilitates storage of DS error encoding parameters for each vault by updating registry information of the DSN  10 , where the registry information may be stored in the DSN memory  22 , a computing device  12 - 16 , the managing unit  18 , and/or the integrity processing unit  20 . 
         [0028]    The managing unit  18  creates and stores user profile information (e.g., an access control list (ACL)) in local memory and/or within memory of the DSN memory  22 . The user profile information includes authentication information, permissions, and/or the security parameters. The security parameters may include encryption/decryption scheme, one or more encryption keys, key generation scheme, and/or data encoding/decoding scheme. 
         [0029]    The managing unit  18  creates billing information for a particular user, a user group, a vault access, public vault access, etc. For instance, the managing unit  18  tracks the number of times a user accesses a non-public vault and/or public vaults, which can be used to generate per-access billing information. In another instance, the managing unit  18  tracks the amount of data stored and/or retrieved by a user device and/or a user group, which can be used to generate per-data-amount billing information. 
         [0030]    As another example, the managing unit  18  performs network operations, network administration, and/or network maintenance. Network operations includes authenticating user data allocation/access requests (e.g., read and/or write requests), managing creation of vaults, establishing authentication credentials for user devices, adding/deleting components (e.g., user devices, storage units, and/or computing devices with a DS client module  34 ) to/from the DSN  10 , and/or establishing authentication credentials for the storage units  36 . Network administration includes monitoring devices and/or units for failures, maintaining vault information, determining device and/or unit activation status, determining device and/or unit loading, and/or determining any other system level operation that affects the performance level of the DSN  10 . Network maintenance includes facilitating replacing, upgrading, repairing, and/or expanding a device and/or unit of the DSN  10 . Examples of load balancing, service differentiation and dynamic resource selection for data access operations are discussed in greater detail with reference to  FIGS. 9-13 . 
         [0031]    To support data storage integrity verification within the DSN  10 , the integrity processing unit  20  (and/or other devices in the DSN  10 ) may perform rebuilding of ‘bad’ or missing encoded data slices. At a high level, the integrity processing unit  20  performs rebuilding by periodically attempting to retrieve/list encoded data slices, and/or slice names of the encoded data slices, from the DSN memory  22 . Retrieved encoded slices are checked for errors due to data corruption, outdated versioning, etc. If a slice includes an error, it is flagged as a ‘bad’ or ‘corrupt’ slice. Encoded data slices that are not received and/or not listed may be flagged as missing slices. Bad and/or missing slices may be subsequently rebuilt using other retrieved encoded data slices that are deemed to be good slices in order to produce rebuilt slices. A multi-stage decoding process may be employed in certain circumstances to recover data even when the number of valid encoded data slices of a set of encoded data slices is less than a relevant decode threshold number. The rebuilt slices may then be written to DSN memory  22 . Note that the integrity processing unit  20  may be a separate unit as shown, included in DSN memory  22 , included in the computing device  16 , and/or distributed among the storage units  36 . 
         [0032]      FIG. 2  is a schematic block diagram of an embodiment of a computing core  26  that includes a processing module  50 , a memory controller  52 , main memory  54 , a video graphics processing unit  55 , an input/output (IO) controller  56 , a peripheral component interconnect (PCI) interface  58 , an IO interface module  60 , at least one IO device interface module  62 , a read only memory (ROM) basic input output system (BIOS)  64 , and one or more memory interface modules. The one or more memory interface module(s) includes one or more of a universal serial bus (USB) interface module  66 , a host bus adapter (HBA) interface module  68 , a network interface module  70 , a flash interface module  72 , a hard drive interface module  74 , and a DSN interface module  76 . 
         [0033]    The DSN interface module  76  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.). The DSN interface module  76  and/or the network interface module  70  may function as one or more of the interface  30 - 33  of  FIG. 1 . Note that the IO device interface module  62  and/or the memory interface modules  66 - 76  may be collectively or individually referred to as IO ports. 
         [0034]      FIG. 3  is a schematic block diagram of an example of dispersed storage error encoding of data. When a computing device  12  or  16  has data to store it disperse storage error encodes the data in accordance with a dispersed storage error encoding process based on dispersed storage error encoding parameters. The dispersed storage error encoding parameters include an encoding function (e.g., information dispersal algorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding, non-systematic encoding, on-line codes, etc.), a data segmenting protocol (e.g., data segment size, fixed, variable, etc.), and per data segment encoding values. The per data segment encoding values include a total, or pillar width, number (T) of encoded data slices per encoding of a data segment (i.e., in a set of encoded data slices); a decode threshold number (D) of encoded data slices of a set of encoded data slices that are needed to recover the data segment; a read threshold number (R) of encoded data slices to indicate a number of encoded data slices per set to be read from storage for decoding of the data segment; and/or a write threshold number (W) to indicate a number of encoded data slices per set that must be accurately stored before the encoded data segment is deemed to have been properly stored. The dispersed storage error encoding parameters may further include slicing information (e.g., the number of encoded data slices that will be created for each data segment) and/or slice security information (e.g., per encoded data slice encryption, compression, integrity checksum, etc.). 
         [0035]    In the present example, Cauchy Reed-Solomon has been selected as the encoding function (a generic example is shown in  FIG. 4  and a specific example is shown in  FIG. 5 ); the data segmenting protocol is to divide the data object into fixed sized data segments; and the per data segment encoding values include: a pillar width of five, a decode threshold of three, a read threshold of four, and a write threshold of four. In accordance with the data segmenting protocol, the computing device  12  or  16  divides the data (e.g., a file (e.g., text, video, audio, etc.), a data object, or other data arrangement) into a plurality of fixed sized data segments (e.g.,  1  through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more). The number of data segments created is dependent of the size of the data and the data segmenting protocol. 
         [0036]    The computing device  12  or  16  then disperse storage error encodes a data segment using the selected encoding function (e.g., Cauchy Reed-Solomon) to produce a set of encoded data slices.  FIG. 4  illustrates a generic Cauchy Reed-Solomon encoding function, which includes an encoding matrix (EM), a data matrix (DM), and a coded matrix (CM). The size of the encoding matrix (EM) is dependent on the pillar width number (T) and the decode threshold number (D) of selected per data segment encoding values. To produce the data matrix (DM), the data segment is divided into a plurality of data blocks and the data blocks are arranged into D number of rows with Z data blocks per row. Note that Z is a function of the number of data blocks created from the data segment and the decode threshold number (D). The coded matrix is produced by matrix multiplying the data matrix by the encoding matrix. 
         [0037]      FIG. 5  illustrates a specific example of Cauchy Reed-Solomon encoding with a pillar number (T) of five and decode threshold number of three. In this example, a first data segment is divided into twelve data blocks (D 1 -D 12 ). The coded matrix includes five rows of coded data blocks, where the first row of X 11 -X 14  corresponds to a first encoded data slice (EDS  1 _ 1 ), the second row of X 21 -X 24  corresponds to a second encoded data slice (EDS  2 _ 1 ), the third row of X 31 -X 34  corresponds to a third encoded data slice (EDS  3 _ 1 ), the fourth row of X 41 -X 44  corresponds to a fourth encoded data slice (EDS  4 _ 1 ), and the fifth row of X 51 -X 54  corresponds to a fifth encoded data slice (EDS  5 _ 1 ). Note that the second number of the EDS designation corresponds to the data segment number. In the illustrated example, the value X11=aD1+bD5+cD9, X12=aD2+bD6+cD10, . . . X53=mD3+nD7+oD11, and X54=mD4+nD8+oD12. 
         [0038]    Returning to the discussion of  FIG. 3 , the computing device also creates a slice name (SN) for each encoded data slice (EDS) in the set of encoded data slices. A typical format for a slice name  80  is shown in  FIG. 6 . As shown, the slice name (SN)  80  includes a pillar number of the encoded data slice (e.g., one of 1-T), a data segment number (e.g., one of 1-Y), a vault identifier (ID), a data object identifier (ID), and may further include revision level information of the encoded data slices. The slice name functions as at least part of a DSN address for the encoded data slice for storage and retrieval from the DSN memory  22 . 
         [0039]    As a result of encoding, the computing device  12  or  16  produces a plurality of sets of encoded data slices, which are provided with their respective slice names to the storage units for storage. As shown, the first set of encoded data slices includes EDS  1 _ 1  through EDS  5 _ 1  and the first set of slice names includes SN  1 _ 1  through SN  5 _ 1  and the last set of encoded data slices includes EDS  1 _Y through EDS  5 _Y and the last set of slice names includes SN  1 _Y through SN  5 _Y. 
         [0040]      FIG. 7  is a schematic block diagram of an example of dispersed storage error decoding of a data object that was dispersed storage error encoded and stored in the example of  FIG. 4 . In this example, the computing device  12  or  16  retrieves from the storage units at least the decode threshold number of encoded data slices per data segment. As a specific example, the computing device retrieves a read threshold number of encoded data slices. 
         [0041]    In order to recover a data segment from a decode threshold number of encoded data slices, the computing device uses a decoding function as shown in  FIG. 8 . As shown, the decoding function is essentially an inverse of the encoding function of  FIG. 4 . The coded matrix includes a decode threshold number of rows (e.g., three in this example) and the decoding matrix in an inversion of the encoding matrix that includes the corresponding rows of the coded matrix. For example, if the coded matrix includes rows  1 ,  2 , and  4 , the encoding matrix is reduced to rows  1 ,  2 , and  4 , and then inverted to produce the decoding matrix. 
         [0042]    In general, DSN memory stores a plurality of dispersed storage (DS) error encoded data. The DS error encoded data may be encoded in accordance with one or more examples described with reference to  FIGS. 3-6 , and organized (for example) in slice groupings or pillar groups. The data that is encoded into the DS error encoded data may be of any size and/or of any content. For example, the data may be one or more digital books, a copy of a company&#39;s emails, a large-scale Internet search, a video security file, one or more entertainment video files (e.g., television programs, movies, etc.), data files, and/or indexing and key information for use in dispersed storage operations. 
         [0043]    In a dispersed storage network, it is natural for some stored data (also referred to herein as “data objects”) to be of greater importance or in greater demand than other stored data. Often, the relative demand for a given piece of data is a dynamic metric that evolves and peaks (sometimes rapidly) over time. Knowing the frequency of access, relative importance, size, etc. of data may be useful when determining appropriate resources for storing the data. As described more fully below in conjunction with the novel examples of  FIGS. 9 and 10 , DSN information is utilized for dynamic resource selection and routing when storing frequently accessed data or data stored in heavily burdened storage units (e.g., for purposes of improving throughput performance and through the use of replicated data). 
         [0044]    Dispersed storage offers the ability to adaptively scale at speeds much greater than that of copy-based storage schemes, as replication of an encoded data slice can be generally be performed more quickly than replication of an entire data object. When experiencing heavy load levels, a storage unit in accordance with the present disclosure can designate one or more secondary storage units to receive a replicated data slice for temporary storage, and redirect requests for the data slice to the one or more secondary storage units. A temporarily stored replicated data slice may expire after: the rate of requests slows to a point where the originating storage unit can sufficiently address access requests, the elapsed period of time since a request was received for the slice exceeds a predetermined threshold amount of time, upon expiration of a fixed timer, or in the event that a storage unit hosting the replicated data slice becomes overburdened (in which case, the storage unit may initiate a further replication process). 
         [0045]    Referring now to  FIG. 9 , a schematic block diagram of another embodiment of a DSN performing replication of encoded data slices in accordance with the present disclosure is shown. The illustrated DSN includes the network  24  of  FIG. 1 , and the computing device  16  and network  24  of  FIG. 1 . The illustrated DSN memory includes a set of storage units  1 - n  (where, for example, n is an integer greater than or equal to three). Each storage unit may be implemented utilizing the storage unit  36  of  FIG. 1 , and each of the storage units includes a DS client module  34  (not separately illustrated), a processing module  82 , memory  84 , and a slice storage location table  86 . The storage units of a storage set may be located at a same physical location (site) or located at multiple physical locations without departing from the technology as described herein. 
         [0046]    Each of the storage units  1 - n  include a slice storage location table  86  that is used when processing slice access requests, and affiliates slice names associated with encoded data slices with relevant storage locations/storage unit identifier information. Alternatively, or in addition, the computing device  16  (or another device of the DSN) may maintain a slice storage location table  86  when it is functioning as a higher-level controller for the storage units  1 - n.    
         [0047]    In the illustrated example, the computing device  16  issues write slice requests to store slices (e.g., encoded data slices)  1 - n  in memory  84  of respective storage units  1 - n , as reflected in one or more slice storage location tables. Using storage unit  2  as an example, the processing module  82  may determine that the frequency of slice access of slice  2  compares unfavorably to a slice access threshold. The processing module may obtain the frequency of slice access based on one or more of a frequency of slice access query, a lookup, a list, an error message, a request, and a command. For example, the processing module determines that the frequency of slice access compares unfavorably to (e.g., exceeds) the slice access threshold when the frequency of slice  2  access over a specified period of time exceeds or is likely to exceed a threshold number of accesses. The processing module  82  then determines at least one secondary storage unit for storage of a replicated slice  2 , generates the replicated slice  2  and forwards it to one or more of the identified secondary storage units (e.g., storage unit  1  and storage unit n) for storage therein. As described more fully below in conjunction with  FIG. 10 , determining the at least one secondary storage unit may be based on a variety of factors, such as a performance requirement, an estimated access performance level, a storage unit location/address, etc. 
         [0048]    In addition, the processing module  82  may update (or cause an update to) one or more of the slice storage location tables  86  and  88 . For example, a slice storage location table may be updated to include an identifier associated with the at least one secondary storage unit, and affiliate a slice name associated with slice  2  with the identifier associated with the at least one secondary storage unit. Next, the processing module  82  may determine whether the frequency of slice access of (replicated) slice  2  compares favorably to a second slice access threshold. For example, the processing module determines that the frequency of slice access compares unfavorably to the second slice access threshold when the frequency of slice access is greater than the second slice access threshold. Alternatively, the determination may be made by a processing module  82  of a storage unit that stores a replicated slice  2 . The first and second slice access thresholds may or may not be the same. 
         [0049]    When the second slice access threshold compares favorably, the processing module  82  updates the relevant slice storage location table to exclude at least one of the at least one secondary storage units. Updates to the slice storage table may include disassociating the slice name associated with slice  2  with an identifier associated with at least one of the at least one secondary storage unit. The at least one secondary storage unit (either independently or at the direction of another device) may delete the replicated slice  2  after a fixed amount of time or when a time period since a last slice access is greater than a deletion time threshold. 
         [0050]      FIG. 10  is a logic diagram illustrating an example of replicating encoded data slices in accordance with the present disclosure. The method begins at step  100  where a processing module (e.g., of a storage unit) determines whether a frequency of slice access of an encoded data slice compares favorably to a slice access threshold. The processing module may obtain the frequency of slice access based on one or more of a frequency of slice access query, a lookup, a list, an error message, a request, and a command. For example, the processing module determines that the frequency of slice access compares unfavorably to (e.g., exceeds) the slice access threshold when the frequency of slice access is 500 accesses per minute and the slice access threshold is 100 accesses per minute. The method loops at step  100  when the processing module determines that the frequency of access compares favorably to the slice access threshold. The method continues to step  102  when the processing module determines that the frequency of access compares unfavorably to the slice access threshold. 
         [0051]    At step  102 , the processing module determines at least one secondary storage unit. The determination may be based on one or more of a current access performance level, a performance requirement, an estimated access performance level, a request pattern, a candidate secondary storage unit list, a storage unit location, a storage unit performance level, and a storage unit Internet protocol (IP) address. For example, the processing module determines the at least one secondary storage unit to include a West Coast storage unit when the request pattern includes West Coast slice access requests and a storage unit performance level associated with the West Coast storage unit compares favorably to an access latency performance requirement. 
         [0052]    The method continues at step  104  where the processing module generates a replicated encoded data slice of the encoded data slice. Generation of the replicated data slice may include one or more of immediately retrieving the encoded data slice, retrieving the encoded data slice when a dispersed storage network (DSN) activity level compares favorably to an activity level threshold, rebuilding the encoded data slice (e.g., from related encoded data slices stored in other storage units), and forming the replicated encoded data slice from the encoded data slice such that the replicated encoded data slice is substantially the same as the encoded data slice. 
         [0053]    The method continues at step  106  where the processing module sends the replicated encoded data slice to the identified at least one secondary storage unit for storage therein. Alternatively, or in addition to, the processing module determines whether the replicated encoded data slice is already stored in the at least one secondary storage unit, and sends the replicated encoded data slice to the at least one secondary storage unit when the replicated encoded data slice is not already stored therein. The method continues at step  108  where the processing module updates a slice storage location table (e.g., a table maintained by a higher-level controller and/or one or more storage units) to include an identifier associated with the at least one secondary storage unit. Updating the slice storage location table may include affiliating a slice name associated with the encoded data slice with the identifier associated with the at least one secondary storage unit. 
         [0054]    The method continues at step  110  where the processing module determines whether the frequency of slice access of the encoded data slice compares favorably to a second slice access threshold. For example, the processing module determines that the frequency of slice access compares unfavorably to the second slice access threshold when the frequency of slice access is greater than the second slice access threshold. The method loops at step  110  when the processing module determines that the frequency of slice access compares unfavorably to the second slice access threshold. The method continues to step  112  when the processing module determines that the frequency of slice access compares favorably to the second slice access threshold. 
         [0055]    At step  112 , the processing module updates the slice storage location table to exclude at least one of the at least one secondary storage units. Updates to the slice storage table may include disassociating the slice name associated with the encoded data slice with an identifier associated with at least one of the at least one secondary storage unit. The at least one secondary storage unit (either independently or at the direction of another device) may delete the replicated encoded data slice, for example, after a fixed amount of time or when a time period since a last replicated encoded data slice access is greater than a deletion time threshold. The method repeats back to step  100 . 
         [0056]    The methods described above in conjunction with the storage units can alternatively be performed by other modules (e.g., DS client modules  34 ) of a dispersed storage network or by other devices (e.g., managing unit  20 ). Any combination of a first module, a second module, a third module, a fourth module, etc. of the computing devices and the storage units may perform the method described above. In addition, at least one memory section (e.g., a first memory section, a second memory section, a third memory section, a fourth memory section, a fifth memory section, a sixth memory section, etc. of a non-transitory computer readable storage medium) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices and/or by the storage units of the dispersed storage network (DSN), cause the one or more computing devices and/or the storage units to perform any or all of the method steps described above. 
         [0057]    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. Such relativity between items ranges from a difference of a few percent to magnitude differences. As may also be used herein, the term(s) “configured to”, “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 an example of 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 “configured to”, “operable to”, “coupled 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. 
         [0058]    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 that signal  1  has a greater magnitude than signal  2 , a favorable comparison may be achieved when the magnitude of signal  1  is greater than that of signal  2  or when the magnitude of signal  2  is less than that of signal  1 . As may be used herein, the term “compares unfavorably”, indicates that a comparison between two or more items, signals, etc., fails to provide the desired relationship. 
         [0059]    As may also be used herein, the terms “processing module”, “processing circuit”, “processor”, and/or “processing unit” 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, module, processing circuit, and/or processing unit may be, or further include, memory and/or an integrated memory element, which may be a single memory device, a plurality of memory devices, and/or embedded circuitry of another processing module, module, processing circuit, and/or processing unit. 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, module, processing circuit, and/or processing unit 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 if the processing module, module, processing circuit, and/or processing unit 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 may store, and the processing module, module, processing circuit, and/or processing unit executes, hard coded and/or operational instructions corresponding to at least some of the steps and/or functions illustrated in one or more of the Figures. Such a memory device or memory element can be included in an article of manufacture. 
         [0060]    One or more embodiments have 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 claims. Further, 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. 
         [0061]    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 claims. 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. 
         [0062]    In addition, a flow diagram may include a “start” and/or “continue” indication. The “start” and “continue” indications reflect that the steps presented can optionally be incorporated in or otherwise used in conjunction with other routines. In this context, “start” indicates the beginning of the first step presented and may be preceded by other activities not specifically shown. Further, the “continue” indication reflects that the steps presented may be performed multiple times and/or may be succeeded by other activities not specifically shown. Further, while a flow diagram indicates a particular ordering of steps, other orderings are likewise possible provided that the principles of causality are maintained. 
         [0063]    The one or more embodiments are used herein to illustrate one or more aspects, one or more features, one or more concepts, and/or one or more examples. A physical embodiment of an apparatus, an article of manufacture, a machine, and/or of a process may include one or more of the aspects, features, concepts, examples, etc. described with reference to one or more of the embodiments discussed herein. Further, from Figure to Figure, the embodiments may incorporate the same or similarly named functions, steps, modules, etc. that may use the same or different reference numbers and, as such, the functions, steps, modules, etc. may be the same or similar functions, steps, modules, etc. or different ones. 
         [0064]    Unless specifically stated to the contra, signals to, from, and/or between elements in a figure of any of the figures presented herein may be analog or digital, continuous time or discrete time, and single-ended or differential. For instance, if a signal path is shown as a single-ended path, it also represents a differential signal path. Similarly, if a signal path is shown as a differential path, it also represents a single-ended signal path. While one or more particular architectures are described herein, other architectures can likewise be implemented that use one or more data buses not expressly shown, direct connectivity between elements, and/or indirect coupling between other elements as recognized by one of average skill in the art. 
         [0065]    The term “module” is used in the description of one or more of the embodiments. A module implements one or more functions via a device such as a processor or other processing device or other hardware that may include or operate in association with a memory that stores operational instructions. A module may operate independently and/or in conjunction with software and/or firmware. As also used herein, a module may contain one or more sub-modules, each of which may be one or more modules. 
         [0066]    As may further be used herein, a computer readable memory includes one or more memory elements. A memory element may be a separate memory device, multiple memory devices, or a set of memory locations within a memory device. 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. The memory device may be in a form a solid state memory, a hard drive memory, cloud memory, thumb drive, server memory, computing device memory, and/or other physical medium for storing digital information. A computer readable memory/storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
         [0067]    While particular combinations of various functions and features of the one or more embodiments have been expressly described herein, other combinations of these features and functions are likewise possible. The present disclosure is not limited by the particular examples disclosed herein and expressly incorporates these other combinations.