Patent Publication Number: US-9841925-B2

Title: Adjusting timing of storing data in a dispersed storage network

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 No. 62/031,342, entitled “DISPERSED STORAGE NETWORK STORAGE RETRY MECHANISM”, filed Jul. 31, 2014, which is hereby incorporated herein by reference in its entirety and made part of the present U.S. Utility Patent Application for all purposes. 
     The present U.S. Utility Patent Application also claims priority pursuant to 35 U.S.C. §120 as a continuation-in-part of U.S. Utility application Ser. No. 14/707,943, entitled “ACCESSING A DISPERSED STORAGE NETWORK”, filed May 8, 2015, which claims priority pursuant to 35 U.S.C. §119(e) to U.S. Provisional Application No. 62/019,074, entitled “UTILIZING A DECENTRALIZED AGREEMENT PROTOCOL IN A DISPERSED STORAGE NETWORK”, filed Jun. 30, 2014, both 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 
     Not Applicable 
     INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC 
     Not Applicable 
     BACKGROUND OF THE INVENTION 
     Technical Field of the Invention 
     This invention relates generally to computer networks and more particularly to dispersed storage of data and distributed task processing of data. 
     Description of Related Art 
     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. 
     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. 
     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 an Internet storage system. The Internet 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. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S) 
         FIG. 1  is a schematic block diagram of an embodiment of a distributed computing system in accordance with the present invention; 
         FIG. 2  is a schematic block diagram of an embodiment of a computing core in accordance with the present invention; 
         FIG. 3  is a diagram of an example of a distributed storage and task processing in accordance with the present invention; 
         FIG. 4  is a schematic block diagram of an embodiment of an outbound distributed storage and/or task (DST) processing in accordance with the present invention; 
         FIG. 5  is a logic diagram of an example of a method for outbound DST processing in accordance with the present invention; 
         FIG. 6  is a schematic block diagram of an embodiment of a dispersed error encoding in accordance with the present invention; 
         FIG. 7  is a diagram of an example of a segment processing of the dispersed error encoding in accordance with the present invention; 
         FIG. 8  is a diagram of an example of error encoding and slicing processing of the dispersed error encoding in accordance with the present invention; 
         FIG. 9  is a diagram of an example of grouping selection processing of the outbound DST processing in accordance with the present invention; 
         FIG. 10  is a diagram of an example of converting data into slice groups in accordance with the present invention; 
         FIG. 11  is a schematic block diagram of an embodiment of a DST execution unit in accordance with the present invention; 
         FIG. 12  is a schematic block diagram of an example of operation of a DST execution unit in accordance with the present invention; 
         FIG. 13  is a schematic block diagram of an embodiment of an inbound distributed storage and/or task (DST) processing in accordance with the present invention; 
         FIG. 14  is a logic diagram of an example of a method for inbound DST processing in accordance with the present invention; 
         FIG. 15  is a diagram of an example of de-grouping selection processing of the inbound DST processing in accordance with the present invention; 
         FIG. 16  is a schematic block diagram of an embodiment of a dispersed error decoding in accordance with the present invention; 
         FIG. 17  is a diagram of an example of de-slicing and error decoding processing of the dispersed error decoding in accordance with the present invention; 
         FIG. 18  is a diagram of an example of a de-segment processing of the dispersed error decoding in accordance with the present invention; 
         FIG. 19  is a diagram of an example of converting slice groups into data in accordance with the present invention; 
         FIG. 20  is a diagram of an example of a distributed storage within the distributed computing system in accordance with the present invention; 
         FIG. 21  is a schematic block diagram of an example of operation of outbound distributed storage and/or task (DST) processing for storing data in accordance with the present invention; 
         FIG. 22  is a schematic block diagram of an example of a dispersed error encoding for the example of  FIG. 21  in accordance with the present invention; 
         FIG. 23  is a diagram of an example of converting data into pillar slice groups for storage in accordance with the present invention; 
         FIG. 24  is a schematic block diagram of an example of a storage operation of a DST execution unit in accordance with the present invention; 
         FIG. 25  is a schematic block diagram of an example of operation of inbound distributed storage and/or task (DST) processing for retrieving dispersed error encoded data in accordance with the present invention; 
         FIG. 26  is a schematic block diagram of an example of a dispersed error decoding for the example of  FIG. 25  in accordance with the present invention; 
         FIG. 27  is a schematic block diagram of an example of a distributed storage and task processing network (DSTN) module storing a plurality of data and a plurality of task codes in accordance with the present invention; 
         FIG. 28  is a schematic block diagram of an example of the distributed computing system performing tasks on stored data in accordance with the present invention; 
         FIG. 29  is a schematic block diagram of an embodiment of a task distribution module facilitating the example of  FIG. 28  in accordance with the present invention; 
         FIG. 30  is a diagram of a specific example of the distributed computing system performing tasks on stored data in accordance with the present invention; 
         FIG. 31  is a schematic block diagram of an example of a distributed storage and task processing network (DSTN) module storing data and task codes for the example of  FIG. 30  in accordance with the present invention; 
         FIG. 32  is a diagram of an example of DST allocation information for the example of  FIG. 30  in accordance with the present invention; 
         FIGS. 33-38  are schematic block diagrams of the DSTN module performing the example of  FIG. 30  in accordance with the present invention; 
         FIG. 39  is a diagram of an example of combining result information into final results for the example of  FIG. 30  in accordance with the present invention; 
         FIG. 40A  is a schematic block diagram of an embodiment of a decentralized agreement module in accordance with the present invention; 
         FIG. 40B  is a flowchart illustrating an example of selecting the resource in accordance with the present invention; 
         FIG. 40C  is a schematic block diagram of an embodiment of a dispersed storage network (DSN) in accordance with the present invention; 
         FIG. 40D  is a flowchart illustrating an example of accessing a dispersed storage network (DSN) memory in accordance with the present invention; 
         FIGS. 41A and 41C  are a schematic block diagram of another embodiment of a dispersed storage network (DSN) in accordance with the present invention; 
         FIG. 41B  is a schematic block diagram of an embodiment of an access unit in accordance with the present invention; 
         FIG. 41D  is a flowchart illustrating an example of adjusting timing of storing data in accordance with the present invention; 
         FIG. 42A  is a schematic block diagram of another embodiment of a dispersed storage network (DSN) in accordance with the present invention; 
         FIG. 42B  is a flowchart illustrating an example of accessing stored data in accordance with the present invention; 
         FIG. 43A  is a schematic block diagram of another embodiment of a dispersed storage network (DSN) in accordance with the present invention; 
         FIG. 43B  is a flowchart illustrating an example of migrating data from a first storage format to a second storage format in accordance with the present invention; 
         FIG. 44A  is a schematic block diagram of another embodiment of a dispersed storage network (DSN) in accordance with the present invention; 
         FIG. 44B  is a flowchart illustrating an example of recovering data in accordance with the present invention; 
         FIG. 45A  is a schematic block diagram of another embodiment of a dispersed storage network (DSN) in accordance with the present invention; 
         FIG. 45B  is a flowchart illustrating an example of migrating data from a first storage pool tier level to a second storage pool tier level in accordance with the present invention; 
         FIG. 46A  is a schematic block diagram of another embodiment of a dispersed storage network (DSN) in accordance with the present invention; 
         FIG. 46B  is a flowchart illustrating an example of accessing data within a vault storage container of a dispersed storage network (DSN) in accordance with the present invention; 
         FIG. 47A  is a schematic block diagram of another embodiment of a dispersed storage network (DSN) in accordance with the present invention; 
         FIG. 47B  is a flowchart illustrating an example of selecting a set of memory devices in accordance with the present invention; 
         FIG. 48A  is a schematic block diagram of another embodiment of a dispersed storage network (DSN) in accordance with the present invention; 
         FIG. 48B  is a flowchart illustrating an example of updating a dispersed hierarchical index in accordance with the present invention; and 
         FIG. 49  is a flowchart illustrating an example of synchronizing a dispersed hierarchical index with stored data in accordance with the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       FIG. 1  is a schematic block diagram of an embodiment of a distributed computing system  10  that includes a user device  12  and/or a user device  14 , a distributed storage and/or task (DST) processing unit  16 , a distributed storage and/or task network (DSTN) managing unit  18 , a DST integrity processing unit  20 , and a distributed storage and/or task network (DSTN) module  22 . The components of the distributed computing system  10  are coupled via a network  24 , which 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). 
     The DSTN module  22  includes a plurality of distributed storage and/or task (DST) execution units  36  that may be located at geographically different sites (e.g., one in Chicago, one in Milwaukee, etc.). Each of the DST execution units is operable to store dispersed error encoded data and/or to execute, in a distributed manner, one or more tasks on data. 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, etc. 
     Each of the user devices  12 - 14 , the DST processing unit  16 , the DSTN managing unit  18 , and the DST integrity processing unit  20  include a computing core  26  and may 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 personal 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 personal 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. User device  12  and DST processing unit  16  are configured to include a DST client module  34 . 
     With respect to interfaces, each interface  30 ,  32 , and  33  includes software and/or 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 user device  14  and the DST processing unit  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 user device  12  and the DSTN module  22  and between the DST processing unit  16  and the DSTN module  22 . As yet another example, interface  33  supports a communication link for each of the DSTN managing unit  18  and DST integrity processing unit  20  to the network  24 . 
     The distributed computing system  10  is operable to support dispersed storage (DS) error encoded data storage and retrieval, to support distributed task processing on received data, and/or to support distributed task processing on stored data. In general and with respect to DS error encoded data storage and retrieval, the distributed computing system  10  supports three primary operations: storage management, data storage and retrieval (an example of which will be discussed with reference to  FIGS. 20-26 ), and data storage integrity verification. In accordance with these three primary functions, data can be encoded, distributedly stored in physically different locations, and subsequently retrieved in a reliable and secure manner. Such a system is tolerant of a significant number of failures (e.g., up to a failure level, which may be greater than or equal to a pillar width minus a decode threshold minus one) that may result from individual storage device failures and/or network equipment failures without loss of data and without the need for a redundant or backup copy. Further, the system allows the data to be stored for an indefinite period of time without data loss and does so in a secure manner (e.g., the system is very resistant to attempts at hacking the data). 
     The second primary function (i.e., distributed data storage and retrieval) begins and ends with a user device  12 - 14 . For instance, if a second type of user device  14  has data  40  to store in the DSTN module  22 , it sends the data  40  to the DST processing unit  16  via its interface  30 . The interface  30  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, the interface  30  may attach a user identification code (ID) to the data  40 . 
     To support storage management, the DSTN managing unit  18  performs DS management services. One such DS management service includes the DSTN managing unit  18  establishing distributed data storage parameters (e.g., vault creation, distributed storage parameters, security parameters, billing information, user profile information, etc.) for a user device  12 - 14  individually or as part of a group of user devices. For example, the DSTN managing unit  18  coordinates creation of a vault (e.g., a virtual memory block) within memory of the DSTN module  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 DSTN managing unit  18  may facilitate storage of DS error encoding parameters for each vault of a plurality of vaults by updating registry information for the distributed computing system  10 . The facilitating includes storing updated registry information in one or more of the DSTN module  22 , the user device  12 , the DST processing unit  16 , and the DST integrity processing unit  20 . 
     The DS error encoding parameters (e.g., or dispersed storage error coding parameters) include data segmenting information (e.g., how many segments data (e.g., a file, a group of files, a data block, etc.) is divided into), segment security information (e.g., per segment encryption, compression, integrity checksum, etc.), error coding information (e.g., pillar width, decode threshold, read threshold, write threshold, etc.), slicing information (e.g., the number of encoded data slices that will be created for each data segment); and slice security information (e.g., per encoded data slice encryption, compression, integrity checksum, etc.). 
     The DSTN 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 DSTN module  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. 
     The DSTN managing unit  18  creates billing information for a particular user, a user group, a vault access, public vault access, etc. For instance, the DSTN managing unit  18  tracks the number of times a user accesses a private vault and/or public vaults, which can be used to generate a per-access billing information. In another instance, the DSTN 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 a per-data-amount billing information. 
     Another DS management service includes the DSTN managing unit  18  performing network operations, network administration, and/or network maintenance. Network operations includes authenticating user data allocation 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, DST execution units, and/or DST processing units) from the distributed computing system  10 , and/or establishing authentication credentials for DST execution 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 system  10 . Network maintenance includes facilitating replacing, upgrading, repairing, and/or expanding a device and/or unit of the system  10 . 
     To support data storage integrity verification within the distributed computing system  10 , the DST integrity processing unit  20  performs rebuilding of ‘bad’ or missing encoded data slices. At a high level, the DST 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 DSTN module  22 . For retrieved encoded slices, they are checked for errors due to data corruption, outdated version, etc. If a slice includes an error, it is flagged as a ‘bad’ slice. For encoded data slices that were not received and/or not listed, they are flagged as missing slices. Bad and/or missing slices are subsequently rebuilt using other retrieved encoded data slices that are deemed to be good slices to produce rebuilt slices. The rebuilt slices are stored in memory of the DSTN module  22 . Note that the DST integrity processing unit  20  may be a separate unit as shown, it may be included in the DSTN module  22 , it may be included in the DST processing unit  16 , and/or distributed among the DST execution units  36 . 
     To support distributed task processing on received data, the distributed computing system  10  has two primary operations: DST (distributed storage and/or task processing) management and DST execution on received data (an example of which will be discussed with reference to  FIGS. 3-19 ). With respect to the storage portion of the DST management, the DSTN managing unit  18  functions as previously described. With respect to the tasking processing of the DST management, the DSTN managing unit  18  performs distributed task processing (DTP) management services. One such DTP management service includes the DSTN managing unit  18  establishing DTP parameters (e.g., user-vault affiliation information, billing information, user-task information, etc.) for a user device  12 - 14  individually or as part of a group of user devices. 
     Another DTP management service includes the DSTN managing unit  18  performing DTP network operations, network administration (which is essentially the same as described above), and/or network maintenance (which is essentially the same as described above). Network operations include, but are not limited to, authenticating user task processing requests (e.g., valid request, valid user, etc.), authenticating results and/or partial results, establishing DTP authentication credentials for user devices, adding/deleting components (e.g., user devices, DST execution units, and/or DST processing units) from the distributed computing system, and/or establishing DTP authentication credentials for DST execution units. 
     To support distributed task processing on stored data, the distributed computing system  10  has two primary operations: DST (distributed storage and/or task) management and DST execution on stored data. With respect to the DST execution on stored data, if the second type of user device  14  has a task request  38  for execution by the DSTN module  22 , it sends the task request  38  to the DST processing unit  16  via its interface  30 . An example of DST execution on stored data will be discussed in greater detail with reference to  FIGS. 27-39 . With respect to the DST management, it is substantially similar to the DST management to support distributed task processing on received data. 
       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 DSTN interface module  76 . 
     The DSTN 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 DSTN interface module  76  and/or the network interface module  70  may function as the interface  30  of the user device  14  of  FIG. 1 . Further note that the IO device interface module  62  and/or the memory interface modules may be collectively or individually referred to as IO ports. 
       FIG. 3  is a diagram of an example of the distributed computing system performing a distributed storage and task processing operation. The distributed computing system includes a DST (distributed storage and/or task) client module  34  (which may be in user device  14  and/or in DST processing unit  16  of  FIG. 1 ), a network  24 , a plurality of DST execution units  1 - n  that includes two or more DST execution units  36  of  FIG. 1  (which form at least a portion of DSTN module  22  of  FIG. 1 ), a DST managing module (not shown), and a DST integrity verification module (not shown). The DST client module  34  includes an outbound DST processing section  80  and an inbound DST processing section  82 . Each of the DST execution units  1 - n  includes a controller  86 , a processing module  84 , memory  88 , a DT (distributed task) execution module  90 , and a DST client module  34 . 
     In an example of operation, the DST client module  34  receives data  92  and one or more tasks  94  to be performed upon the data  92 . The data  92  may be of any size and of any content, where, due to the size (e.g., greater than a few Terabytes), the content (e.g., secure data, etc.), and/or task(s) (e.g., MIPS intensive), distributed processing of the task(s) on the data is desired. For example, the data  92  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 any other large amount of data (e.g., greater than a few Terabytes). 
     Within the DST client module  34 , the outbound DST processing section  80  receives the data  92  and the task(s)  94 . The outbound DST processing section  80  processes the data  92  to produce slice groupings  96 . As an example of such processing, the outbound DST processing section  80  partitions the data  92  into a plurality of data partitions. For each data partition, the outbound DST processing section  80  dispersed storage (DS) error encodes the data partition to produce encoded data slices and groups the encoded data slices into a slice grouping  96 . In addition, the outbound DST processing section  80  partitions the task  94  into partial tasks  98 , where the number of partial tasks  98  may correspond to the number of slice groupings  96 . 
     The outbound DST processing section  80  then sends, via the network  24 , the slice groupings  96  and the partial tasks  98  to the DST execution units  1 - n  of the DSTN module  22  of  FIG. 1 . For example, the outbound DST processing section  80  sends slice group  1  and partial task  1  to DST execution unit  1 . As another example, the outbound DST processing section  80  sends slice group #n and partial task #n to DST execution unit #n. 
     Each DST execution unit performs its partial task  98  upon its slice group  96  to produce partial results  102 . For example, DST execution unit #1 performs partial task #1 on slice group #1 to produce a partial result #1, for results. As a more specific example, slice group #1 corresponds to a data partition of a series of digital books and the partial task #1 corresponds to searching for specific phrases, recording where the phrase is found, and establishing a phrase count. In this more specific example, the partial result #1 includes information as to where the phrase was found and includes the phrase count. 
     Upon completion of generating their respective partial results  102 , the DST execution units send, via the network  24 , their partial results  102  to the inbound DST processing section  82  of the DST client module  34 . The inbound DST processing section  82  processes the received partial results  102  to produce a result  104 . Continuing with the specific example of the preceding paragraph, the inbound DST processing section  82  combines the phrase count from each of the DST execution units  36  to produce a total phrase count. In addition, the inbound DST processing section  82  combines the ‘where the phrase was found’ information from each of the DST execution units  36  within their respective data partitions to produce ‘where the phrase was found’ information for the series of digital books. 
     In another example of operation, the DST client module  34  requests retrieval of stored data within the memory of the DST execution units  36  (e.g., memory of the DSTN module). In this example, the task  94  is retrieve data stored in the memory of the DSTN module. Accordingly, the outbound DST processing section  80  converts the task  94  into a plurality of partial tasks  98  and sends the partial tasks  98  to the respective DST execution units  1 - n.    
     In response to the partial task  98  of retrieving stored data, a DST execution unit  36  identifies the corresponding encoded data slices  100  and retrieves them. For example, DST execution unit #1 receives partial task #1 and retrieves, in response thereto, retrieved slices #1. The DST execution units  36  send their respective retrieved slices  100  to the inbound DST processing section  82  via the network  24 . 
     The inbound DST processing section  82  converts the retrieved slices  100  into data  92 . For example, the inbound DST processing section  82  de-groups the retrieved slices  100  to produce encoded slices per data partition. The inbound DST processing section  82  then DS error decodes the encoded slices per data partition to produce data partitions. The inbound DST processing section  82  de-partitions the data partitions to recapture the data  92 . 
       FIG. 4  is a schematic block diagram of an embodiment of an outbound distributed storage and/or task (DST) processing section  80  of a DST client module  34   FIG. 1  coupled to a DSTN module  22  of a  FIG. 1  (e.g., a plurality of n DST execution units  36 ) via a network  24 . The outbound DST processing section  80  includes a data partitioning module  110 , a dispersed storage (DS) error encoding module  112 , a grouping selector module  114 , a control module  116 , and a distributed task control module  118 . 
     In an example of operation, the data partitioning module  110  partitions data  92  into a plurality of data partitions  120 . The number of partitions and the size of the partitions may be selected by the control module  116  via control  160  based on the data  92  (e.g., its size, its content, etc.), a corresponding task  94  to be performed (e.g., simple, complex, single step, multiple steps, etc.), DS encoding parameters (e.g., pillar width, decode threshold, write threshold, segment security parameters, slice security parameters, etc.), capabilities of the DST execution units  36  (e.g., processing resources, availability of processing recourses, etc.), and/or as may be inputted by a user, system administrator, or other operator (human or automated). For example, the data partitioning module  110  partitions the data  92  (e.g., 100 Terabytes) into 100,000 data segments, each being 1 Gigabyte in size. Alternatively, the data partitioning module  110  partitions the data  92  into a plurality of data segments, where some of data segments are of a different size, are of the same size, or a combination thereof. 
     The DS error encoding module  112  receives the data partitions  120  in a serial manner, a parallel manner, and/or a combination thereof. For each data partition  120 , the DS error encoding module  112  DS error encodes the data partition  120  in accordance with control information  160  from the control module  116  to produce encoded data slices  122 . The DS error encoding includes segmenting the data partition into data segments, segment security processing (e.g., encryption, compression, watermarking, integrity check (e.g., CRC), etc.), error encoding, slicing, and/or per slice security processing (e.g., encryption, compression, watermarking, integrity check (e.g., CRC), etc.). The control information  160  indicates which steps of the DS error encoding are active for a given data partition and, for active steps, indicates the parameters for the step. For example, the control information  160  indicates that the error encoding is active and includes error encoding parameters (e.g., pillar width, decode threshold, write threshold, read threshold, type of error encoding, etc.). 
     The grouping selector module  114  groups the encoded slices  122  of a data partition into a set of slice groupings  96 . The number of slice groupings corresponds to the number of DST execution units  36  identified for a particular task  94 . For example, if five DST execution units  36  are identified for the particular task  94 , the grouping selector module groups the encoded slices  122  of a data partition into five slice groupings  96 . The grouping selector module  114  outputs the slice groupings  96  to the corresponding DST execution units  36  via the network  24 . 
     The distributed task control module  118  receives the task  94  and converts the task  94  into a set of partial tasks  98 . For example, the distributed task control module  118  receives a task to find where in the data (e.g., a series of books) a phrase occurs and a total count of the phrase usage in the data. In this example, the distributed task control module  118  replicates the task  94  for each DST execution unit  36  to produce the partial tasks  98 . In another example, the distributed task control module  118  receives a task to find where in the data a first phrase occurs, where in the data a second phrase occurs, and a total count for each phrase usage in the data. In this example, the distributed task control module  118  generates a first set of partial tasks  98  for finding and counting the first phrase and a second set of partial tasks for finding and counting the second phrase. The distributed task control module  118  sends respective first and/or second partial tasks  98  to each DST execution unit  36 . 
       FIG. 5  is a logic diagram of an example of a method for outbound distributed storage and task (DST) processing that begins at step  126  where a DST client module receives data and one or more corresponding tasks. The method continues at step  128  where the DST client module determines a number of DST units to support the task for one or more data partitions. For example, the DST client module may determine the number of DST units to support the task based on the size of the data, the requested task, the content of the data, a predetermined number (e.g., user indicated, system administrator determined, etc.), available DST units, capability of the DST units, and/or any other factor regarding distributed task processing of the data. The DST client module may select the same DST units for each data partition, may select different DST units for the data partitions, or a combination thereof. 
     The method continues at step  130  where the DST client module determines processing parameters of the data based on the number of DST units selected for distributed task processing. The processing parameters include data partitioning information, DS encoding parameters, and/or slice grouping information. The data partitioning information includes a number of data partitions, size of each data partition, and/or organization of the data partitions (e.g., number of data blocks in a partition, the size of the data blocks, and arrangement of the data blocks). The DS encoding parameters include segmenting information, segment security information, error encoding information (e.g., dispersed storage error encoding function parameters including one or more of pillar width, decode threshold, write threshold, read threshold, generator matrix), slicing information, and/or per slice security information. The slice grouping information includes information regarding how to arrange the encoded data slices into groups for the selected DST units. As a specific example, if the DST client module determines that five DST units are needed to support the task, then it determines that the error encoding parameters include a pillar width of five and a decode threshold of three. 
     The method continues at step  132  where the DST client module determines task partitioning information (e.g., how to partition the tasks) based on the selected DST units and data processing parameters. The data processing parameters include the processing parameters and DST unit capability information. The DST unit capability information includes the number of DT (distributed task) execution units, execution capabilities of each DT execution unit (e.g., MIPS capabilities, processing resources (e.g., quantity and capability of microprocessors, CPUs, digital signal processors, co-processor, microcontrollers, arithmetic logic circuitry, and/or any other analog and/or digital processing circuitry), availability of the processing resources, memory information (e.g., type, size, availability, etc.)), and/or any information germane to executing one or more tasks. 
     The method continues at step  134  where the DST client module processes the data in accordance with the processing parameters to produce slice groupings. The method continues at step  136  where the DST client module partitions the task based on the task partitioning information to produce a set of partial tasks. The method continues at step  138  where the DST client module sends the slice groupings and the corresponding partial tasks to respective DST units. 
       FIG. 6  is a schematic block diagram of an embodiment of the dispersed storage (DS) error encoding module  112  of an outbound distributed storage and task (DST) processing section. The DS error encoding module  112  includes a segment processing module  142 , a segment security processing module  144 , an error encoding module  146 , a slicing module  148 , and a per slice security processing module  150 . Each of these modules is coupled to a control module  116  to receive control information  160  therefrom. 
     In an example of operation, the segment processing module  142  receives a data partition  120  from a data partitioning module and receives segmenting information as the control information  160  from the control module  116 . The segmenting information indicates how the segment processing module  142  is to segment the data partition  120 . For example, the segmenting information indicates how many rows to segment the data based on a decode threshold of an error encoding scheme, indicates how many columns to segment the data into based on a number and size of data blocks within the data partition  120 , and indicates how many columns to include in a data segment  152 . The segment processing module  142  segments the data  120  into data segments  152  in accordance with the segmenting information. 
     The segment security processing module  144 , when enabled by the control module  116 , secures the data segments  152  based on segment security information received as control information  160  from the control module  116 . The segment security information includes data compression, encryption, watermarking, integrity check (e.g., cyclic redundancy check (CRC), etc.), and/or any other type of digital security. For example, when the segment security processing module  144  is enabled, it may compress a data segment  152 , encrypt the compressed data segment, and generate a CRC value for the encrypted data segment to produce a secure data segment  154 . When the segment security processing module  144  is not enabled, it passes the data segments  152  to the error encoding module  146  or is bypassed such that the data segments  152  are provided to the error encoding module  146 . 
     The error encoding module  146  encodes the secure data segments  154  in accordance with error correction encoding parameters received as control information  160  from the control module  116 . The error correction encoding parameters (e.g., also referred to as dispersed storage error coding parameters) include identifying an error correction encoding scheme (e.g., forward error correction algorithm, a Reed-Solomon based algorithm, an online coding algorithm, an information dispersal algorithm, etc.), a pillar width, a decode threshold, a read threshold, a write threshold, etc. For example, the error correction encoding parameters identify a specific error correction encoding scheme, specifies a pillar width of five, and specifies a decode threshold of three. From these parameters, the error encoding module  146  encodes a data segment  154  to produce an encoded data segment  156 . 
     The slicing module  148  slices the encoded data segment  156  in accordance with the pillar width of the error correction encoding parameters received as control information  160 . For example, if the pillar width is five, the slicing module  148  slices an encoded data segment  156  into a set of five encoded data slices. As such, for a plurality of encoded data segments  156  for a given data partition, the slicing module outputs a plurality of sets of encoded data slices  158 . 
     The per slice security processing module  150 , when enabled by the control module  116 , secures each encoded data slice  158  based on slice security information received as control information  160  from the control module  116 . The slice security information includes data compression, encryption, watermarking, integrity check (e.g., CRC, etc.), and/or any other type of digital security. For example, when the per slice security processing module  150  is enabled, it compresses an encoded data slice  158 , encrypts the compressed encoded data slice, and generates a CRC value for the encrypted encoded data slice to produce a secure encoded data slice  122 . When the per slice security processing module  150  is not enabled, it passes the encoded data slices  158  or is bypassed such that the encoded data slices  158  are the output of the DS error encoding module  112 . Note that the control module  116  may be omitted and each module stores its own parameters. 
       FIG. 7  is a diagram of an example of a segment processing of a dispersed storage (DS) error encoding module. In this example, a segment processing module  142  receives a data partition  120  that includes 45 data blocks (e.g., d 1 -d 45 ), receives segmenting information (i.e., control information  160 ) from a control module, and segments the data partition  120  in accordance with the control information  160  to produce data segments  152 . Each data block may be of the same size as other data blocks or of a different size. In addition, the size of each data block may be a few bytes to megabytes of data. As previously mentioned, the segmenting information indicates how many rows to segment the data partition into, indicates how many columns to segment the data partition into, and indicates how many columns to include in a data segment. 
     In this example, the decode threshold of the error encoding scheme is three; as such the number of rows to divide the data partition into is three. The number of columns for each row is set to 15, which is based on the number and size of data blocks. The data blocks of the data partition are arranged in rows and columns in a sequential order (i.e., the first row includes the first 15 data blocks; the second row includes the second 15 data blocks; and the third row includes the last 15 data blocks). 
     With the data blocks arranged into the desired sequential order, they are divided into data segments based on the segmenting information. In this example, the data partition is divided into 8 data segments; the first 7 include 2 columns of three rows and the last includes 1 column of three rows. Note that the first row of the 8 data segments is in sequential order of the first 15 data blocks; the second row of the 8 data segments in sequential order of the second 15 data blocks; and the third row of the 8 data segments in sequential order of the last 15 data blocks. Note that the number of data blocks, the grouping of the data blocks into segments, and size of the data blocks may vary to accommodate the desired distributed task processing function. 
       FIG. 8  is a diagram of an example of error encoding and slicing processing of the dispersed error encoding processing the data segments of  FIG. 7 . In this example, data segment  1  includes 3 rows with each row being treated as one word for encoding. As such, data segment  1  includes three words for encoding: word  1  including data blocks d 1  and d 2 , word  2  including data blocks d 16  and d 17 , and word  3  including data blocks d 31  and d 32 . Each of data segments  2 - 7  includes three words where each word includes two data blocks. Data segment  8  includes three words where each word includes a single data block (e.g., d 15 , d 30 , and d 45 ). 
     In operation, an error encoding module  146  and a slicing module  148  convert each data segment into a set of encoded data slices in accordance with error correction encoding parameters as control information  160 . More specifically, when the error correction encoding parameters indicate a unity matrix Reed-Solomon based encoding algorithm, 5 pillars, and decode threshold of 3, the first three encoded data slices of the set of encoded data slices for a data segment are substantially similar to the corresponding word of the data segment. For instance, when the unity matrix Reed-Solomon based encoding algorithm is applied to data segment  1 , the content of the first encoded data slice (DS 1 _d 1 &amp; 2 ) of the first set of encoded data slices (e.g., corresponding to data segment  1 ) is substantially similar to content of the first word (e.g., d 1  &amp; d 2 ); the content of the second encoded data slice (DS 1 _d 16 &amp; 17 ) of the first set of encoded data slices is substantially similar to content of the second word (e.g., d 16  &amp; d 17 ); and the content of the third encoded data slice (DS 1 _d 31 &amp; 32 ) of the first set of encoded data slices is substantially similar to content of the third word (e.g., d 31  &amp; d 32 ). 
     The content of the fourth and fifth encoded data slices (e.g., ES 1 _ 1  and ES 1 _ 2 ) of the first set of encoded data slices include error correction data based on the first-third words of the first data segment. With such an encoding and slicing scheme, retrieving any three of the five encoded data slices allows the data segment to be accurately reconstructed. 
     The encoding and slicing of data segments  2 - 7  yield sets of encoded data slices similar to the set of encoded data slices of data segment  1 . For instance, the content of the first encoded data slice (DS 2 _d 3 &amp; 4 ) of the second set of encoded data slices (e.g., corresponding to data segment  2 ) is substantially similar to content of the first word (e.g., d 3  &amp; d 4 ); the content of the second encoded data slice (DS 2 _d 18 &amp; 19 ) of the second set of encoded data slices is substantially similar to content of the second word (e.g., d 18  &amp; d 19 ); and the content of the third encoded data slice (DS 2 _d 33 &amp; 34 ) of the second set of encoded data slices is substantially similar to content of the third word (e.g., d 33  &amp; d 34 ). The content of the fourth and fifth encoded data slices (e.g., ES 1 _ 1  and ES 1 _ 2 ) of the second set of encoded data slices includes error correction data based on the first-third words of the second data segment. 
       FIG. 9  is a diagram of an example of grouping selection processing of an outbound distributed storage and task (DST) processing in accordance with grouping selector information as control information  160  from a control module. Encoded slices for data partition  122  are grouped in accordance with the control information  160  to produce slice groupings  96 . In this example, a grouping selector module  114  organizes the encoded data slices into five slice groupings (e.g., one for each DST execution unit of a distributed storage and task network (DSTN) module). As a specific example, the grouping selector module  114  creates a first slice grouping for a DST execution unit #1, which includes first encoded slices of each of the sets of encoded slices. As such, the first DST execution unit receives encoded data slices corresponding to data blocks  1 - 15  (e.g., encoded data slices of contiguous data). 
     The grouping selector module  114  also creates a second slice grouping for a DST execution unit #2, which includes second encoded slices of each of the sets of encoded slices. As such, the second DST execution unit receives encoded data slices corresponding to data blocks  16 - 30 . The grouping selector module  114  further creates a third slice grouping for DST execution unit #3, which includes third encoded slices of each of the sets of encoded slices. As such, the third DST execution unit receives encoded data slices corresponding to data blocks  31 - 45 . 
     The grouping selector module  114  creates a fourth slice grouping for DST execution unit #4, which includes fourth encoded slices of each of the sets of encoded slices. As such, the fourth DST execution unit receives encoded data slices corresponding to first error encoding information (e.g., encoded data slices of error coding (EC) data). The grouping selector module  114  further creates a fifth slice grouping for DST execution unit #5, which includes fifth encoded slices of each of the sets of encoded slices. As such, the fifth DST execution unit receives encoded data slices corresponding to second error encoding information. 
       FIG. 10  is a diagram of an example of converting data  92  into slice groups that expands on the preceding figures. As shown, the data  92  is partitioned in accordance with a partitioning function  164  into a plurality of data partitions ( 1 - x , where x is an integer greater than 4). Each data partition (or chunkset of data) is encoded and grouped into slice groupings as previously discussed by an encoding and grouping function  166 . For a given data partition, the slice groupings are sent to distributed storage and task (DST) execution units. From data partition to data partition, the ordering of the slice groupings to the DST execution units may vary. 
     For example, the slice groupings of data partition #1 is sent to the DST execution units such that the first DST execution receives first encoded data slices of each of the sets of encoded data slices, which corresponds to a first continuous data chunk of the first data partition (e.g., refer to  FIG. 9 ), a second DST execution receives second encoded data slices of each of the sets of encoded data slices, which corresponds to a second continuous data chunk of the first data partition, etc. 
     For the second data partition, the slice groupings may be sent to the DST execution units in a different order than it was done for the first data partition. For instance, the first slice grouping of the second data partition (e.g., slice group  2 _ 1 ) is sent to the second DST execution unit; the second slice grouping of the second data partition (e.g., slice group  2 _ 2 ) is sent to the third DST execution unit; the third slice grouping of the second data partition (e.g., slice group  2 _ 3 ) is sent to the fourth DST execution unit; the fourth slice grouping of the second data partition (e.g., slice group  2 _ 4 , which includes first error coding information) is sent to the fifth DST execution unit; and the fifth slice grouping of the second data partition (e.g., slice group  2 _ 5 , which includes second error coding information) is sent to the first DST execution unit. 
     The pattern of sending the slice groupings to the set of DST execution units may vary in a predicted pattern, a random pattern, and/or a combination thereof from data partition to data partition. In addition, from data partition to data partition, the set of DST execution units may change. For example, for the first data partition, DST execution units  1 - 5  may be used; for the second data partition, DST execution units  6 - 10  may be used; for the third data partition, DST execution units  3 - 7  may be used; etc. As is also shown, the task is divided into partial tasks that are sent to the DST execution units in conjunction with the slice groupings of the data partitions. 
       FIG. 11  is a schematic block diagram of an embodiment of a DST (distributed storage and/or task) execution unit that includes an interface  169 , a controller  86 , memory  88 , one or more DT (distributed task) execution modules  90 , and a DST client module  34 . The memory  88  is of sufficient size to store a significant number of encoded data slices (e.g., thousands of slices to hundreds-of-millions of slices) and may include one or more hard drives and/or one or more solid-state memory devices (e.g., flash memory, DRAM, etc.). 
     In an example of storing a slice group, the DST execution module receives a slice grouping  96  (e.g., slice group #1) via interface  169 . The slice grouping  96  includes, per partition, encoded data slices of contiguous data or encoded data slices of error coding (EC) data. For slice group #1, the DST execution module receives encoded data slices of contiguous data for partitions #1 and #x (and potentially others between 3 and x) and receives encoded data slices of EC data for partitions #2 and #3 (and potentially others between 3 and x). Examples of encoded data slices of contiguous data and encoded data slices of error coding (EC) data are discussed with reference to  FIG. 9 . The memory  88  stores the encoded data slices of slice groupings  96  in accordance with memory control information  174  it receives from the controller  86 . 
     The controller  86  (e.g., a processing module, a CPU, etc.) generates the memory control information  174  based on a partial task(s)  98  and distributed computing information (e.g., user information (e.g., user ID, distributed computing permissions, data access permission, etc.), vault information (e.g., virtual memory assigned to user, user group, temporary storage for task processing, etc.), task validation information, etc.). For example, the controller  86  interprets the partial task(s)  98  in light of the distributed computing information to determine whether a requestor is authorized to perform the task  98 , is authorized to access the data, and/or is authorized to perform the task on this particular data. When the requestor is authorized, the controller  86  determines, based on the task  98  and/or another input, whether the encoded data slices of the slice grouping  96  are to be temporarily stored or permanently stored. Based on the foregoing, the controller  86  generates the memory control information  174  to write the encoded data slices of the slice grouping  96  into the memory  88  and to indicate whether the slice grouping  96  is permanently stored or temporarily stored. 
     With the slice grouping  96  stored in the memory  88 , the controller  86  facilitates execution of the partial task(s)  98 . In an example, the controller  86  interprets the partial task  98  in light of the capabilities of the DT execution module(s)  90 . The capabilities include one or more of MIPS capabilities, processing resources (e.g., quantity and capability of microprocessors, CPUs, digital signal processors, co-processor, microcontrollers, arithmetic logic circuitry, and/or any other analog and/or digital processing circuitry), availability of the processing resources, etc. If the controller  86  determines that the DT execution module(s)  90  have sufficient capabilities, it generates task control information  176 . 
     The task control information  176  may be a generic instruction (e.g., perform the task on the stored slice grouping) or a series of operational codes. In the former instance, the DT execution module  90  includes a co-processor function specifically configured (fixed or programmed) to perform the desired task  98 . In the latter instance, the DT execution module  90  includes a general processor topology where the controller stores an algorithm corresponding to the particular task  98 . In this instance, the controller  86  provides the operational codes (e.g., assembly language, source code of a programming language, object code, etc.) of the algorithm to the DT execution module  90  for execution. 
     Depending on the nature of the task  98 , the DT execution module  90  may generate intermediate partial results  102  that are stored in the memory  88  or in a cache memory (not shown) within the DT execution module  90 . In either case, when the DT execution module  90  completes execution of the partial task  98 , it outputs one or more partial results  102 . The partial results  102  may also be stored in memory  88 . 
     If, when the controller  86  is interpreting whether capabilities of the DT execution module(s)  90  can support the partial task  98 , the controller  86  determines that the DT execution module(s)  90  cannot adequately support the task  98  (e.g., does not have the right resources, does not have sufficient available resources, available resources would be too slow, etc.), it then determines whether the partial task  98  should be fully offloaded or partially offloaded. 
     If the controller  86  determines that the partial task  98  should be fully offloaded, it generates DST control information  178  and provides it to the DST client module  34 . The DST control information  178  includes the partial task  98 , memory storage information regarding the slice grouping  96 , and distribution instructions. The distribution instructions instruct the DST client module  34  to divide the partial task  98  into sub-partial tasks  172 , to divide the slice grouping  96  into sub-slice groupings  170 , and identify other DST execution units. The DST client module  34  functions in a similar manner as the DST client module  34  of  FIGS. 3-10  to produce the sub-partial tasks  172  and the sub-slice groupings  170  in accordance with the distribution instructions. 
     The DST client module  34  receives DST feedback  168  (e.g., sub-partial results), via the interface  169 , from the DST execution units to which the task was offloaded. The DST client module  34  provides the sub-partial results to the DST execution unit, which processes the sub-partial results to produce the partial result(s)  102 . 
     If the controller  86  determines that the partial task  98  should be partially offloaded, it determines what portion of the task  98  and/or slice grouping  96  should be processed locally and what should be offloaded. For the portion that is being locally processed, the controller  86  generates task control information  176  as previously discussed. For the portion that is being offloaded, the controller  86  generates DST control information  178  as previously discussed. 
     When the DST client module  34  receives DST feedback  168  (e.g., sub-partial results) from the DST executions units to which a portion of the task was offloaded, it provides the sub-partial results to the DT execution module  90 . The DT execution module  90  processes the sub-partial results with the sub-partial results it created to produce the partial result(s)  102 . 
     The memory  88  may be further utilized to retrieve one or more of stored slices  100 , stored results  104 , partial results  102  when the DT execution module  90  stores partial results  102  and/or results  104  in the memory  88 . For example, when the partial task  98  includes a retrieval request, the controller  86  outputs the memory control  174  to the memory  88  to facilitate retrieval of slices  100  and/or results  104 . 
       FIG. 12  is a schematic block diagram of an example of operation of a distributed storage and task (DST) execution unit storing encoded data slices and executing a task thereon. To store the encoded data slices of a partition  1  of slice grouping  1 , a controller  86  generates write commands as memory control information  174  such that the encoded slices are stored in desired locations (e.g., permanent or temporary) within memory  88 . 
     Once the encoded slices are stored, the controller  86  provides task control information  176  to a distributed task (DT) execution module  90 . As a first step of executing the task in accordance with the task control information  176 , the DT execution module  90  retrieves the encoded slices from memory  88 . The DT execution module  90  then reconstructs contiguous data blocks of a data partition. As shown for this example, reconstructed contiguous data blocks of data partition  1  include data blocks  1 - 15  (e.g., d 1 -d 15 ). 
     With the contiguous data blocks reconstructed, the DT execution module  90  performs the task on the reconstructed contiguous data blocks. For example, the task may be to search the reconstructed contiguous data blocks for a particular word or phrase, identify where in the reconstructed contiguous data blocks the particular word or phrase occurred, and/or count the occurrences of the particular word or phrase on the reconstructed contiguous data blocks. The DST execution unit continues in a similar manner for the encoded data slices of other partitions in slice grouping  1 . Note that with using the unity matrix error encoding scheme previously discussed, if the encoded data slices of contiguous data are uncorrupted, the decoding of them is a relatively straightforward process of extracting the data. 
     If, however, an encoded data slice of contiguous data is corrupted (or missing), it can be rebuilt by accessing other DST execution units that are storing the other encoded data slices of the set of encoded data slices of the corrupted encoded data slice. In this instance, the DST execution unit having the corrupted encoded data slices retrieves at least three encoded data slices (of contiguous data and of error coding data) in the set from the other DST execution units (recall for this example, the pillar width is 5 and the decode threshold is 3). The DST execution unit decodes the retrieved data slices using the DS error encoding parameters to recapture the corresponding data segment. The DST execution unit then re-encodes the data segment using the DS error encoding parameters to rebuild the corrupted encoded data slice. Once the encoded data slice is rebuilt, the DST execution unit functions as previously described. 
       FIG. 13  is a schematic block diagram of an embodiment of an inbound distributed storage and/or task (DST) processing section  82  of a DST client module coupled to DST execution units of a distributed storage and task network (DSTN) module via a network  24 . The inbound DST processing section  82  includes a de-grouping module  180 , a DS (dispersed storage) error decoding module  182 , a data de-partitioning module  184 , a control module  186 , and a distributed task control module  188 . Note that the control module  186  and/or the distributed task control module  188  may be separate modules from corresponding ones of outbound DST processing section or may be the same modules. 
     In an example of operation, the DST execution units have completed execution of corresponding partial tasks on the corresponding slice groupings to produce partial results  102 . The inbound DST processing section  82  receives the partial results  102  via the distributed task control module  188 . The inbound DST processing section  82  then processes the partial results  102  to produce a final result, or results  104 . For example, if the task was to find a specific word or phrase within data, the partial results  102  indicate where in each of the prescribed portions of the data the corresponding DST execution units found the specific word or phrase. The distributed task control module  188  combines the individual partial results  102  for the corresponding portions of the data into a final result  104  for the data as a whole. 
     In another example of operation, the inbound DST processing section  82  is retrieving stored data from the DST execution units (i.e., the DSTN module). In this example, the DST execution units output encoded data slices  100  corresponding to the data retrieval requests. The de-grouping module  180  receives retrieved slices  100  and de-groups them to produce encoded data slices per data partition  122 . The DS error decoding module  182  decodes, in accordance with DS error encoding parameters, the encoded data slices per data partition  122  to produce data partitions  120 . 
     The data de-partitioning module  184  combines the data partitions  120  into the data  92 . The control module  186  controls the conversion of retrieved slices  100  into the data  92  using control signals  190  to each of the modules. For instance, the control module  186  provides de-grouping information to the de-grouping module  180 , provides the DS error encoding parameters to the DS error decoding module  182 , and provides de-partitioning information to the data de-partitioning module  184 . 
       FIG. 14  is a logic diagram of an example of a method that is executable by distributed storage and task (DST) client module regarding inbound DST processing. The method begins at step  194  where the DST client module receives partial results. The method continues at step  196  where the DST client module retrieves the task corresponding to the partial results. For example, the partial results include header information that identifies the requesting entity, which correlates to the requested task. 
     The method continues at step  198  where the DST client module determines result processing information based on the task. For example, if the task were to identify a particular word or phrase within the data, the result processing information would indicate to aggregate the partial results for the corresponding portions of the data to produce the final result. As another example, if the task were to count the occurrences of a particular word or phrase within the data, results of processing the information would indicate to add the partial results to produce the final results. The method continues at step  200  where the DST client module processes the partial results in accordance with the result processing information to produce the final result or results. 
       FIG. 15  is a diagram of an example of de-grouping selection processing of an inbound distributed storage and task (DST) processing section of a DST client module. In general, this is an inverse process of the grouping module of the outbound DST processing section of  FIG. 9 . Accordingly, for each data partition (e.g., partition #1), the de-grouping module retrieves the corresponding slice grouping from the DST execution units (EU) (e.g., DST  1 - 5 ). 
     As shown, DST execution unit #1 provides a first slice grouping, which includes the first encoded slices of each of the sets of encoded slices (e.g., encoded data slices of contiguous data of data blocks  1 - 15 ); DST execution unit #2 provides a second slice grouping, which includes the second encoded slices of each of the sets of encoded slices (e.g., encoded data slices of contiguous data of data blocks  16 - 30 ); DST execution unit #3 provides a third slice grouping, which includes the third encoded slices of each of the sets of encoded slices (e.g., encoded data slices of contiguous data of data blocks  31 - 45 ); DST execution unit #4 provides a fourth slice grouping, which includes the fourth encoded slices of each of the sets of encoded slices (e.g., first encoded data slices of error coding (EC) data); and DST execution unit #5 provides a fifth slice grouping, which includes the fifth encoded slices of each of the sets of encoded slices (e.g., first encoded data slices of error coding (EC) data). 
     The de-grouping module de-groups the slice groupings (e.g., received slices  100 ) using a de-grouping selector  180  controlled by a control signal  190  as shown in the example to produce a plurality of sets of encoded data slices (e.g., retrieved slices for a partition into sets of slices  122 ). Each set corresponding to a data segment of the data partition. 
       FIG. 16  is a schematic block diagram of an embodiment of a dispersed storage (DS) error decoding module  182  of an inbound distributed storage and task (DST) processing section. The DS error decoding module  182  includes an inverse per slice security processing module  202 , a de-slicing module  204 , an error decoding module  206 , an inverse segment security module  208 , a de-segmenting processing module  210 , and a control module  186 . 
     In an example of operation, the inverse per slice security processing module  202 , when enabled by the control module  186 , unsecures each encoded data slice  122  based on slice de-security information received as control information  190  (e.g., the compliment of the slice security information discussed with reference to  FIG. 6 ) received from the control module  186 . The slice security information includes data decompression, decryption, de-watermarking, integrity check (e.g., CRC verification, etc.), and/or any other type of digital security. For example, when the inverse per slice security processing module  202  is enabled, it verifies integrity information (e.g., a CRC value) of each encoded data slice  122 , it decrypts each verified encoded data slice, and decompresses each decrypted encoded data slice to produce slice encoded data  158 . When the inverse per slice security processing module  202  is not enabled, it passes the encoded data slices  122  as the sliced encoded data  158  or is bypassed such that the retrieved encoded data slices  122  are provided as the sliced encoded data  158 . 
     The de-slicing module  204  de-slices the sliced encoded data  158  into encoded data segments  156  in accordance with a pillar width of the error correction encoding parameters received as control information  190  from the control module  186 . For example, if the pillar width is five, the de-slicing module  204  de-slices a set of five encoded data slices into an encoded data segment  156 . The error decoding module  206  decodes the encoded data segments  156  in accordance with error correction decoding parameters received as control information  190  from the control module  186  to produce secure data segments  154 . The error correction decoding parameters include identifying an error correction encoding scheme (e.g., forward error correction algorithm, a Reed-Solomon based algorithm, an information dispersal algorithm, etc.), a pillar width, a decode threshold, a read threshold, a write threshold, etc. For example, the error correction decoding parameters identify a specific error correction encoding scheme, specify a pillar width of five, and specify a decode threshold of three. 
     The inverse segment security processing module  208 , when enabled by the control module  186 , unsecures the secured data segments  154  based on segment security information received as control information  190  from the control module  186 . The segment security information includes data decompression, decryption, de-watermarking, integrity check (e.g., CRC, etc.) verification, and/or any other type of digital security. For example, when the inverse segment security processing module  208  is enabled, it verifies integrity information (e.g., a CRC value) of each secure data segment  154 , it decrypts each verified secured data segment, and decompresses each decrypted secure data segment to produce a data segment  152 . When the inverse segment security processing module  208  is not enabled, it passes the decoded data segment  154  as the data segment  152  or is bypassed. 
     The de-segment processing module  210  receives the data segments  152  and receives de-segmenting information as control information  190  from the control module  186 . The de-segmenting information indicates how the de-segment processing module  210  is to de-segment the data segments  152  into a data partition  120 . For example, the de-segmenting information indicates how the rows and columns of data segments are to be rearranged to yield the data partition  120 . 
       FIG. 17  is a diagram of an example of de-slicing and error decoding processing of a dispersed error decoding module. A de-slicing module  204  receives at least a decode threshold number of encoded data slices  158  for each data segment in accordance with control information  190  and provides encoded data  156 . In this example, a decode threshold is three. As such, each set of encoded data slices  158  is shown to have three encoded data slices per data segment. The de-slicing module  204  may receive three encoded data slices per data segment because an associated distributed storage and task (DST) client module requested retrieving only three encoded data slices per segment or selected three of the retrieved encoded data slices per data segment. As shown, which is based on the unity matrix encoding previously discussed with reference to  FIG. 8 , an encoded data slice may be a data-based encoded data slice (e.g., DS 1 _d 1 &amp;d 2 ) or an error code based encoded data slice (e.g., ES 3 _ 1 ). 
     An error decoding module  206  decodes the encoded data  156  of each data segment in accordance with the error correction decoding parameters of control information  190  to produce secured segments  154 . In this example, data segment  1  includes 3 rows with each row being treated as one word for encoding. As such, data segment  1  includes three words: word  1  including data blocks d 1  and d 2 , word  2  including data blocks d 16  and d 17 , and word  3  including data blocks d 31  and d 32 . Each of data segments  2 - 7  includes three words where each word includes two data blocks. Data segment  8  includes three words where each word includes a single data block (e.g., d 15 , d 30 , and d 45 ). 
       FIG. 18  is a diagram of an example of de-segment processing of an inbound distributed storage and task (DST) processing. In this example, a de-segment processing module  210  receives data segments  152  (e.g.,  1 - 8 ) and rearranges the data blocks of the data segments into rows and columns in accordance with de-segmenting information of control information  190  to produce a data partition  120 . Note that the number of rows is based on the decode threshold (e.g., 3 in this specific example) and the number of columns is based on the number and size of the data blocks. 
     The de-segmenting module  210  converts the rows and columns of data blocks into the data partition  120 . Note that each data block may be of the same size as other data blocks or of a different size. In addition, the size of each data block may be a few bytes to megabytes of data. 
       FIG. 19  is a diagram of an example of converting slice groups into data  92  within an inbound distributed storage and task (DST) processing section. As shown, the data  92  is reconstructed from a plurality of data partitions ( 1 - x , where x is an integer greater than 4). Each data partition (or chunk set of data) is decoded and re-grouped using a de-grouping and decoding function  212  and a de-partition function  214  from slice groupings as previously discussed. For a given data partition, the slice groupings (e.g., at least a decode threshold per data segment of encoded data slices) are received from DST execution units. From data partition to data partition, the ordering of the slice groupings received from the DST execution units may vary as discussed with reference to  FIG. 10 . 
       FIG. 20  is a diagram of an example of a distributed storage and/or retrieval within the distributed computing system. The distributed computing system includes a plurality of distributed storage and/or task (DST) processing client modules  34  (one shown) coupled to a distributed storage and/or task processing network (DSTN) module, or multiple DSTN modules, via a network  24 . The DST client module  34  includes an outbound DST processing section  80  and an inbound DST processing section  82 . The DSTN module includes a plurality of DST execution units. Each DST execution unit includes a controller  86 , memory  88 , one or more distributed task (DT) execution modules  90 , and a DST client module  34 . 
     In an example of data storage, the DST client module  34  has data  92  that it desires to store in the DSTN module. The data  92  may be a file (e.g., video, audio, text, graphics, etc.), a data object, a data block, an update to a file, an update to a data block, etc. In this instance, the outbound DST processing module  80  converts the data  92  into encoded data slices  216  as will be further described with reference to  FIGS. 21-23 . The outbound DST processing module  80  sends, via the network  24 , to the DST execution units for storage as further described with reference to  FIG. 24 . 
     In an example of data retrieval, the DST client module  34  issues a retrieve request to the DST execution units for the desired data  92 . The retrieve request may address each DST executions units storing encoded data slices of the desired data, address a decode threshold number of DST execution units, address a read threshold number of DST execution units, or address some other number of DST execution units. In response to the request, each addressed DST execution unit retrieves its encoded data slices  100  of the desired data and sends them to the inbound DST processing section  82 , via the network  24 . 
     When, for each data segment, the inbound DST processing section  82  receives at least a decode threshold number of encoded data slices  100 , it converts the encoded data slices  100  into a data segment. The inbound DST processing section  82  aggregates the data segments to produce the retrieved data  92 . 
       FIG. 21  is a schematic block diagram of an embodiment of an outbound distributed storage and/or task (DST) processing section  80  of a DST client module coupled to a distributed storage and task network (DSTN) module (e.g., a plurality of DST execution units) via a network  24 . The outbound DST processing section  80  includes a data partitioning module  110 , a dispersed storage (DS) error encoding module  112 , a grouping selector module  114 , a control module  116 , and a distributed task control module  118 . 
     In an example of operation, the data partitioning module  110  is by-passed such that data  92  is provided directly to the DS error encoding module  112 . The control module  116  coordinates the by-passing of the data partitioning module  110  by outputting a bypass  220  message to the data partitioning module  110 . 
     The DS error encoding module  112  receives the data  92  in a serial manner, a parallel manner, and/or a combination thereof. The DS error encoding module  112  DS error encodes the data in accordance with control information  160  from the control module  116  to produce encoded data slices  218 . The DS error encoding includes segmenting the data  92  into data segments, segment security processing (e.g., encryption, compression, watermarking, integrity check (e.g., CRC, etc.)), error encoding, slicing, and/or per slice security processing (e.g., encryption, compression, watermarking, integrity check (e.g., CRC, etc.)). The control information  160  indicates which steps of the DS error encoding are active for the data  92  and, for active steps, indicates the parameters for the step. For example, the control information  160  indicates that the error encoding is active and includes error encoding parameters (e.g., pillar width, decode threshold, write threshold, read threshold, type of error encoding, etc.). 
     The grouping selector module  114  groups the encoded slices  218  of the data segments into pillars of slices  216 . The number of pillars corresponds to the pillar width of the DS error encoding parameters. In this example, the distributed task control module  118  facilitates the storage request. 
       FIG. 22  is a schematic block diagram of an example of a dispersed storage (DS) error encoding module  112  for the example of  FIG. 21 . The DS error encoding module  112  includes a segment processing module  142 , a segment security processing module  144 , an error encoding module  146 , a slicing module  148 , and a per slice security processing module  150 . Each of these modules is coupled to a control module  116  to receive control information  160  therefrom. 
     In an example of operation, the segment processing module  142  receives data  92  and receives segmenting information as control information  160  from the control module  116 . The segmenting information indicates how the segment processing module is to segment the data. For example, the segmenting information indicates the size of each data segment. The segment processing module  142  segments the data  92  into data segments  152  in accordance with the segmenting information. 
     The segment security processing module  144 , when enabled by the control module  116 , secures the data segments  152  based on segment security information received as control information  160  from the control module  116 . The segment security information includes data compression, encryption, watermarking, integrity check (e.g., CRC, etc.), and/or any other type of digital security. For example, when the segment security processing module  144  is enabled, it compresses a data segment  152 , encrypts the compressed data segment, and generates a CRC value for the encrypted data segment to produce a secure data segment. When the segment security processing module  144  is not enabled, it passes the data segments  152  to the error encoding module  146  or is bypassed such that the data segments  152  are provided to the error encoding module  146 . 
     The error encoding module  146  encodes the secure data segments in accordance with error correction encoding parameters received as control information  160  from the control module  116 . The error correction encoding parameters include identifying an error correction encoding scheme (e.g., forward error correction algorithm, a Reed-Solomon based algorithm, an information dispersal algorithm, etc.), a pillar width, a decode threshold, a read threshold, a write threshold, etc. For example, the error correction encoding parameters identify a specific error correction encoding scheme, specifies a pillar width of five, and specifies a decode threshold of three. From these parameters, the error encoding module  146  encodes a data segment to produce an encoded data segment. 
     The slicing module  148  slices the encoded data segment in accordance with a pillar width of the error correction encoding parameters. For example, if the pillar width is five, the slicing module slices an encoded data segment into a set of five encoded data slices. As such, for a plurality of data segments, the slicing module  148  outputs a plurality of sets of encoded data slices as shown within encoding and slicing function  222  as described. 
     The per slice security processing module  150 , when enabled by the control module  116 , secures each encoded data slice based on slice security information received as control information  160  from the control module  116 . The slice security information includes data compression, encryption, watermarking, integrity check (e.g., CRC, etc.), and/or any other type of digital security. For example, when the per slice security processing module  150  is enabled, it may compress an encoded data slice, encrypt the compressed encoded data slice, and generate a CRC value for the encrypted encoded data slice to produce a secure encoded data slice tweaking. When the per slice security processing module  150  is not enabled, it passes the encoded data slices or is bypassed such that the encoded data slices  218  are the output of the DS error encoding module  112 . 
       FIG. 23  is a diagram of an example of converting data  92  into pillar slice groups utilizing encoding, slicing and pillar grouping function  224  for storage in memory of a distributed storage and task network (DSTN) module. As previously discussed the data  92  is encoded and sliced into a plurality of sets of encoded data slices; one set per data segment. The grouping selector module organizes the sets of encoded data slices into pillars of data slices. In this example, the DS error encoding parameters include a pillar width of 5 and a decode threshold of 3. As such, for each data segment, 5 encoded data slices are created. 
     The grouping selector module takes the first encoded data slice of each of the sets and forms a first pillar, which may be sent to the first DST execution unit. Similarly, the grouping selector module creates the second pillar from the second slices of the sets; the third pillar from the third slices of the sets; the fourth pillar from the fourth slices of the sets; and the fifth pillar from the fifth slices of the set. 
       FIG. 24  is a schematic block diagram of an embodiment of a distributed storage and/or task (DST) execution unit that includes an interface  169 , a controller  86 , memory  88 , one or more distributed task (DT) execution modules  90 , and a DST client module  34 . A computing core  26  may be utilized to implement the one or more DT execution modules  90  and the DST client module  34 . The memory  88  is of sufficient size to store a significant number of encoded data slices (e.g., thousands of slices to hundreds-of-millions of slices) and may include one or more hard drives and/or one or more solid-state memory devices (e.g., flash memory, DRAM, etc.). 
     In an example of storing a pillar of slices  216 , the DST execution unit receives, via interface  169 , a pillar of slices  216  (e.g., pillar #1 slices). The memory  88  stores the encoded data slices  216  of the pillar of slices in accordance with memory control information  174  it receives from the controller  86 . The controller  86  (e.g., a processing module, a CPU, etc.) generates the memory control information  174  based on distributed storage information (e.g., user information (e.g., user ID, distributed storage permissions, data access permission, etc.), vault information (e.g., virtual memory assigned to user, user group, etc.), etc.). Similarly, when retrieving slices, the DST execution unit receives, via interface  169 , a slice retrieval request. The memory  88  retrieves the slice in accordance with memory control information  174  it receives from the controller  86 . The memory  88  outputs the slice  100 , via the interface  169 , to a requesting entity. 
       FIG. 25  is a schematic block diagram of an example of operation of an inbound distributed storage and/or task (DST) processing section  82  for retrieving dispersed error encoded data  92 . The inbound DST processing section  82  includes a de-grouping module  180 , a dispersed storage (DS) error decoding module  182 , a data de-partitioning module  184 , a control module  186 , and a distributed task control module  188 . Note that the control module  186  and/or the distributed task control module  188  may be separate modules from corresponding ones of an outbound DST processing section or may be the same modules. 
     In an example of operation, the inbound DST processing section  82  is retrieving stored data  92  from the DST execution units (i.e., the DSTN module). In this example, the DST execution units output encoded data slices corresponding to data retrieval requests from the distributed task control module  188 . The de-grouping module  180  receives pillars of slices  100  and de-groups them in accordance with control information  190  from the control module  186  to produce sets of encoded data slices  218 . The DS error decoding module  182  decodes, in accordance with the DS error encoding parameters received as control information  190  from the control module  186 , each set of encoded data slices  218  to produce data segments, which are aggregated into retrieved data  92 . The data de-partitioning module  184  is by-passed in this operational mode via a bypass signal  226  of control information  190  from the control module  186 . 
       FIG. 26  is a schematic block diagram of an embodiment of a dispersed storage (DS) error decoding module  182  of an inbound distributed storage and task (DST) processing section. The DS error decoding module  182  includes an inverse per slice security processing module  202 , a de-slicing module  204 , an error decoding module  206 , an inverse segment security module  208 , and a de-segmenting processing module  210 . The dispersed error decoding module  182  is operable to de-slice and decode encoded slices per data segment  218  utilizing a de-slicing and decoding function  228  to produce a plurality of data segments that are de-segmented utilizing a de-segment function  230  to recover data  92 . 
     In an example of operation, the inverse per slice security processing module  202 , when enabled by the control module  186  via control information  190 , unsecures each encoded data slice  218  based on slice de-security information (e.g., the compliment of the slice security information discussed with reference to  FIG. 6 ) received as control information  190  from the control module  186 . The slice de-security information includes data decompression, decryption, de-watermarking, integrity check (e.g., CRC verification, etc.), and/or any other type of digital security. For example, when the inverse per slice security processing module  202  is enabled, it verifies integrity information (e.g., a CRC value) of each encoded data slice  218 , it decrypts each verified encoded data slice, and decompresses each decrypted encoded data slice to produce slice encoded data. When the inverse per slice security processing module  202  is not enabled, it passes the encoded data slices  218  as the sliced encoded data or is bypassed such that the retrieved encoded data slices  218  are provided as the sliced encoded data. 
     The de-slicing module  204  de-slices the sliced encoded data into encoded data segments in accordance with a pillar width of the error correction encoding parameters received as control information  190  from a control module  186 . For example, if the pillar width is five, the de-slicing module de-slices a set of five encoded data slices into an encoded data segment. Alternatively, the encoded data segment may include just three encoded data slices (e.g., when the decode threshold is 3). 
     The error decoding module  206  decodes the encoded data segments in accordance with error correction decoding parameters received as control information  190  from the control module  186  to produce secure data segments. The error correction decoding parameters include identifying an error correction encoding scheme (e.g., forward error correction algorithm, a Reed-Solomon based algorithm, an information dispersal algorithm, etc.), a pillar width, a decode threshold, a read threshold, a write threshold, etc. For example, the error correction decoding parameters identify a specific error correction encoding scheme, specify a pillar width of five, and specify a decode threshold of three. 
     The inverse segment security processing module  208 , when enabled by the control module  186 , unsecures the secured data segments based on segment security information received as control information  190  from the control module  186 . The segment security information includes data decompression, decryption, de-watermarking, integrity check (e.g., CRC, etc.) verification, and/or any other type of digital security. For example, when the inverse segment security processing module is enabled, it verifies integrity information (e.g., a CRC value) of each secure data segment, it decrypts each verified secured data segment, and decompresses each decrypted secure data segment to produce a data segment  152 . When the inverse segment security processing module  208  is not enabled, it passes the decoded data segment  152  as the data segment or is bypassed. The de-segmenting processing module  210  aggregates the data segments  152  into the data  92  in accordance with control information  190  from the control module  186 . 
       FIG. 27  is a schematic block diagram of an example of a distributed storage and task processing network (DSTN) module that includes a plurality of distributed storage and task (DST) execution units (#1 through #n, where, for example, n is an integer greater than or equal to three). Each of the DST execution units includes a DST client module  34 , a controller  86 , one or more DT (distributed task) execution modules  90 , and memory  88 . 
     In this example, the DSTN module stores, in the memory of the DST execution units, a plurality of DS (dispersed storage) encoded data (e.g., 1 through n, where n is an integer greater than or equal to two) and stores a plurality of DS encoded task codes (e.g., 1 through k, where k is an integer greater than or equal to two). The DS encoded data may be encoded in accordance with one or more examples described with reference to  FIGS. 3-19  (e.g., organized in slice groupings) or encoded in accordance with one or more examples described with reference to  FIGS. 20-26  (e.g., organized in pillar groups). The data that is encoded into the DS 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 any other large amount of data (e.g., greater than a few Terabytes). 
     The tasks that are encoded into the DS encoded task code 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, etc. The tasks may be encoded into the DS encoded task code in accordance with one or more examples described with reference to  FIGS. 3-19  (e.g., organized in slice groupings) or encoded in accordance with one or more examples described with reference to  FIGS. 20-26  (e.g., organized in pillar groups). 
     In an example of operation, a DST client module of a user device or of a DST processing unit issues a DST request to the DSTN module. The DST request may include a request to retrieve stored data, or a portion thereof, may include a request to store data that is included with the DST request, may include a request to perform one or more tasks on stored data, may include a request to perform one or more tasks on data included with the DST request, etc. In the cases where the DST request includes a request to store data or to retrieve data, the client module and/or the DSTN module processes the request as previously discussed with reference to one or more of  FIGS. 3-19  (e.g., slice groupings) and/or  20 - 26  (e.g., pillar groupings). In the case where the DST request includes a request to perform one or more tasks on data included with the DST request, the DST client module and/or the DSTN module process the DST request as previously discussed with reference to one or more of  FIGS. 3-19 . 
     In the case where the DST request includes a request to perform one or more tasks on stored data, the DST client module and/or the DSTN module processes the DST request as will be described with reference to one or more of  FIGS. 28-39 . In general, the DST client module identifies data and one or more tasks for the DSTN module to execute upon the identified data. The DST request may be for a one-time execution of the task or for an on-going execution of the task. As an example of the latter, as a company generates daily emails, the DST request may be to daily search new emails for inappropriate content and, if found, record the content, the email sender(s), the email recipient(s), email routing information, notify human resources of the identified email, etc. 
       FIG. 28  is a schematic block diagram of an example of a distributed computing system performing tasks on stored data. In this example, two distributed storage and task (DST) client modules  1 - 2  are shown: the first may be associated with a user device and the second may be associated with a DST processing unit or a high priority user device (e.g., high priority clearance user, system administrator, etc.). Each DST client module includes a list of stored data  234  and a list of tasks codes  236 . The list of stored data  234  includes one or more entries of data identifying information, where each entry identifies data stored in the DSTN module  22 . The data identifying information (e.g., data ID) includes one or more of a data file name, a data file directory listing, DSTN addressing information of the data, a data object identifier, etc. The list of tasks  236  includes one or more entries of task code identifying information, when each entry identifies task codes stored in the DSTN module  22 . The task code identifying information (e.g., task ID) includes one or more of a task file name, a task file directory listing, DSTN addressing information of the task, another type of identifier to identify the task, etc. 
     As shown, the list of data  234  and the list of tasks  236  are each smaller in number of entries for the first DST client module than the corresponding lists of the second DST client module. This may occur because the user device associated with the first DST client module has fewer privileges in the distributed computing system than the device associated with the second DST client module. Alternatively, this may occur because the user device associated with the first DST client module serves fewer users than the device associated with the second DST client module and is restricted by the distributed computing system accordingly. As yet another alternative, this may occur through no restraints by the distributed computing system, it just occurred because the operator of the user device associated with the first DST client module has selected fewer data and/or fewer tasks than the operator of the device associated with the second DST client module. 
     In an example of operation, the first DST client module selects one or more data entries  238  and one or more tasks  240  from its respective lists (e.g., selected data ID and selected task ID). The first DST client module sends its selections to a task distribution module  232 . The task distribution module  232  may be within a stand-alone device of the distributed computing system, may be within the user device that contains the first DST client module, or may be within the DSTN module  22 . 
     Regardless of the task distribution module&#39;s location, it generates DST allocation information  242  from the selected task ID  240  and the selected data ID  238 . The DST allocation information  242  includes data partitioning information, task execution information, and/or intermediate result information. The task distribution module  232  sends the DST allocation information  242  to the DSTN module  22 . Note that one or more examples of the DST allocation information will be discussed with reference to one or more of  FIGS. 29-39 . 
     The DSTN module  22  interprets the DST allocation information  242  to identify the stored DS encoded data (e.g., DS error encoded data  2 ) and to identify the stored DS error encoded task code (e.g., DS error encoded task code  1 ). In addition, the DSTN module  22  interprets the DST allocation information  242  to determine how the data is to be partitioned and how the task is to be partitioned. The DSTN module  22  also determines whether the selected DS error encoded data  238  needs to be converted from pillar grouping to slice grouping. If so, the DSTN module  22  converts the selected DS error encoded data into slice groupings and stores the slice grouping DS error encoded data by overwriting the pillar grouping DS error encoded data or by storing it in a different location in the memory of the DSTN module  22  (i.e., does not overwrite the pillar grouping DS encoded data). 
     The DSTN module  22  partitions the data and the task as indicated in the DST allocation information  242  and sends the portions to selected DST execution units of the DSTN module  22 . Each of the selected DST execution units performs its partial task(s) on its slice groupings to produce partial results. The DSTN module  22  collects the partial results from the selected DST execution units and provides them, as result information  244 , to the task distribution module. The result information  244  may be the collected partial results, one or more final results as produced by the DSTN module  22  from processing the partial results in accordance with the DST allocation information  242 , or one or more intermediate results as produced by the DSTN module  22  from processing the partial results in accordance with the DST allocation information  242 . 
     The task distribution module  232  receives the result information  244  and provides one or more final results  104  therefrom to the first DST client module. The final result(s)  104  may be result information  244  or a result(s) of the task distribution module&#39;s processing of the result information  244 . 
     In concurrence with processing the selected task of the first DST client module, the distributed computing system may process the selected task(s) of the second DST client module on the selected data(s) of the second DST client module. Alternatively, the distributed computing system may process the second DST client module&#39;s request subsequent to, or preceding, that of the first DST client module. Regardless of the ordering and/or parallel processing of the DST client module requests, the second DST client module provides its selected data  238  and selected task  240  to a task distribution module  232 . If the task distribution module  232  is a separate device of the distributed computing system or within the DSTN module, the task distribution modules  232  coupled to the first and second DST client modules may be the same module. The task distribution module  232  processes the request of the second DST client module in a similar manner as it processed the request of the first DST client module. 
       FIG. 29  is a schematic block diagram of an embodiment of a task distribution module  232  facilitating the example of  FIG. 28 . The task distribution module  232  includes a plurality of tables it uses to generate distributed storage and task (DST) allocation information  242  for selected data and selected tasks received from a DST client module. The tables include data storage information  248 , task storage information  250 , distributed task (DT) execution module information  252 , and task   sub-task mapping information  246 . 
     The data storage information table  248  includes a data identification (ID) field  260 , a data size field  262 , an addressing information field  264 , distributed storage (DS) information  266 , and may further include other information regarding the data, how it is stored, and/or how it can be processed. For example, DS encoded data #1 has a data ID of 1, a data size of AA (e.g., a byte size of a few Terabytes or more), addressing information of Addr_ 1 _AA, and DS parameters of 3/5; SEG_ 1 ; and SLC_ 1 . In this example, the addressing information may be a virtual address corresponding to the virtual address of the first storage word (e.g., one or more bytes) of the data and information on how to calculate the other addresses, may be a range of virtual addresses for the storage words of the data, physical addresses of the first storage word or the storage words of the data, may be a list of slice names of the encoded data slices of the data, etc. The DS parameters may include identity of an error encoding scheme, decode threshold/pillar width (e.g., 3/5 for the first data entry), segment security information (e.g., SEG_ 1 ), per slice security information (e.g., SLC_ 1 ), and/or any other information regarding how the data was encoded into data slices. 
     The task storage information table  250  includes a task identification (ID) field  268 , a task size field  270 , an addressing information field  272 , distributed storage (DS) information  274 , and may further include other information regarding the task, how it is stored, and/or how it can be used to process data. For example, DS encoded task #2 has a task ID of 2, a task size of XY, addressing information of Addr_ 2 _XY, and DS parameters of 3/5; SEG_ 2 ; and SLC_ 2 . In this example, the addressing information may be a virtual address corresponding to the virtual address of the first storage word (e.g., one or more bytes) of the task and information on how to calculate the other addresses, may be a range of virtual addresses for the storage words of the task, physical addresses of the first storage word or the storage words of the task, may be a list of slices names of the encoded slices of the task code, etc. The DS parameters may include identity of an error encoding scheme, decode threshold/pillar width (e.g., 3/5 for the first data entry), segment security information (e.g., SEG_ 2 ), per slice security information (e.g., SLC_ 2 ), and/or any other information regarding how the task was encoded into encoded task slices. Note that the segment and/or the per-slice security information include a type of encryption (if enabled), a type of compression (if enabled), watermarking information (if enabled), and/or an integrity check scheme (if enabled). 
     The task   sub-task mapping information table  246  includes a task field  256  and a sub-task field  258 . The task field  256  identifies a task stored in the memory of a distributed storage and task network (DSTN) module and the corresponding sub-task fields  258  indicates whether the task includes sub-tasks and, if so, how many and if any of the sub-tasks are ordered. In this example, the task   sub-task mapping information table  246  includes an entry for each task stored in memory of the DSTN module (e.g., task  1  through task k). In particular, this example indicates that task  1  includes 7 sub-tasks; task  2  does not include sub-tasks, and task k includes r number of sub-tasks (where r is an integer greater than or equal to two). 
     The DT execution module table  252  includes a DST execution unit ID field  276 , a DT execution module ID field  278 , and a DT execution module capabilities field  280 . The DST execution unit ID field  276  includes the identity of DST units in the DSTN module. The DT execution module ID field  278  includes the identity of each DT execution unit in each DST unit. For example, DST unit  1  includes three DT executions modules (e.g.,  1 _ 1 ,  1 _ 2 , and  1 _ 3 ). The DT execution capabilities field  280  includes identity of the capabilities of the corresponding DT execution unit. For example, DT execution module  1 _ 1  includes capabilities X, where X includes one or more of MIPS capabilities, processing resources (e.g., quantity and capability of microprocessors, CPUs, digital signal processors, co-processor, microcontrollers, arithmetic logic circuitry, and/or any other analog and/or digital processing circuitry), availability of the processing resources, memory information (e.g., type, size, availability, etc.), and/or any information germane to executing one or more tasks. 
     From these tables, the task distribution module  232  generates the DST allocation information  242  to indicate where the data is stored, how to partition the data, where the task is stored, how to partition the task, which DT execution units should perform which partial task on which data partitions, where and how intermediate results are to be stored, etc. If multiple tasks are being performed on the same data or different data, the task distribution module factors such information into its generation of the DST allocation information. 
       FIG. 30  is a diagram of a specific example of a distributed computing system performing tasks on stored data as a task flow  318 . In this example, selected data  92  is data  2  and selected tasks are tasks  1 ,  2 , and  3 . Task  1  corresponds to analyzing translation of data from one language to another (e.g., human language or computer language); task  2  corresponds to finding specific words and/or phrases in the data; and task  3  corresponds to finding specific translated words and/or phrases in translated data. 
     In this example, task  1  includes 7 sub-tasks: task  1 _ 1 —identify non-words (non-ordered); task  1 _ 2 —identify unique words (non-ordered); task  1 _ 3 —translate (non-ordered); task  1 _ 4 —translate back (ordered after task  1 _ 3 ); task  1 _ 5 —compare to ID errors (ordered after task  1 - 4 ); task  1 _ 6 —determine non-word translation errors (ordered after task  1 _ 5  and  1 _ 1 ); and task  1 _ 7 —determine correct translations (ordered after 1_ 5  and  1 _ 2 ). The sub-task further indicates whether they are an ordered task (i.e., are dependent on the outcome of another task) or non-order (i.e., are independent of the outcome of another task). Task  2  does not include sub-tasks and task  3  includes two sub-tasks: task  3 _ 1  translate; and task  3 _ 2  find specific word or phrase in translated data. 
     In general, the three tasks collectively are selected to analyze data for translation accuracies, translation errors, translation anomalies, occurrence of specific words or phrases in the data, and occurrence of specific words or phrases on the translated data. Graphically, the data  92  is translated  306  into translated data  282 ; is analyzed for specific words and/or phrases  300  to produce a list of specific words and/or phrases  286 ; is analyzed for non-words  302  (e.g., not in a reference dictionary) to produce a list of non-words  290 ; and is analyzed for unique words  316  included in the data  92  (i.e., how many different words are included in the data) to produce a list of unique words  298 . Each of these tasks is independent of each other and can therefore be processed in parallel if desired. 
     The translated data  282  is analyzed (e.g., sub-task  3 _ 2 ) for specific translated words and/or phrases  304  to produce a list of specific translated words and/or phrases  288 . The translated data  282  is translated back  308  (e.g., sub-task  1 _ 4 ) into the language of the original data to produce re-translated data  284 . These two tasks are dependent on the translate task (e.g., task  1 _ 3 ) and thus must be ordered after the translation task, which may be in a pipelined ordering or a serial ordering. The re-translated data  284  is then compared  310  with the original data  92  to find words and/or phrases that did not translate (one way and/or the other) properly to produce a list of incorrectly translated words  294 . As such, the comparing task (e.g., sub-task  1 _ 5 )  310  is ordered after the translation  306  and re-translation tasks  308  (e.g., sub-tasks  1 _ 3  and  1 _ 4 ). 
     The list of words incorrectly translated  294  is compared  312  to the list of non-words  290  to identify words that were not properly translated because the words are non-words to produce a list of errors due to non-words  292 . In addition, the list of words incorrectly translated  294  is compared  314  to the list of unique words  298  to identify unique words that were properly translated to produce a list of correctly translated words  296 . The comparison may also identify unique words that were not properly translated to produce a list of unique words that were not properly translated. Note that each list of words (e.g., specific words and/or phrases, non-words, unique words, translated words and/or phrases, etc.,) may include the word and/or phrase, how many times it is used, where in the data it is used, and/or any other information requested regarding a word and/or phrase. 
       FIG. 31  is a schematic block diagram of an example of a distributed storage and task processing network (DSTN) module storing data and task codes for the example of  FIG. 30 . As shown, DS encoded data  2  is stored as encoded data slices across the memory (e.g., stored in memories  88 ) of DST execution units  1 - 5 ; the DS encoded task code  1  (of task  1 ) and DS encoded task  3  are stored as encoded task slices across the memory of DST execution units  1 - 5 ; and DS encoded task code  2  (of task  2 ) is stored as encoded task slices across the memory of DST execution units  3 - 7 . As indicated in the data storage information table and the task storage information table of  FIG. 29 , the respective data/task has DS parameters of 3/5 for their decode threshold/pillar width; hence spanning the memory of five DST execution units. 
       FIG. 32  is a diagram of an example of distributed storage and task (DST) allocation information  242  for the example of  FIG. 30 . The DST allocation information  242  includes data partitioning information  320 , task execution information  322 , and intermediate result information  324 . The data partitioning information  320  includes the data identifier (ID), the number of partitions to split the data into, address information for each data partition, and whether the DS encoded data has to be transformed from pillar grouping to slice grouping. The task execution information  322  includes tabular information having a task identification field  326 , a task ordering field  328 , a data partition field ID  330 , and a set of DT execution modules  332  to use for the distributed task processing per data partition. The intermediate result information  324  includes tabular information having a name ID field  334 , an ID of the DST execution unit assigned to process the corresponding intermediate result  336 , a scratch pad storage field  338 , and an intermediate result storage field  340 . 
     Continuing with the example of  FIG. 30 , where tasks  1 - 3  are to be distributedly performed on data  2 , the data partitioning information includes the ID of data  2 . In addition, the task distribution module determines whether the DS encoded data  2  is in the proper format for distributed computing (e.g., was stored as slice groupings). If not, the task distribution module indicates that the DS encoded data  2  format needs to be changed from the pillar grouping format to the slice grouping format, which will be done by the DSTN module. In addition, the task distribution module determines the number of partitions to divide the data into (e.g.,  2 _ 1  through  2 _ z ) and addressing information for each partition. 
     The task distribution module generates an entry in the task execution information section for each sub-task to be performed. For example, task  1 _ 1  (e.g., identify non-words on the data) has no task ordering (i.e., is independent of the results of other sub-tasks), is to be performed on data partitions  2 _ 1  through  2 _ z  by DT execution modules  1 _ 1 ,  2 _ 1 ,  3 _ 1 ,  4 _ 1 , and  5 _ 1 . For instance, DT execution modules  1 _ 1 ,  2 _ 1 ,  3 _ 1 ,  4 _ 1 , and  5 _ 1  search for non-words in data partitions  2 _ 1  through  2 _ z  to produce task  1 _ 1  intermediate results (R 1 - 1 , which is a list of non-words). Task  1 _ 2  (e.g., identify unique words) has similar task execution information as task  1 _ 1  to produce task  1 _ 2  intermediate results (R 1 - 2 , which is the list of unique words). 
     Task  1 _ 3  (e.g., translate) includes task execution information as being non-ordered (i.e., is independent), having DT execution modules  1 _ 1 ,  2 _ 1 ,  3 _ 1 ,  4 _ 1 , and  5 _ 1  translate data partitions  2 _ 1  through  2 _ 4  and having DT execution modules  1 _ 2 ,  2 _ 2 ,  3 _ 2 ,  4 _ 2 , and  5 _ 2  translate data partitions  2 _ 5  through  2 _ z  to produce task  1 _ 3  intermediate results (R 1 - 3 , which is the translated data). In this example, the data partitions are grouped, where different sets of DT execution modules perform a distributed sub-task (or task) on each data partition group, which allows for further parallel processing. 
     Task  1 _ 4  (e.g., translate back) is ordered after task  1 _ 3  and is to be executed on task  1 _ 3 &#39;s intermediate result (e.g., R 1 - 3 _ 1 ) (e.g., the translated data). DT execution modules  1 _ 1 ,  2 _ 1 ,  3 _ 1 ,  4 _ 1 , and  5 _ 1  are allocated to translate back task  1 _ 3  intermediate result partitions R 1 - 3 _ 1  through R 1 - 3 _ 4  and DT execution modules  1 _ 2 ,  2 _ 2 ,  6 _ 1 ,  7 _ 1 , and  7 _ 2  are allocated to translate back task  1 _ 3  intermediate result partitions R 1 - 3 _ 5  through R 1 - 3 _ z  to produce task  1 - 4  intermediate results (R 1 - 4 , which is the translated back data). 
     Task  1 _ 5  (e.g., compare data and translated data to identify translation errors) is ordered after task  1 _ 4  and is to be executed on task  1 _ 4 &#39;s intermediate results (R 4 - 1 ) and on the data. DT execution modules  1 _ 1 ,  2 _ 1 ,  3 _ 1 ,  4 _ 1 , and  5 _ 1  are allocated to compare the data partitions ( 2 _ 1  through  2 _ z ) with partitions of task  1 - 4  intermediate results partitions R 1 - 4 _ 1  through R 1 - 4 _ z  to produce task  1 _ 5  intermediate results (R 1 - 5 , which is the list words translated incorrectly). 
     Task  1 _ 6  (e.g., determine non-word translation errors) is ordered after tasks  1 _ 1  and  1 _ 5  and is to be executed on tasks  1 _ 1 &#39;s and  1 _ 5 &#39;s intermediate results (R 1 - 1  and R 1 - 5 ). DT execution modules  1 _ 1 ,  2 _ 1 ,  3 _ 1 ,  4 _ 1 , and  5 _ 1  are allocated to compare the partitions of task  1 _ 1  intermediate results (R 1 - 1 _ 1  through R 1 - 1 _ z ) with partitions of task  1 - 5  intermediate results partitions (R 1 - 5 _ 1  through R 1 - 5 _ z ) to produce task  1 _ 6  intermediate results (R 1 - 6 , which is the list translation errors due to non-words). 
     Task  1 _ 7  (e.g., determine words correctly translated) is ordered after tasks  1 _ 2  and  1 _ 5  and is to be executed on tasks  1 _ 2 &#39;s and  1 _ 5 &#39;s intermediate results (R 1 - 1  and R 1 - 5 ). DT execution modules  1 _ 2 ,  2 _ 2 ,  3 _ 2 ,  4 _ 2 , and  5 _ 2  are allocated to compare the partitions of task  1 _ 2  intermediate results (R 1 - 2 _ 1  through R 1 - 2 _ z ) with partitions of task  1 - 5  intermediate results partitions (R 1 - 5 _ 1  through R 1 - 5 _ z ) to produce task  1 _ 7  intermediate results (R 1 - 7 , which is the list of correctly translated words). 
     Task  2  (e.g., find specific words and/or phrases) has no task ordering (i.e., is independent of the results of other sub-tasks), is to be performed on data partitions  2 _ 1  through  2 _ z  by DT execution modules  3 _ 1 ,  4 _ 1 ,  5 _ 1 ,  6 _ 1 , and  7 _ 1 . For instance, DT execution modules  3 _ 1 ,  4 _ 1 ,  5 _ 1 ,  6 _ 1 , and  7 _ 1  search for specific words and/or phrases in data partitions  2 _ 1  through  2 _ z  to produce task  2  intermediate results (R 2 , which is a list of specific words and/or phrases). 
     Task  3 _ 2  (e.g., find specific translated words and/or phrases) is ordered after task  1 _ 3  (e.g., translate) is to be performed on partitions R 1 - 3 _ 1  through R 1 - 3 _ z  by DT execution modules  1 _ 2 ,  2 _ 2 ,  3 _ 2 ,  4 _ 2 , and  5 _ 2 . For instance, DT execution modules  1 _ 2 ,  2 _ 2 ,  3 _ 2 ,  4 _ 2 , and  5 _ 2  search for specific translated words and/or phrases in the partitions of the translated data (R 1 - 3 _ 1  through R 1 - 3 _ z ) to produce task  3 _ 2  intermediate results (R 3 - 2 , which is a list of specific translated words and/or phrases). 
     For each task, the intermediate result information indicates which DST unit is responsible for overseeing execution of the task and, if needed, processing the partial results generated by the set of allocated DT execution units. In addition, the intermediate result information indicates a scratch pad memory for the task and where the corresponding intermediate results are to be stored. For example, for intermediate result R 1 - 1  (the intermediate result of task  1 _ 1 ), DST unit  1  is responsible for overseeing execution of the task  1 _ 1  and coordinates storage of the intermediate result as encoded intermediate result slices stored in memory of DST execution units  1 - 5 . In general, the scratch pad is for storing non-DS encoded intermediate results and the intermediate result storage is for storing DS encoded intermediate results. 
       FIGS. 33-38  are schematic block diagrams of the distributed storage and task network (DSTN) module performing the example of  FIG. 30 . In  FIG. 33 , the DSTN module accesses the data  92  and partitions it into a plurality of partitions  1 - z  in accordance with distributed storage and task network (DST) allocation information. For each data partition, the DSTN identifies a set of its DT (distributed task) execution modules  90  to perform the task (e.g., identify non-words (i.e., not in a reference dictionary) within the data partition) in accordance with the DST allocation information. From data partition to data partition, the set of DT execution modules  90  may be the same, different, or a combination thereof (e.g., some data partitions use the same set while other data partitions use different sets). 
     For the first data partition, the first set of DT execution modules (e.g.,  1 _ 1 ,  2 _ 1 ,  3 _ 1 ,  4 _ 1 , and  5 _ 1  per the DST allocation information of  FIG. 32 ) executes task  1 _ 1  to produce a first partial result  102  of non-words found in the first data partition. The second set of DT execution modules (e.g.,  1 _ 1 ,  2 _ 1 ,  3 _ 1 ,  4 _ 1 , and  5 _ 1  per the DST allocation information of  FIG. 32 ) executes task  1 _ 1  to produce a second partial result  102  of non-words found in the second data partition. The sets of DT execution modules (as per the DST allocation information) perform task  1 _ 1  on the data partitions until the “z” set of DT execution modules performs task  1 _ 1  on the “zth” data partition to produce a “zth” partial result  102  of non-words found in the “zth” data partition. 
     As indicated in the DST allocation information of  FIG. 32 , DST execution unit  1  is assigned to process the first through “zth” partial results to produce the first intermediate result (R 1 - 1 ), which is a list of non-words found in the data. For instance, each set of DT execution modules  90  stores its respective partial result in the scratchpad memory of DST execution unit  1  (which is identified in the DST allocation or may be determined by DST execution unit  1 ). A processing module of DST execution  1  is engaged to aggregate the first through “zth” partial results to produce the first intermediate result (e.g., R 1 _ 1 ). The processing module stores the first intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit  1 . 
     DST execution unit  1  engages its DST client module to slice grouping based DS error encode the first intermediate result (e.g., the list of non-words). To begin the encoding, the DST client module determines whether the list of non-words is of a sufficient size to partition (e.g., greater than a Terabyte). If yes, it partitions the first intermediate result (R 1 - 1 ) into a plurality of partitions (e.g., R 1 - 1 _ 1  through R 1 - 1 _ m ). If the first intermediate result is not of sufficient size to partition, it is not partitioned. 
     For each partition of the first intermediate result, or for the first intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data  2 , which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units  1 - 5 ). 
     In  FIG. 34 , the DSTN module is performing task  1 _ 2  (e.g., find unique words) on the data  92 . To begin, the DSTN module accesses the data  92  and partitions it into a plurality of partitions  1 - z  in accordance with the DST allocation information or it may use the data partitions of task  1 _ 1  if the partitioning is the same. For each data partition, the DSTN identifies a set of its DT execution modules to perform task  1 _ 2  in accordance with the DST allocation information. From data partition to data partition, the set of DT execution modules may be the same, different, or a combination thereof. For the data partitions, the allocated set of DT execution modules executes task  1 _ 2  to produce a partial results (e.g., 1 st  through “zth”) of unique words found in the data partitions. 
     As indicated in the DST allocation information of  FIG. 32 , DST execution unit  1  is assigned to process the first through “zth” partial results  102  of task  1 _ 2  to produce the second intermediate result (R 1 - 2 ), which is a list of unique words found in the data  92 . The processing module of DST execution  1  is engaged to aggregate the first through “zth” partial results of unique words to produce the second intermediate result. The processing module stores the second intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit  1 . 
     DST execution unit  1  engages its DST client module to slice grouping based DS error encode the second intermediate result (e.g., the list of non-words). To begin the encoding, the DST client module determines whether the list of unique words is of a sufficient size to partition (e.g., greater than a Terabyte). If yes, it partitions the second intermediate result (R 1 - 2 ) into a plurality of partitions (e.g., R 1 - 2 _ 1  through R 1 - 2 _ m ). If the second intermediate result is not of sufficient size to partition, it is not partitioned. 
     For each partition of the second intermediate result, or for the second intermediate results, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data  2 , which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units  1 - 5 ). 
     In  FIG. 35 , the DSTN module is performing task  1 _ 3  (e.g., translate) on the data  92 . To begin, the DSTN module accesses the data  92  and partitions it into a plurality of partitions  1 - z  in accordance with the DST allocation information or it may use the data partitions of task  1 _ 1  if the partitioning is the same. For each data partition, the DSTN identifies a set of its DT execution modules to perform task  1 _ 3  in accordance with the DST allocation information (e.g., DT execution modules  1 _ 1 ,  2 _ 1 ,  3 _ 1 ,  4 _ 1 , and  5 _ 1  translate data partitions  2 _ 1  through  2 _ 4  and DT execution modules  1 _ 2 ,  2 _ 2 ,  3 _ 2 ,  4 _ 2 , and  5 _ 2  translate data partitions  2 _ 5  through  2 _ z ). For the data partitions, the allocated set of DT execution modules  90  executes task  1 _ 3  to produce partial results  102  (e.g., 1 st  through “zth”) of translated data. 
     As indicated in the DST allocation information of  FIG. 32 , DST execution unit  2  is assigned to process the first through “zth” partial results of task  1 _ 3  to produce the third intermediate result (R 1 - 3 ), which is translated data. The processing module of DST execution  2  is engaged to aggregate the first through “zth” partial results of translated data to produce the third intermediate result. The processing module stores the third intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit  2 . 
     DST execution unit  2  engages its DST client module to slice grouping based DS error encode the third intermediate result (e.g., translated data). To begin the encoding, the DST client module partitions the third intermediate result (R 1 - 3 ) into a plurality of partitions (e.g., R 1 - 3 _ 1  through R 1 - 3 _ y ). For each partition of the third intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data  2 , which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units  2 - 6  per the DST allocation information). 
     As is further shown in  FIG. 35 , the DSTN module is performing task  1 _ 4  (e.g., retranslate) on the translated data of the third intermediate result. To begin, the DSTN module accesses the translated data (from the scratchpad memory or from the intermediate result memory and decodes it) and partitions it into a plurality of partitions in accordance with the DST allocation information. For each partition of the third intermediate result, the DSTN identifies a set of its DT execution modules  90  to perform task  1 _ 4  in accordance with the DST allocation information (e.g., DT execution modules  1 _ 1 ,  2 _ 1 ,  3 _ 1 ,  4 _ 1 , and  5 _ 1  are allocated to translate back partitions R 1 - 3 _ 1  through R 1 - 3 _ 4  and DT execution modules  1 _ 2 ,  2 _ 2 ,  6 _ 1 ,  7 _ 1 , and  7 _ 2  are allocated to translate back partitions R 1 - 3 _ 5  through R 1 - 3 _ z ). For the partitions, the allocated set of DT execution modules executes task  1 _ 4  to produce partial results  102  (e.g., 1 st  through “zth”) of re-translated data. 
     As indicated in the DST allocation information of  FIG. 32 , DST execution unit  3  is assigned to process the first through “zth” partial results of task  1 _ 4  to produce the fourth intermediate result (R 1 - 4 ), which is retranslated data. The processing module of DST execution  3  is engaged to aggregate the first through “zth” partial results of retranslated data to produce the fourth intermediate result. The processing module stores the fourth intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit  3 . 
     DST execution unit  3  engages its DST client module to slice grouping based DS error encode the fourth intermediate result (e.g., retranslated data). To begin the encoding, the DST client module partitions the fourth intermediate result (R 1 - 4 ) into a plurality of partitions (e.g., R 1 - 4 _ 1  through R 1 - 4 _ z ). For each partition of the fourth intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data  2 , which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units  3 - 7  per the DST allocation information). 
     In  FIG. 36 , a distributed storage and task network (DSTN) module is performing task  1 _ 5  (e.g., compare) on data  92  and retranslated data of  FIG. 35 . To begin, the DSTN module accesses the data  92  and partitions it into a plurality of partitions in accordance with the DST allocation information or it may use the data partitions of task  1 _ 1  if the partitioning is the same. The DSTN module also accesses the retranslated data from the scratchpad memory, or from the intermediate result memory and decodes it, and partitions it into a plurality of partitions in accordance with the DST allocation information. The number of partitions of the retranslated data corresponds to the number of partitions of the data. 
     For each pair of partitions (e.g., data partition  1  and retranslated data partition  1 ), the DSTN identifies a set of its DT execution modules  90  to perform task  1 _ 5  in accordance with the DST allocation information (e.g., DT execution modules  1 _ 1 ,  2 _ 1 ,  3 _ 1 ,  4 _ 1 , and  5 _ 1 ). For each pair of partitions, the allocated set of DT execution modules executes task  1 _ 5  to produce partial results  102  (e.g., 1 st  through “zth”) of a list of incorrectly translated words and/or phrases. 
     As indicated in the DST allocation information of  FIG. 32 , DST execution unit  1  is assigned to process the first through “zth” partial results of task  1 _ 5  to produce the fifth intermediate result (R 1 - 5 ), which is the list of incorrectly translated words and/or phrases. In particular, the processing module of DST execution  1  is engaged to aggregate the first through “zth” partial results of the list of incorrectly translated words and/or phrases to produce the fifth intermediate result. The processing module stores the fifth intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit  1 . 
     DST execution unit  1  engages its DST client module to slice grouping based DS error encode the fifth intermediate result. To begin the encoding, the DST client module partitions the fifth intermediate result (R 1 - 5 ) into a plurality of partitions (e.g., R 1 - 5 _ 1  through R 1 - 5 _ z ). For each partition of the fifth intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data  2 , which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units  1 - 5  per the DST allocation information). 
     As is further shown in  FIG. 36 , the DSTN module is performing task  1 _ 6  (e.g., translation errors due to non-words) on the list of incorrectly translated words and/or phrases (e.g., the fifth intermediate result R 1 - 5 ) and the list of non-words (e.g., the first intermediate result R 1 - 1 ). To begin, the DSTN module accesses the lists and partitions them into a corresponding number of partitions. 
     For each pair of partitions (e.g., partition R 1 - 1 _ 1  and partition R 1 - 5 _ 1 ), the DSTN identifies a set of its DT execution modules  90  to perform task  1 _ 6  in accordance with the DST allocation information (e.g., DT execution modules  1 _ 1 ,  2 _ 1 ,  3 _ 1 ,  4 _ 1 , and  5 _ 1 ). For each pair of partitions, the allocated set of DT execution modules executes task  1 _ 6  to produce partial results  102  (e.g., 1 st  through “zth”) of a list of incorrectly translated words and/or phrases due to non-words. 
     As indicated in the DST allocation information of  FIG. 32 , DST execution unit  2  is assigned to process the first through “zth” partial results of task  1 _ 6  to produce the sixth intermediate result (R 1 - 6 ), which is the list of incorrectly translated words and/or phrases due to non-words. In particular, the processing module of DST execution  2  is engaged to aggregate the first through “zth” partial results of the list of incorrectly translated words and/or phrases due to non-words to produce the sixth intermediate result. The processing module stores the sixth intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit  2 . 
     DST execution unit  2  engages its DST client module to slice grouping based DS error encode the sixth intermediate result. To begin the encoding, the DST client module partitions the sixth intermediate result (R 1 - 6 ) into a plurality of partitions (e.g., R 1 - 6 _ 1  through R 1 - 6 _ z ). For each partition of the sixth intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data  2 , which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units  2 - 6  per the DST allocation information). 
     As is still further shown in  FIG. 36 , the DSTN module is performing task  1 _ 7  (e.g., correctly translated words and/or phrases) on the list of incorrectly translated words and/or phrases (e.g., the fifth intermediate result R 1 - 5 ) and the list of unique words (e.g., the second intermediate result R 1 - 2 ). To begin, the DSTN module accesses the lists and partitions them into a corresponding number of partitions. 
     For each pair of partitions (e.g., partition R 1 - 2 _ 1  and partition R 1 - 5 _ 1 ), the DSTN identifies a set of its DT execution modules  90  to perform task  1 _ 7  in accordance with the DST allocation information (e.g., DT execution modules  1 _ 2 ,  2 _ 2 ,  3 _ 2 ,  4 _ 2 , and  5 _ 2 ). For each pair of partitions, the allocated set of DT execution modules executes task  1 _ 7  to produce partial results  102  (e.g., 1 st  through “zth”) of a list of correctly translated words and/or phrases. 
     As indicated in the DST allocation information of  FIG. 32 , DST execution unit  3  is assigned to process the first through “zth” partial results of task  1 _ 7  to produce the seventh intermediate result (R 1 - 7 ), which is the list of correctly translated words and/or phrases. In particular, the processing module of DST execution  3  is engaged to aggregate the first through “zth” partial results of the list of correctly translated words and/or phrases to produce the seventh intermediate result. The processing module stores the seventh intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit  3 . 
     DST execution unit  3  engages its DST client module to slice grouping based DS error encode the seventh intermediate result. To begin the encoding, the DST client module partitions the seventh intermediate result (R 1 - 7 ) into a plurality of partitions (e.g., R 1 - 7 _ 1  through R 1 - 7 _ z ). For each partition of the seventh intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data  2 , which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units  3 - 7  per the DST allocation information). 
     In  FIG. 37 , the distributed storage and task network (DSTN) module is performing task  2  (e.g., find specific words and/or phrases) on the data  92 . To begin, the DSTN module accesses the data and partitions it into a plurality of partitions  1 - z  in accordance with the DST allocation information or it may use the data partitions of task  1 _ 1  if the partitioning is the same. For each data partition, the DSTN identifies a set of its DT execution modules  90  to perform task  2  in accordance with the DST allocation information. From data partition to data partition, the set of DT execution modules may be the same, different, or a combination thereof. For the data partitions, the allocated set of DT execution modules executes task  2  to produce partial results  102  (e.g., 1 st  through “zth”) of specific words and/or phrases found in the data partitions. 
     As indicated in the DST allocation information of  FIG. 32 , DST execution unit  7  is assigned to process the first through “zth” partial results of task  2  to produce task  2  intermediate result (R 2 ), which is a list of specific words and/or phrases found in the data. The processing module of DST execution  7  is engaged to aggregate the first through “zth” partial results of specific words and/or phrases to produce the task  2  intermediate result. The processing module stores the task  2  intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit  7 . 
     DST execution unit  7  engages its DST client module to slice grouping based DS error encode the task  2  intermediate result. To begin the encoding, the DST client module determines whether the list of specific words and/or phrases is of a sufficient size to partition (e.g., greater than a Terabyte). If yes, it partitions the task  2  intermediate result (R 2 ) into a plurality of partitions (e.g., R 2 _ 1  through R 2 _ m ). If the task  2  intermediate result is not of sufficient size to partition, it is not partitioned. 
     For each partition of the task  2  intermediate result, or for the task  2  intermediate results, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data  2 , which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units  1 - 4 , and  7 ). 
     In  FIG. 38 , the distributed storage and task network (DSTN) module is performing task  3  (e.g., find specific translated words and/or phrases) on the translated data (R 1 - 3 ). To begin, the DSTN module accesses the translated data (from the scratchpad memory or from the intermediate result memory and decodes it) and partitions it into a plurality of partitions in accordance with the DST allocation information. For each partition, the DSTN identifies a set of its DT execution modules to perform task  3  in accordance with the DST allocation information. From partition to partition, the set of DT execution modules may be the same, different, or a combination thereof. For the partitions, the allocated set of DT execution modules  90  executes task  3  to produce partial results  102  (e.g., 1 st  through “zth”) of specific translated words and/or phrases found in the data partitions. 
     As indicated in the DST allocation information of  FIG. 32 , DST execution unit  5  is assigned to process the first through “zth” partial results of task  3  to produce task  3  intermediate result (R 3 ), which is a list of specific translated words and/or phrases found in the translated data. In particular, the processing module of DST execution  5  is engaged to aggregate the first through “zth” partial results of specific translated words and/or phrases to produce the task  3  intermediate result. The processing module stores the task  3  intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit  7 . 
     DST execution unit  5  engages its DST client module to slice grouping based DS error encode the task  3  intermediate result. To begin the encoding, the DST client module determines whether the list of specific translated words and/or phrases is of a sufficient size to partition (e.g., greater than a Terabyte). If yes, it partitions the task  3  intermediate result (R 3 ) into a plurality of partitions (e.g., R 3 _ 1  through R 3 _ m ). If the task  3  intermediate result is not of sufficient size to partition, it is not partitioned. 
     For each partition of the task  3  intermediate result, or for the task  3  intermediate results, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data  2 , which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units  1 - 4 ,  5 , and  7 ). 
       FIG. 39  is a diagram of an example of combining result information into final results  104  for the example of  FIG. 30 . In this example, the result information includes the list of specific words and/or phrases found in the data (task  2  intermediate result), the list of specific translated words and/or phrases found in the data (task  3  intermediate result), the list of non-words found in the data (task  1  first intermediate result R 1 - 1 ), the list of unique words found in the data (task  1  second intermediate result R 1 - 2 ), the list of translation errors due to non-words (task  1  sixth intermediate result R 1 - 6 ), and the list of correctly translated words and/or phrases (task  1  seventh intermediate result R 1 - 7 ). The task distribution module provides the result information to the requesting DST client module as the results  104 . 
       FIG. 40A  is a schematic block diagram of an embodiment of a decentralized agreement module  350  that includes a set of deterministic functions  1 -N, a set of normalizing functions  1 -N, a set of scoring functions  1 -N, and a ranking function  352 . Each of the deterministic function, the normalizing function, the scoring function, and the ranking function  352 , may be implemented utilizing the processing module  84  of  FIG. 3 . The decentralized agreement module  350  may be implemented utilizing any module and/or unit of a dispersed storage network (DSN). For example, the decentralized agreement module is implemented utilizing the distributed storage and task (DST) client module  34  of  FIG. 1 . 
     The decentralized agreement module  350  functions to receive a ranked scoring information request  354  and to generate ranked scoring information  358  based on the ranked scoring information request  354  and other information. The ranked scoring information request  354  includes one or more of an asset identifier (ID)  356  of an asset associated with the request, an asset type indicator, one or more location identifiers of locations associated with the DSN, one or more corresponding location weights, and a requesting entity ID. The asset includes any portion of data associated with the DSN including one or more asset types including a data object, a data record, an encoded data slice, a data segment, a set of encoded data slices, and a plurality of sets of encoded data slices. As such, the asset ID  356  of the asset includes one or more of a data name, a data record identifier, a source name, a slice name, and a plurality of sets of slice names. 
     Each location of the DSN includes an aspect of a DSN resource. Examples of locations includes one or more of a storage unit, a memory device of the storage unit, a site, a storage pool of storage units, a pillar index associated with each encoded data slice of a set of encoded data slices generated by an information dispersal algorithm (IDA), a DST client module  34  of  FIG. 1 , a DST processing unit  16  of  FIG. 1 , a DST integrity processing unit  20  of  FIG. 1 , a DSTN managing unit  18  of  FIG. 1 , a user device  12  of  FIG. 1 , and a user device  14  of  FIG. 1 . 
     Each location is associated with a location weight based on one or more of a resource prioritization of utilization scheme and physical configuration of the DSN. The location weight includes an arbitrary bias which adjusts a proportion of selections to an associated location such that a probability that an asset will be mapped to that location is equal to the location weight divided by a sum of all location weights for all locations of comparison. For example, each storage pool of a plurality of storage pools is associated with a location weight based on storage capacity. For instance, storage pools with more storage capacity are associated with higher location weights than others. The other information may include a set of location identifiers and a set of location weights associated with the set of location identifiers. For example, the other information includes location identifiers and location weights associated with a set of memory devices of a storage unit when the requesting entity utilizes the decentralized agreement module  350  to produce ranked scoring information  358  with regards to selection of a memory device of the set of memory devices for accessing a particular encoded data slice (e.g., where the asset ID includes a slice name of the particular encoded data slice). 
     The decentralized agreement module  350  outputs substantially identical ranked scoring information for each ranked scoring information request that includes substantially identical content of the ranked scoring information request. For example, a first requesting entity issues a first ranked scoring information request to the decentralized agreement module  350  and receives first ranked scoring information. A second requesting entity issues a second ranked scoring information request to the decentralized agreement module and receives second ranked scoring information. The second ranked scoring information is substantially the same as the first ranked scoring information when the second ranked scoring information request is substantially the same as the first ranked scoring information request. 
     As such, two or more requesting entities may utilize the decentralized agreement module  350  to determine substantially identical ranked scoring information. As a specific example, the first requesting entity selects a first storage pool of a plurality of storage pools for storing a set of encoded data slices utilizing the decentralized agreement module  350  and the second requesting entity identifies the first storage pool of the plurality of storage pools for retrieving the set of encoded data slices utilizing the decentralized agreement module  350 . 
     In an example of operation, the decentralized agreement module  350  receives the ranked scoring information request  354 . Each deterministic function performs a deterministic function on a combination and/or concatenation (e.g., add, append, interleave) of the asset ID  356  of the ranked scoring information request  354  and an associated location ID of the set of location IDs to produce an interim result. The deterministic function includes at least one of a hashing function, a hash-based message authentication code function, a mask generating function, a cyclic redundancy code function, hashing module of a number of locations, consistent hashing, rendezvous hashing, and a sponge function. As a specific example, deterministic function  2  appends a location ID  2  of a storage pool  2  to a source name as the asset ID to produce a combined value and performs the mask generating function on the combined value to produce interim result  2 . 
     With a set of interim results  1 -N, each normalizing function performs a normalizing function on a corresponding interim result to produce a corresponding normalized interim result. The performing of the normalizing function includes dividing the interim result by a number of possible permutations of the output of the deterministic function to produce the normalized interim result. For example, normalizing function  2  performs the normalizing function on the interim result  2  to produce a normalized interim result  2 . 
     With a set of normalized interim results  1 -N, each scoring function performs a scoring function on a corresponding normalized interim result to produce a corresponding score. The performing of the scoring function includes dividing an associated location weight by a negative log of the normalized interim result. For example, scoring function  2  divides location weight  2  of the storage pool  2  (e.g., associated with location ID  2 ) by a negative log of the normalized interim result  2  to produce a score  2 . 
     With a set of scores  1 -N, the ranking function  352  performs a ranking function on the set of scores  1 -N to generate the ranked scoring information  358 . The ranking function includes rank ordering each score with other scores of the set of scores  1 -N, where a highest score is ranked first. As such, a location associated with the highest score may be considered a highest priority location for resource utilization (e.g., accessing, storing, retrieving, etc., the given asset of the request). Having generated the ranked scoring information  358 , the decentralized agreement module  350  outputs the ranked scoring information  358  to the requesting entity. 
       FIG. 40B  is a flowchart illustrating an example of selecting a resource. The method begins or continues at step  360  where a processing module (e.g., of a decentralized agreement module) receives a ranked scoring information request from a requesting entity with regards to a set of candidate resources. For each candidate resource, the method continues at step  362  where the processing module performs a deterministic function on a location identifier (ID) of the candidate resource and an asset ID of the ranked scoring information request to produce an interim result. As a specific example, the processing module combines the asset ID and the location ID of the candidate resource to produce a combined value and performs a hashing function on the combined value to produce the interim result. 
     For each interim result, the method continues at step  364  where the processing module performs a normalizing function on the interim result to produce a normalized interim result. As a specific example, the processing module obtains a permutation value associated with the deterministic function (e.g., maximum number of permutations of output of the deterministic function) and divides the interim result by the permutation value to produce the normalized interim result (e.g., with a value between 0 and 1). 
     For each normalized interim result, the method continues at step  366  where the processing module performs a scoring function on the normalized interim result utilizing a location weight associated with the candidate resource associated with the interim result to produce a score of a set of scores. As a specific example, the processing module divides the location weight by a negative log of the normalized interim result to produce the score. 
     The method continues at step  368  where the processing module rank orders the set of scores to produce ranked scoring information (e.g., ranking a highest value first). The method continues at step  370  where the processing module outputs the ranked scoring information to the requesting entity. The requesting entity may utilize the ranked scoring information to select one location of a plurality of locations. 
       FIG. 40C  is a schematic block diagram of an embodiment of a dispersed storage network (DSN) that includes the distributed storage and task (DST) processing unit  16  of  FIG. 1 , the network  24  of  FIG. 1 , and the distributed storage and task network (DSTN) module  22  of  FIG. 1 . Hereafter, the DSTN module  22  may be interchangeably referred to as a DSN memory. The DST processing unit  16  includes a decentralized agreement module  380  and the DST client module  34  of  FIG. 1 . The decentralized agreement module  380  be implemented utilizing the decentralized agreement module  350  of  FIG. 40A . The DSTN module  22  includes a plurality of DST execution (EX) unit pools  1 -P. Each DST execution unit pool includes one or more sites  1 -S. Each site includes one or more DST execution units  1 -N. Each DST execution unit may be associated with at least one pillar of N pillars associated with an information dispersal algorithm (IDA), where a data segment is dispersed storage error encoded using the IDA to produce one or more sets of encoded data slices, and where each set includes N encoded data slices and like encoded data slices (e.g., slice  3 &#39;s) of two or more sets of encoded data slices are included in a common pillar (e.g., pillar  3 ). Each site may not include every pillar and a given pillar may be implemented at more than one site. Each DST execution unit includes a plurality of memories  1 -M. Each DST execution unit may be implemented utilizing the DST execution unit  36  of  FIG. 1 . Hereafter, a DST execution unit may be referred to interchangeably as a storage unit and a set of DST execution units may be interchangeably referred to as a set of storage units and/or as a storage unit set. 
     The DSN functions to receive data access requests  382 , select resources of at least one DST execution unit pool for data access, utilize the selected DST execution unit pool for the data access, and issue a data access response  392  based on the data access. The selecting of the resources includes utilizing a decentralized agreement function of the decentralized agreement module  380 , where a plurality of locations are ranked against each other. The selecting may include selecting one storage pool of the plurality of storage pools, selecting DST execution units at various sites of the plurality of sites, selecting a memory of the plurality of memories for each DST execution unit, and selecting combinations of memories, DST execution units, sites, pillars, and storage pools. 
     In an example of operation, the DST client module  34  receives the data access request  382  from a requesting entity, where the data access request  382  includes at least one of a store data request, a retrieve data request, a delete data request, a data name, and a requesting entity identifier (ID). Having received the data access request  382 , the DST client module  34  determines a DSN address associated with the data access request. The DSN address includes at least one of a source name (e.g., including a vault ID and an object number associated with the data name), a data segment ID, a set of slice names, a plurality of sets of slice names. The determining includes at least one of generating (e.g., for the store data request) and retrieving (e.g., from a DSN directory, from a dispersed hierarchical index) based on the data name (e.g., for the retrieve data request). 
     Having determined the DSN address, the DST client module  34  selects a plurality of resource levels (e.g., DST EX unit pool, site, DST execution unit, pillar, memory) associated with the DSTN module  22 . The determining may be based on one or more of the data name, the requesting entity ID, a predetermination, a lookup, a DSN performance indicator, and interpreting an error message. For example, the DST client module  34  selects the DST execution unit pool as a first resource level and a set of memory devices of a plurality of memory devices as a second resource level based on a system registry lookup for a vault associated with the requesting entity. 
     Having selected the plurality resource levels, the DST client module  34 , for each resource level, issues a ranked scoring information request  384  to the decentralized agreement module  380  utilizing the DSN address as an asset ID. The decentralized agreement module  380  performs the decentralized agreement function based on the asset ID (e.g., the DSN address), identifiers of locations of the selected resource levels, and location weights of the locations to generate ranked scoring information  386 . 
     For each resource level, the DST client module  34  receives corresponding ranked scoring information  386 . Having received the ranked scoring information  386 , the DST client module  34  identifies one or more resources associated with the resource level based on the ranked scoring information  386 . For example, the DST client module  34  identifies a DST execution unit pool associated with a highest score and identifies a set of memory devices within DST execution units of the identified DST execution unit pool with a highest score. 
     Having identified the one or more resources, the DST client module  34  accesses the DSTN module  22  based on the identified one or more resources associated with each resource level. For example, the DST client module  34  issues resource access requests  388  (e.g., write slice requests when storing data, read slice requests when recovering data) to the identified DST execution unit pool, where the resource access requests  388  further identify the identified set of memory devices. Having accessed the DSTN module  22 , the DST client module  34  receives resource access responses  390  (e.g., write slice responses, read slice responses). The DST client module  34  issues the data access response  392  based on the received resource access responses  390 . For example, the DST client module  34  decodes received encoded data slices to reproduce data and generates the data access response  392  to include the reproduced data. 
       FIG. 40D  is a flowchart illustrating an example of accessing a dispersed storage network (DSN) memory. The method begins or continues at step  394  where a processing module (e.g., of a distributed storage and task (DST) client module) receives a data access request from a requesting entity. The data access request includes one or more of a storage request, a retrieval request, a requesting entity identifier, and a data identifier (ID). The method continues at step  396  where the processing module determines a DSN address associated with the data access request. For example, the processing module generates the DSN address for the storage request. As another example, the processing module performs a lookup for the retrieval request based on the data identifier. 
     The method continues at step  398  where the processing module selects a plurality of resource levels associated with the DSN memory. The selecting may be based on one or more of a predetermination, a range of weights associated with available resources, a resource performance level, and a resource performance requirement level. For each resource level, the method continues at step  400  where the processing module determines ranked scoring information. For example, the processing module issues a ranked scoring information request to a decentralized agreement module based on the DSN address and receives corresponding ranked scoring information for the resource level, where the decentralized agreement module performs a decentralized agreement protocol function on the DSN address using the associated resource identifiers and resource weights for the resource level to produce the ranked scoring information for the resource level. 
     For each resource level, the method continues at step  402  where the processing module selects one or more resources associated with the resource level based on the ranked scoring information. For example, the processing module selects a resource associated with a highest score when one resource is required. As another example, the processing module selects a plurality of resources associated with highest scores when a plurality of resources are required. 
     The method continues at step  404  where the processing module accesses the DSN memory utilizing the selected one or more resources for each of the plurality of resource levels. For example, the processing module identifies network addressing information based on the selected resources including one or more of a storage unit Internet protocol address and a memory device identifier, generates a set of encoded data slice access requests based on the data access request and the DSN address, and sends the set of encoded data slice access requests to the DSN memory utilizing the identified network addressing information. 
     The method continues at step  406  where the processing module issues a data access response to the requesting entity based on one or more resource access responses from the DSN memory. For example, the processing module issues a data storage status indicator when storing data. As another example, the processing module generates the data access response to include recovered data when retrieving data. 
       FIGS. 41A and 41C  are a schematic block diagram of another embodiment of a dispersed storage network (DSN) that includes a plurality of access units  1 -N, the network  24  of  FIG. 1 , and a distributed storage and task (DST) execution (EX) unit set  410 . Each access unit includes a decentralized agreement module  412  and a corresponding DST client module of a plurality of DST client modules  1 -N of the plurality of access units  1 -N. The decentralized agreement module  412  may be implemented utilizing the decentralized agreement module  350  of  FIG. 40A . Each DST client module may be implemented utilizing the DST client module  34  of  FIG. 1 . Hereafter, each access unit may be interchangeably referred to as a DSN unit and the plurality of access units may be interchangeably referred to as a plurality of DSN units. 
     The DST execution unit set  410  includes a set of DST execution units  1 - n . Each DST execution unit may be implemented utilizing the DST execution unit  36  of  FIG. 1 . Hereafter, each DST execution unit may be interchangeably referred to as a storage unit and the DST execution unit set  410  may be interchangeably referred to as a set of storage units and/or a DSN memory of the DSN. The DSN functions to adjust timing of storing of data in the DST execution unit set  410 , where a data object is divided into a plurality of data segments, each data segment is dispersed storage error encoded to produce a set of encoded data slices of a plurality of sets of encoded data slices, and a plurality of sets of encoded data slices are stored in the DST execution unit set  410 . 
       FIG. 41A  illustrates steps of an example of operation of the adjusting of timing of the storing of the data where access units  1 - 2  received stored data object A requests of revisions  4  and  3  respectively. Each of the DST client modules  1  and  2  dispersed storage error encodes received revisions of the data object A to produce a corresponding at least one set of encoded data slices for storage in the set of DST execution units  1 - n  and generates a corresponding range of DSN addresses (e.g., at least one set of slice names). Having produced the at least one set of encoded data slices, a DSN unit of the plurality of DSN units sends a write request to DSN memory, where the write request includes the range of DSN addresses. For example, at time=t 1 , substantially simultaneously, the DST client module  1  sends, via the network  24 , a write request A- 1  to the set of DST execution units and the DST client module  2  sends a write request A- 2  to the set of DST execution units. 
     Having sent the write request, in response to the write request the DSN unit receives an error message indicating that another DSN unit of the plurality of DSN units has current write permission to the DSN memory to the range of DSN addresses. For example, the DST client module  1  receives, via the network  24 , an error response A- 1  that includes the error message and DST client module  2  receives, via the network  24 , a success response A- 2  indicating that the access unit  2  has the current write permission to the DSN memory to the range of DSN addresses (e.g., the corresponding set of slice names). 
     Having received the error message, the DSN unit (e.g., the access unit  1 ) performs a scoring function using one or more properties of the range of DSN addresses and one or more properties of each of at least some of the plurality of DSN units to produce a scoring resultant. The one or more properties of the range of DSN addresses includes one of an individual DSN address, at least some DSN addresses in the range of DSN addresses, a source name corresponding to a data object, a set of source names corresponding to a set of data objects, an individual slice name, and a range of slice names. The one or more properties of each of the at least some of the plurality of DSN units includes at least one of a DSN unit identifier and a DSN unit weighting factor. 
     The performing the scoring function includes obtaining the one or more properties of the range of DSN addresses, obtaining the one or more properties of each of the at least some of the plurality of DSN units, and performing a decentralized agreement protocol function on the obtained one or more properties of the range of DSN addresses and the obtained one or more properties of each of the at least some of the plurality of DSN units to produce the scoring resultant. The obtaining the one or more properties of the range of DSN addresses includes selecting the range of DSN addresses associated with the error message. For example, the DST client module  1  identifies a slice name associated with the error response A- 1  as the range of DSN addresses. As another example, the DST client module  1  identifies a range of slice names associated with the error response A- 1  as the range of DSN addresses. 
     The obtaining the one or more properties of each of the at least some of the plurality of DSN units includes a variety of approaches. In a first approach, the DSN unit accesses a centralized system registry to retrieve the one or more properties of the each of the at least some of the plurality of DSN units. For example, the DST client module  1  accesses the centralized system registry and extracts DSN unit identifiers and weighting factors of the DSN units. In a second approach of the obtaining the one or more properties of each of the at least some of the plurality of DSN units, the DSN unit extracts the one or more properties of the each of the at least some of the plurality of DSN units from the error message. For example, the DST client module  1  extracts the DSN unit identifiers and weighting factors of the DSN units from the error message, when the error message indicates identities of DSN units receiving the error message (e.g., a list of contending access units). 
     In a third approach of the obtaining the one or more properties of each of the at least some of the plurality of DSN units, the DSN unit (e.g., access unit  1 ) determines an expected number of concurrent write requests for a block of DSN addresses that includes the range of DSN addresses (e.g., block is one or more ranges) and selects the expected number of DSN units from the plurality of DSN units to produce the at least some of the plurality of DSN units. The determining the expected number of concurrent write requests includes one of accessing the centralized system registry to determine the expected number (e.g., a predetermined estimated number), randomly selecting the expected number using a random number generation function (e.g., pick a number between 5 and 20), determining the expected number based on historical concurrent write data for the range of DSN addresses (e.g., the DST client module  1  accesses a stark records), and determining the expected number based on information contained in the error message (e.g., a list of contending DSN units). 
     The selecting the expected number of DSN units includes one of accessing the centralized system registry to identify the selected number of DSN units (e.g., a predetermined list of DSN units based on an identity of the DSN unit), where the selected number of DSN units includes the DSN unit, using a random selection function to identify the selected number of DSN units (e.g., every other DSN unit), determining the selected number of DSN units based on historical concurrent write data for the range of DSN addresses (e.g., the DST client module  1  accesses the historical records), and determining the selected number of DSN units based on information contained in the error message (e.g., interpreting identifiers of the contending DSN units). 
     Having obtained the one or more properties of the range of DSN addresses and the one or more properties of each of the at least some of the plurality of DSN units, the DSN unit performs the decentralized agreement protocol function on the obtained one or more properties of the range of DSN addresses and the obtained one or more properties of each of the at least some of the plurality of DSN units to produce the scoring resultant. For example, the DST client module  1  issues a ranked scoring information request  414  to the decentralized agreement module  412  where the ranked scoring information request  414  includes the obtained one or more properties of the range of DSN addresses (e.g., slice names associated with the error message) and the obtained one or more properties of each of the at least some of the plurality of DSN units (e.g., identifiers of five of the access units and weighting factors of the five access units). 
     The decentralized agreement module  412  performs the decentralized agreement protocol function based on the ranked scoring information request  414  to produce ranked scoring information  416  as the scoring resultant. The performing of the decentralized agreement protocol function is discussed in greater detail with reference to  FIG. 41B . 
     Having produced the scoring resultant, the DSN unit interprets the scoring resultant to determine a re-write requesting protocol for resending the write request to the DSN memory. The interpreting the scoring resultant to determine the re-write requesting protocol includes determining a re-write ranking of the DSN unit from the scoring resultant (e.g., ranking versus other DSN units of the contending DSN units) and further includes one of a variety of approaches to determine timing for the resending of the write request. A first approach includes the DSN unit determining, based on the re-write ranking, a time slot of a plurality of times slots for resending the write request (e.g., the access unit  1  determines that a fourth time slot of the plurality of timeslots for resending is associated with the access unit  1 ). A second approach includes the DSN unit determining, based on the re-write ranking, a wait period before resending the write request (e.g., the excess unit  1  determines that a fourth standard deviation of a wait period is associated with the access unit  1 ). When the re-write ranking is of highest ranking, a third approach includes the DSN unit substantially continually resending the write request until a write success response is received by the DSN unit (e.g., the DST client module  1  continually resends the write request A- 1  until receiving the write access response). A fourth approach includes the DSN unit determining, based on the re-write ranking, a resending interval for resending the write request until the write success response is received by the DSN unit (e.g., the DST client module  1  selects a fourth standard interval between retrying sending of the write request A- 1 ). 
       FIG. 41B  is a schematic block diagram of an embodiment of an access unit that includes the DST client module  1  of  FIG. 41A  and the decentralized agreement module  412  of  FIG. 41A . The decentralized agreement module includes a plurality of deterministic functions  1 -N, a plurality of normalizing functions  1 -N, a plurality of scoring functions  1 -N, and a ranking function  420 . Each deterministic function may be implemented utilizing the deterministic function of  FIG. 40A . Each normalizing function may be implemented utilizing the normalizing function of  FIG. 40A . Each scoring function may be implemented utilizing the scoring function of  FIG. 40A . The ranking function  420  may be implemented utilizing the ranking function  352  of  FIG. 40A . 
       FIG. 41B  illustrates further steps of the example of operation of the adjusting of timing of the storing of the data, and in particular performing of the decentralized agreement protocol function, where the DST client module  1  receives the error response A 1  and generates the ranked scoring information request  414  to include a slice name  418  the obtained one or more properties of the range of DSN addresses and access unit identifiers (e.g., identifiers of access units  1 -N when the at least some of the plurality of DSN units includes all of the DSN units) and access unit weights (e.g., weighting factors of access units  1 -N when the at least some of the plurality of DSN units includes all of the DSN units) of the obtained one or more properties of each of the at least some of the plurality of DSN units. Having generated the ranked scoring information request  414 , the DST client module  1  sends the ranked scoring information request  414  to the decentralized agreement module  412 . 
     Each of the deterministic functions performs a first function based on an identifier of one of the at least some of the plurality of DSN units and the one or more properties of the range of DSN addresses to produce an interim result. For example, the deterministic function  2  performs a deterministic function on the slice name  418  and the access unit  2  identifier to produce an interim result  2 . Each of the normalizing functions normalizes a corresponding interim result to produce a normalized result. For example, normalizing function  2  normalizes the interim result  2  to produce a normalized interim result  2 . Each scoring function performs a second function based on the normalized result and a weighting factor for the one of the at least some of the plurality of DSN units to produce a score. For example, the scoring function  2  performs a scoring function on the normalized interim result  2  using the access  2  weight to produce a score  2  of a plurality of scores (e.g.,  1 -N). The ranking function  420  ranks the scores for each of the at least some of the plurality of DSN units to produce the ranked scoring information  416  as the scoring resultant. For example, the ranking function  420  indicates that a tenth DSN unit is associated with a highest score, a sixth DSN unit is associated with a second highest score, etc. 
     The DST client module  1  interprets the ranked scoring information  416  to determine the re-write requesting protocol. Having produced the re-write requesting protocol, the DST client module  1  resends the write request A- 1  at t 4  when the ranked scoring information  416  indicates that the access unit  1  is associated with resending the write request within a fourth time interval (e.g., at t 4 ). 
       FIG. 41C  illustrates further steps of the example of operation of the adjusting of timing of the storing of the data and where, having produced the re-write requesting protocol, the DSN unit resends the write request in accordance with the re-write requesting protocol. For example, the DST client module  1  resends, via the network  24 , the write request A- 1  at t 4  to the DST execution unit set  410  when the ranked scoring information  416  indicates that the access unit  1  is associated with resending the write request within a fourth time interval (e.g., at t 4 ). 
     Alternatively, or in addition to the steps discussed above, the DSN unit may utilize a first number of DSN units as the at least some of the plurality of DSN units when performing the scoring function to produce a first scoring resultant as the scoring resultant, interpret the first scoring resultant to determine a first re-write requesting protocol for resending the write request to the DSN memory, and resend the write request in accordance with the first re-write requesting protocol. When a second error message is received in response to the resending the write request in accordance with the first re-write requesting protocol, the DSN unit utilizes a second number of DSN units as the at least some of the plurality of DSN units when performing the scoring function to produce a second scoring resultant as the scoring resultant, interprets the second scoring resultant to determine a second re-write requesting protocol for resending the write request to the DSN memory, and resends the write request in accordance with the second re-write requesting protocol 
       FIG. 41D  is a flowchart illustrating an example of adjusting timing of storing data. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with  FIGS. 1-40D, 41A -C, and also  FIG. 41D . The method begins or continues at step  430  where a processing module of a computing device (e.g., a dispersed storage network (DSN) unit) of one or more computing devices of a DSN (e.g., one or more DSN units and other computing devices) sends a write request to DSN memory, where the write request includes a range of DSN addresses (e.g., a set of slice names). 
     In response to the write request, the method continues at step  432  where the processing module receives an error message indicating that another DSN unit of the plurality of DSN units has current write permission to the DSN memory to the range of DSN addresses. The method continues at step  434  where the processing module performs a scoring function using one or more properties of the range of DSN addresses and one or more properties of each of at least some of the plurality of DSN units to produce a scoring resultant. 
     The performing the scoring function includes obtaining the one or more properties of the range of DSN addresses, obtaining the one or more properties of each of the at least some of the plurality of DSN units, and performing a decentralized agreement protocol function on the obtained one or more properties of the range of DSN addresses and the obtained one or more properties of each of the at least some of the plurality of DSN units to produce the scoring resultant. 
     The obtaining the one or more properties of each of the at least some of the plurality of DSN units includes a variety of approaches. In a first approach, the processing module accesses a centralized system registry to retrieve the one or more properties of the each of the at least some of the plurality of DSN units, where the one or more properties of the each of the at least some of the plurality of DSN units includes at least one of a DSN unit identifier and a DSN unit weighting factor. In a second approach of the obtaining the one or more properties of each of the at least some of the plurality of DSN units, the processing module extracts the one or more properties of the each of the at least some of the plurality of DSN units from the error message. 
     The obtaining the one or more properties of each of the at least some of the plurality of DSN units may further include determining an expected number of concurrent write requests for a block of DSN addresses that includes the range of DSN addresses and selecting the expected number of DSN units from the plurality of DSN units to produce the at least some of the plurality of DSN units. The determining the expected number of concurrent write requests includes one of the processing module accessing a centralized system registry to determine the expected number, the processing module randomly selecting the expected number using a random number generation function, the processing module determining the expected number based on historical concurrent write data for the range of DSN addresses, and the processing module determining the expected number based on information contained in the error message. 
     The selecting the expected number of DSN units includes one of the processing module accessing the centralized system registry to identify the selected number of DSN units, where the selected number of DSN units includes the DSN unit, the processing module using a random selection function to identify the selected number of DSN units, the processing module determining the selected number of DSN units based on historical concurrent write data for the range of DSN addresses, and the processing module determining the selected number of DSN units based on information contained in the error message. 
     The performing the decentralized agreement protocol function on the obtained one or more properties of the range of DSN addresses and the obtained one or more properties of each of the at least some of the plurality of DSN units to produce the scoring resultant includes, for each of the at least some of the plurality of DSN units, the processing module performing a first function (e.g., a deterministic function) based on an identifier of one of the at least some of the plurality of DSN units and the one or more properties of the range of DSN addresses to produce an interim result, the processing module normalizing the interim result to produce a normalized result, and the processing module performing a second function (e.g., a scoring function) based on the normalized result and a weighting factor for the one of the at least some of the plurality of DSN units to produce a score. Having produced the scores, the processing module ranks the scores for each of the at least some of the plurality of DSN units to produce the scoring resultant. 
     The method continues at step  436  where the processing module interprets the scoring resultant to determine a re-write requesting protocol for resending the write request to the DSN memory. The interpreting includes the processing module determining a re-write ranking of the DSN unit from the scoring resultant and further includes one of the processing module determining, based on the re-write ranking, a time slot of a plurality of times slots for resending the write request, the processing module determining, based on the re-write ranking, a wait period before resending the write request, when the re-write ranking is of highest ranking, the processing module substantially continually resending the write request until a write success response is received by the DSN unit, and the processing module determining, based on the re-write ranking, a resending interval for resending the write request until the write success response is received by the DSN unit. 
     The method continues at step  438  where the processing module resends the write request in accordance with the re-write requesting protocol. Alternatively, or in addition to the steps discussed above, the processing module may utilize a first number of DSN units as the at least some of the plurality of DSN units when performing the scoring function to produce a first scoring resultant as the scoring resultant, interpret the first scoring resultant to determine a first re-write requesting protocol for resending the write request to the DSN memory, and resend the write request in accordance with the first re-write requesting protocol. When a second error message is received in response to the resending the write request in accordance with the first re-write requesting protocol, the processing module utilizes a second number of DSN units as the at least some of the plurality of DSN units when performing the scoring function to produce a second scoring resultant as the scoring resultant, interprets the second scoring resultant to determine a second re-write requesting protocol for resending the write request to the DSN memory, and resend the write request in accordance with the second re-write requesting protocol. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the dispersed storage network or by other devices. In addition, at least one memory section (e.g., 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 of the dispersed storage network (DSN), cause the one or more computing devices to perform any or all of the method steps described above. 
       FIG. 42A  is a schematic block diagram of another embodiment of a dispersed storage network (DSN) that includes the distributed storage and task (DST) client module  34  of  FIG. 1 , the network  24  of  FIG. 1 , and a DST execution unit set  470 . The DST client module  34  includes the outbound DST processing  80  and the inbound DST processing  82  of  FIG. 3 . The outbound DST processing  80  includes the dispersed storage (DS) error encoding  112  of  FIG. 4  and a slice combiner  472 . The inbound DST processing  82  includes the DS error decoding  182  of  FIG. 13  and a slice de-combiner  474 . The DST execution unit set  470  includes a set of DST execution units  1 - n . Each DST execution unit may be implemented utilizing the DST execution unit  36  of  FIG. 1 . 
     The DSN functions to access stored data. In an example of operation of the accessing of the stored data, the DS error encoding  112  dispersed storage error encodes a data segment of data in accordance with dispersal parameters to produce a set of encoded data slices  476 . The dispersal parameters includes one or more of an information dispersal algorithm (IDA) width, a decode threshold number, and an identifier of an encoding matrix. For example, the DS error encoding  112  utilizes dispersal parameters that includes an IDA width of 12 based on a system registry lookup for a vault associated with the data. 
     The slice combiner  472  determines a number of storage locations for storage of the set of encoded data slices. The determining may be based on one or more of initiating a query, receiving a query response, performing a lookup, and receiving storage location instructions. For example, the slice combiner  472  determines to utilize eight storage locations when the DST execution unit set includes eight DST execution units  1 - 8 . For instance, the number of storage locations is less than the IDA width when the number of storage locations is eight and the IDA width is 12. 
     Having determined the number of storage locations, for each storage location, the slice combiner  472  combines a portion of at least two encoded data slices to produce a combined slice  478  for storage at the storage location in accordance with a combining approach when the number of storage locations is less than the IDA width. The combining approach includes one of an even distribution, a weighted distribution, and a predetermined distribution. For example, the slice combiner  472  combines halves of encoded data slices such that each storage location will receive three halves of an encoded data slice when the IDA width is 12 and the number of storage locations is eight. For instance, the slice combiner combines both halves of encoded data slice  1  with a first half of encoded data slice  2  to produce a first combined slice, combines a second half of encoded data slice  2  with both halves of encoded data slice  3  to produce a second combined slice, etc. Many other such combinations are possible. 
     For each combined slice, the outbound DST processing  80  sends, via the network  24 , the combined slice  478  to the corresponding DST execution unit for storage. For example, the outbound DST processing  80  generates a write slice request that includes the first combined slice and sends, via the network  24 , the first combined slice to the DST execution unit  1  for storage. The generating of the write request may further include generating one or more slice names associated with the at least two encoded data slices utilized to produce the combined slice. 
     When retrieving the data, the inbound DST processing  82  receives combined slices  478  from the DST execution unit set. For example, the inbound DST processing  82  issues, via the network  24 , read slice requests to the DST execution unit set and extracts the combined slices  478  from received read slice responses. For each received combined slice, the slice de-combiner  474  performs a de-combiner function on the received combined slice to reproduce the portions of the at least two encoded data slices. 
     Having produced the portions of the at least two encoded data slices, the slice de-combiner  474  aggregates portions of combined encoded data slices to reproduce the set of encoded data slices  476 . Alternatively, the slice de-combiner produces at least a decode threshold number of encoded data slices of the set of encoded data slices. The DS error decoding  182  dispersed storage error decodes the decode threshold number of encoded data slices of the reproduced set of encoded data slices  476  to produce a recovered data segment. 
       FIG. 42B  is a flowchart illustrating an example of accessing stored data. The method begins or continues at step  480  where a processing module (e.g., of a distributed storage and task (DST) client module), when storing data, encodes a data segment of data to produce a set of encoded data slices, where the set of encoded data slices includes an information dispersal algorithm (IDA) width number of encoded data slices. The method continues at step  482  where the processing module determines a number of storage locations, where the number of storage locations is less than the IDA width number. The determining includes at least one of interpreting a system registry, initiating a query, and interpreting a query response. 
     For each storage location, the method continues at step  484  where the processing module combines a portion of at least two encoded data slices of the set of encoded data slices to produce a combined slice for storage in the storage location. The combining is in accordance with a combining approach. For each combined slice, the method continues at step  486  where the processing module sends the combined slice to the storage location for storage. 
     When retrieving the data, the method continues at step  488  where the processing module obtains combined slices associated with a set of encoded data slices. For example, the processing module issues read slice requests to the storage locations, where the read slice requests includes slice names associated with the portions of the encoded data slices, and receives read slice responses that includes the combined slices. 
     For each combined slice, the method continues at step  490  where the processing module de-combines the combined slice to reproduce the portions of the at least two encoded data slices. For example, the processing module de-combines the combined slice in accordance with the combining approach to reproduce the portions of the at least two encoded data slices. 
     The method continues at  492  where the processing module aggregates portions of common encoded data slices to reproduce at least some encoded data slices of the set of encoded data slices. The method continues at step  494  where the processing module decodes a decode threshold number of the reproduced at least some encoded data slices of the set of encoded data slices to produce a recovered data segment. 
       FIG. 43A  is a schematic block diagram of another embodiment of a dispersed storage network (DSN) that includes the distributed storage and task (DST) processing unit  16  of  FIG. 1 , the network  24  of  FIG. 1 , and a DST execution (EX) unit set  500 . The DST execution unit set  500  includes a set of DST execution units  1 - n . Each DST execution unit includes one or more memories  1 - 2  and the processing module  84  of  FIG. 3 . 
     The DSN functions to migrate data from a first storage format to a second storage format. In an example of operation of the migration of the data, the processing module  84  of DST execution unit  1  determines to update a storage format to a second storage format for a plurality of encoded data slices stored in a first memory utilizing a first storage format. The determining includes at least one of interpreting a request, identifying a data type, receiving new storage configuration information, detecting a storage efficiency level that compares unfavorably to a threshold level, and detecting an unfavorable performance level. 
     Having determined to update the storage format, the processing module  84  initiates migration of the plurality of encoded data slices from the first memory to a second memory, where the encoded data slices are stored in the second memory using the updated storage format. Alternatively, the first and second memories are a common memory. For example, the processing module  84  retrieves an encoded data slice stored in the first memory utilizing the first storage format and stores the retrieved encoded data slice in the second memory utilizing the second storage format. 
     While migrating the plurality of encoded data slices, the processing module  84  updates a rate of migration based on a DST execution unit activity level. The updating includes determining the updated DST execution unit activity level. For example, the processing module  84  monitors access requests  502  and access responses  504  associated with one or more of rebuilding encoded data slices, writing the encoded data slices, retrieving the encoded data slices, listing the encoded data slices, performing maintenance tasks, performing balancing tasks, and any other activities of the DST execution unit. For example, the processing module  84  lowers the rate (e.g., including pausing) of migration when detecting a higher than average DST execution unit activity level. 
       FIG. 43B  is a flowchart illustrating an example of migrating data from a first storage format to a second storage format. The method begins or continues at step  506  where a processing module (e.g., of a distributed storage and task (DST) execution unit, a storage unit) identifies a first storage format utilized to store a first plurality of encoded data slices in a first memory of the storage unit of a set of storage units, where a data segment is dispersed storage error encoded to produce a set of encoded data slices that are stored in the set of storage units. The identifying includes at least one of performing a lookup, initiating a query, interpreting a query response, receiving an error message, and detecting a storage inefficiency. 
     The method continues at step  508  where the processing module determines to utilize another storage format for storage of the first plurality of encoded data slices. The determining includes at least one of detecting the storage inefficiency, interpreting the error message, and detecting a favorable comparison of an available storage level to an available storage threshold level. The method continues at step  510  where the processing module selects a second storage format as the other storage format for storage of the first plurality of encoded data slices. The selecting includes at least one of identifying one or more other storage formats, estimating an updated level of storage performance for each of the one or more other storage formats, selecting a storage format as the second storage format that corresponds to a favorable estimated storage performance level (e.g., best or above a threshold level). 
     The method continues at step  512  where the processing module initiates migration of the first plurality of encoded data slices from storage utilizing the first storage format to storage utilizing the second storage format. The initiating includes retrieving an encoded data slice using the first storage format and storing the retrieved encoded data slice using the second storage format. The retrieving and storing may be with a common memory or different memories. 
     While migrating the first plurality of encoded data slices, the method continues at step  514  where the processing module updates a rate of migration based on a storage unit activity level. The updating includes one or more of monitoring the storage unit activity level, raising the rate of migration when the storage unit activity level is less than a low threshold level, and lowering the rate of migration when the storage unit activity level is greater than a high threshold level. 
       FIG. 44A  is a schematic block diagram of another embodiment of a dispersed storage network (DSN) the distributed storage and task (DST) client module  34  of  FIG. 1 , the network  24  of  FIG. 1 , and a DST execution (EX) unit set  520 . The DST client module  34  includes a key derivation function  522 , a blinded password generator  526 , a random number generator  524 , a key re-generator  528 , a decryptor  530 , and the dispersed storage (DS) error decoding  182  of  FIG. 13 . The DST execution unit set  520  includes a set of DST execution units  1 - n . Each DST execution unit includes the memory  88  of  FIG. 3  and the processing module  84  of  FIG. 3 . 
     The DSN functions to recover data stored in the DST execution unit set  520 . In an example of operation of the recovering of the stored data, the key derivation function  522  performs a key derivation algorithm on a password  532  to produce a key  534 . The key derivation algorithm generates the key  534  based on the password  532  in accordance with a number of desired bits for the key  534 . Examples of the key derivation algorithm includes at least one of industry algorithms PBKDF2, bcrypt, and scrypt. Such a key derivation algorithm may take 100 ms or more to process the password  532 , thus providing an anti-hacking system performance improvement. 
     The random number generator  524  generates a set of random numbers b 1 -bn. The blinded password generator  526  generates a set of blinded passwords  1 - n  based on the key  534  and the set of random numbers b 1 -bn. For example, the blinded password generator  526  generates a first blinded password in accordance with the formula of: blinded password  1 =[[MGF(KEY)]^2]^b 1  modulo p; where MGF is a mask generating function, and p is a fixed number related to another fixed number q in accordance with a formula q=(p−1)/2. Having generated the set of blinded passwords  1 - n , the DST client module  34  sends, via the network  24 , the set of blinded passwords  1 - n  to the set of DST execution units  1 - n.    
     The processing module  84  of each DST execution unit generates corresponding confidential information based on a received blinded password, a corresponding recovered random number of a set of original random numbers e 1 -en, and a retrieved encrypted slice, where a data segment of data was previously dispersed storage error encoded to produce a set of encoded data slices and where each encoded data slice was encrypted using a corresponding key of a set of keys to produce an encrypted slice of a set of encrypted slices and where the set of keys were generated based on the password and the set of original random numbers e 1 -en and that includes the corresponding recovered random number. For example, the DST processing module  84  of DST execution unit  1  recovers a first encrypted slice from the memory  88  of the DST execution unit  1 , recovers a first original random number e 1 , generates a first passkey of a set of passkeys  1 - n  in accordance with a formula of: passkey 1=blinded password  1 ^e 1  modulo p, and aggregates the first encrypted slice and the blinded password  1  to produce confidential information  1 . 
     Having generated the corresponding confidential information, each DST execution unit sends the corresponding information to the DST client module  34 . For example, the processing module  84  of the DST execution unit  1  sends, via the network  24 , the confidential information  1  to the DST client module  34 . The DST client module  34  receives at least a decode threshold number of a set of confidential information  1 - n.    
     Having received the confidential information  1 - n , the key re-generator  528  regenerates the set of keys  1 - n  based on received passkeys  1 - n  and the set of random numbers b 1 -bn when receiving passkeys  1 - n . For example, the key re-generator  528  regenerates a first key in accordance with a formula of: key  1 =passkey 1 ^v 1  modulo p, where v 1  is derived based on a relationship of b 1 *v 1 =1 modulo q. 
     The decryptor  530  decrypts received encrypted slices  1 - n  using the regenerated set of keys  1 - n  to reproduce the set of encoded data slices  1 - n . The DS error decoding  182  dispersed storage error decodes a decode threshold number of reproduced encoded data slices of the set of encoded data slices  1 - n  to produce recovered data  536 . 
       FIG. 44B  is a flowchart illustrating an example of recovering data. The method begins or continues at step  538  where a processing module (e.g., of a distributed storage and task (DST) client module) performs a key derivation function on a password to produce a key. The method continues at step  540  where the processing module issues a set of blinded passwords to a set of storage units, where the blinded passwords are generated based on the key. For example, the processing module generates a first blinded password in accordance with a formula of: blinded password  1 =[[MGF(KEY)]^2]^b 1  modulo p, where b 1  is a random number of a set of random numbers. 
     The method continues at step  542  where the processing module receives at least a decode threshold number of confidential information responses, where each response includes an encrypted slice and an associated passkey, where a first passkey is generated by a corresponding storage unit in accordance with a formula of: passkey  1 =bpass 1 ^e 1  modulo p; where e 1  is a recovered random number associated with the encrypted slice and a corresponding original random number. 
     The method continues at step  544  where the processing module regenerates a set of keys using passkeys of the confidential information. For example, the processing module generates a first key of the set of keys in accordance with a formula of key  1 =passkey 1 ^v 1  modulo p; where b 1 *v 1 =1 modulo q and where q=(p−1)/2. 
     The method continues at step  546  where the processing module decrypts a set of encrypted slices of the confidential information using the set of keys to reproduce a set of encoded data slices. For example, the processing module decrypts the encrypted slice  1  using the first key  1  to produce an encoded data slice  1  of the set of encoded data slices. The method continues at step  548  where the processing module dispersed storage error decodes a decode threshold number of the set of reproduced encoded data slices to produce recovered data. 
       FIG. 45A  is a schematic block diagram of another embodiment of a dispersed storage network (DSN) that includes the distributed storage and task (DST) processing unit  16  of  FIG. 1 , the network  24  of  FIG. 1 , and at least two DST execution (EX) unit tier storage pools  2 , and  4 . The DST processing unit  16  includes the DST client module  34  of  FIG. 1 . Each DST execution unit tier storage pool includes a set of DST execution units  1 - n . Each DST execution unit may be implemented utilizing the DST execution unit  36  of  FIG. 1 . 
     The DSN functions to migrate data stored in a first storage pool tier level to a second storage pool tier level, where data is dispersed storage error encoded to produce a plurality of sets of encoded data slices that is stored in the set of DST execution units of the first storage pool tier level. For example, a first data object A is dispersed storage error encoded to produce a plurality of sets of encoded data slices that are stored in the set of DST execution units of the DST execution unit tier  2  storage pool. For instance, slices A- 1 - 1  to A- 1 -M are stored in DST execution unit  1 , slices A- 2 - 1  to A- 2 -M are stored in DST execution unit  2 , etc. 
     In an example of operation of the migration, the DST client module  34  determines to update a storage pool tier level for data stored in the first storage pool, where the storage pool is associated with the first storage pool tier level. The determining includes at least one of interpreting an account update, identifying the data based on an account identifier, detecting nonpayment for storage services, detecting lower payment for storage services, detecting higher payment for storage services, interpret a storage pool tier level schedule, receiving a request, detecting data access inactivity, and detecting an unfavorable storage performance level. For example, the DST client module  34  identifies the encoded data slices associated with the data object A as the data stored in the first storage pool for updating of the storage pool tier level when payment for storage services of the data object A are delinquent. 
     Having determined to update the storage pool tier level, the DST client module  34  determines a second storage pool tier level based on the determining to update the storage pool tier level. The determining may be based on one or more of a comparison of actual payment for storage services to terms of payment for storage services. For example, the DST client module  34  determines to utilize a tier  4  storage pool for storage of the encoded data slices of the data object A when the payment for the storage services of the data object A are delinquent and the tier  4  storage pool is associated with low payment or nonpayment (e.g., a long-term storage service class, a storage service with lower retrieval reliability). 
     Having determined the second storage pool tier level, the DST client module  34  selects a set of DST execution units associated with the second storage pool. The selecting may be based on one or more of a request, a lookup of available sets of storage units and associated tier levels, a storage costs level, a round-robin selection approach, initiating a query, and interpreting a query response. For example, the DST client module selects the DST execution unit tier  4  storage pool when selecting the tier  4  storage pool level and the DST execution unit tier  4  storage pool is available with available storage capacity. 
     Having selected the second storage pool, the DST client module  34  facilitates migration of one or more sets of encoded data slices associated with the data from the first storage pool to the second storage pool. For example, the DST client module  34  issues access requests  550 , via the network  24 , to the DST execution unit tier  2  storage pool to retrieve the slices associated with the data object A, receives access responses  552  that includes the requested slices, and issues further access requests  550  to the DST execution unit tier  4  storage pool, where the further access requests  550  includes the slices associated with the data object A. 
     Having facilitated the migration of the one or more sets of encoded data slices, the DST client module  34  updates at least one of a DSN directory and a dispersed hierarchical index to associate the one or more sets of encoded data slices with the second storage pool and to disassociate the one or more sets of encoded data slices from the first storage pool. For example, the DST client module  34  updates an index entry of the dispersed hierarchical index to include a source name associated with the data object A, a data name for the data object A, and an identifier of the DST execution unit tier  4  storage pool. 
       FIG. 45B  is a flowchart illustrating an example of migrating data from a first storage pool tier level to a second storage pool tier level. The method begins or continues at step  554  where a processing module (e.g., of a distributed storage and task (DST) client module) determines to update a storage pool tier level for data stored in a first storage pool. The determining may be based on one or more of interpreting a request, interpreting an account update, identifying the data based on an account identifier, detecting a payment mismatch for storage services, and detecting an unfavorable storage performance. 
     The method continues at step  556  where the processing module determines a second storage pool tier level, where the determining may be based on one or more of a level commensurate with the updated account, the payment mismatch, and a desired storage performance level. The method continues at step  558  where the processing module selects a second storage pool based on the second storage pool tier level. For example, the processing module identifies one or more storage pools associated with the second storage pool tier level, selects a storage pool based on one or more of availability, available storage space, pricing, a random selection, a preferred storage pool, and an estimated performance for a user. 
     The method continues at step  560  where the processing module identifies one or more sets of encoded data slices associated with the data stored in the first storage pool. The identifying includes at least one or more of accessing one or more of a dispersed storage network directory and a dispersed hierarchical index to identify one or more DSN address range that is associated with the data, identify slice names and the DSN address ranges, and identify stored encoded data slices associated with the identified slice names. 
     The method continues at step  562  where the processing module facilitates migration of the identified one or more sets of encoded data slices from the first storage pool to the second storage pool. For example, the processing module retrieves the identified one or more sets of encoded data slices from the first storage pool and stores the one or more sets of encoded data slices and the second storage pool. 
     The method continues at step  564  where the processing module associates the identified one or more sets of encoded data slices with the second storage pool. The associating includes updating at least one of the DSN directory and the dispersed hierarchical index to associate the identified slice names with the second storage pool and to disassociate the identified slice names from the first storage pool. 
       FIG. 46A  is a schematic block diagram of another embodiment of a dispersed storage network (DSN) that includes the distributed storage and task (DST) processing unit  16  of  FIG. 1 , the network  24  of  FIG. 1 , and a DST execution (EX) unit storage vault  570 . The DST processing unit  16  includes the DST client module  34  of  FIG. 1 . The DST execution unit storage vault  570  includes at least one set of DST execution units  1 - n . Each DST execution unit may be implemented utilizing the DST execution unit  36  of  FIG. 1 . 
     The DSN functions to access data stored in a plurality of containers  1 -C (e.g., virtual containers) within the set of DST execution units  1 - n , where data is dispersed storage error encoded to produce a plurality of sets of encoded data slices that are stored in at least one container within the set of DST execution units  1 - n . For example, for a data object A, a plurality of sets encoded data slices, including A- 1 - 1 , A- 2 - 1 , through A-n- 1  for a first set through an Mth set A- 1 -M, A- 2 -M, through A-n-M are stored in a first container within the set of DST execution units  1 - n . In an example of operation, the DST client module  34  obtains a request (e.g., store, retrieve) to access the data associated with the first container of the DST execution unit storage vault. 
     Having obtained the request, the DST client module  34  identifies a first header object associated with the first container. The determining may include one or more of a performing local lookup, interpreting an index entry of a dispersed hierarchical index, interpreting a DSN directory entry, and may be further based on one or more of an identifier of a requesting entity, an identifier of the data, performing a container identifier look up based on the identifier of the requesting entity, and performing a lookup of a DSN address of the first header object based on the container identifier. For example, the DST client module  34  performs a dispersed hierarchical index lookup utilizing the identifier of the data to recover an index entry, extracts the container identifier of container  1  from the index entry, performs another dispersed hierarchical index lookup utilizing the container identifier to recover another index entry, and extracts the DSN address of the first header object from the recovered other index entry for the container  1 . 
     Having identified the first header object, the DST client module  34  recovers the first header object from the DSN memory. For example, the DST client module  34  issues, via the network  24 , access requests  572  to the set of DST execution units  1 - n  using the DSN address of the first header object, receives access responses  574 , and decodes a decode threshold number of encoded headers slices of the access responses to reproduce the first header object. 
     Having reproduced the first header object, the DST client module  34  further processes the request to access the data utilizing a first encryption key extracted from the first header object. For example, the DST client module  34 , when storing the data, encrypts the data using the first encryption key to produce encrypted data, dispersed storage error encodes the encrypted data to produce a plurality of sets of encoded data slices, and sends, via the network  24 , the plurality of sets of encoded data slices to the set of DST execution units for storage. As another example, the DST client module  34 , when retrieving the data, recovers a decode threshold number of encoded data slices for each set of encoded data slices from the set of DST execution units, decodes, for each set of encoded data slices, each decode threshold number of encoded data slices to produce recovered encrypted data, and decrypts the recovered encrypted data using the first encryption key to produce recovered data. 
     Having stored the data, the DST client module  34  may determine to make the data associated with the first container of the DST execution unit storage vault unusable. The determining may be based on one or more of receiving a data deletion request for the first container, interpreting a data deletion schedule, and detecting that an available storage space is less than a low available storage space threshold level. 
     When determining to make the data associated with the first container unusable, the DST client module  34  identifies the first data object associated with the first container. Having identified the first header object, the DST client module  34  facilitates disabling use of the first encryption key of the first header object. 
     As a specific example, the DST client module  34  recovers the first header object, deletes the first encryption key to produce an updated first header object, and stores the updated first header object in the set of DST execution units  1 - n . The storing includes dispersed storage error encoding the updated first header object to produce a set of updated first header object slices and sending, via the network  24 , the set of updated first header object slices to the set of DST execution units  1 - n  for storage. 
     As another specific example, the DST client module  34  recovers the first header object, generates a copy of the first header object, stores the copy of the first header object in another data object, deletes the first encryption key from the first header object to produce the updated first header object, and stores the updated first header object in the set of DST execution units  1 - n . As yet another specific example, the DST client module  34  recovers the first header object, encrypts the first encryption key using a secret key to produce an obfuscated first encryption key, replaces the first encryption key with the obfuscated first encryption key within the recovered first header object to produce the updated first header object, and stores the updated first header object in the set of DST execution units  1 - n.    
       FIG. 46B  is a flowchart illustrating an example of accessing data within a vault storage container of a dispersed storage network (DSN). The method begins or continues at step  576  where a processing module (e.g., of a distributed storage and task (DST) client module) receives a request to access data associated with a first container of a common storage vault of a DSN memory. The receiving includes at least one of receiving a retrieval request and receiving a storage request. 
     The method continues at step  578  where the processing module identifies a first header object associated with the first container. For example, the processing module identifies the common storage vault and a container ID based on one or more of a requesting entity identifier and an identifier of the data; and performs a lookup using the container ID to obtain a DSN address of the first header object. 
     The method continues at step  580  where the processing module recovers the first header object from the DSN memory. For example, the processing module issues read slice requests to the DSN memory using the DSN address of the first header object, receives header slices, and dispersed storage error decodes a decode threshold number of the header slices to reproduce the first header object. 
     The method continues at step  582  where the processing module facilitates execution of the request to access the data utilizing a first encryption key of the first header object. As a storage example, the processing module extracts the first encryption key from the first header object, encrypts the data using the extracted first encryption key to produce encrypted data, dispersed storage error encodes the encrypted data to produce slices, and sends the slices to a set of storage units associated with the first container. As a retrieval example, the processing module extracts the first encryption key from the first header object, recovers slices from the first container of the set of storage units, dispersed storage error decodes the recovered slices to reproduce the encrypted data, and decrypts the encrypted data using the extracted first encryption key to reproduce the data. 
     The method continues at step  584  where the processing module determines to make all data associated with the first container of the common storage vault unusable. The determining may be based on one or more of receiving a delete container request, receiving a hide container request, receiving a make unusable request, interpreting a storage error, and interpreting an error message. 
     The method continues at step  586  where the processing module identifies the first data object associated with the first container. For example, the processing module performs a lookup using the container identifier to obtain the DSN address of the first header object. The method continues at step  588  where the processing module facilitates disabling use of the first encryption key of the first header object for accessing all the data. As a specific example, the processing module permanently deletes the first encryption key. As another example, the processing module moves the first encryption key to another storage location. As yet another example, the processing module replaces the first encryption key with an encrypted version of the first encryption key utilizing a secret key. Alternatively, or in addition to, the processing module deletes encoded data slices associated with the first container (e.g., for each set of encoded data slices associated with each data object associated with the first container). 
       FIG. 47A  is a schematic block diagram of another embodiment of a dispersed storage network (DSN) that includes the distributed storage and task (DST) processing unit  16  of  FIG. 1 , the network  24  of  FIG. 1 , and a DST execution (EX) unit set  590 . The DST processing unit  16  includes the DST client module  34  of  FIG. 1 . The DST execution unit set  590  includes a set of DST execution units  1 - n . Each DST execution unit includes a plurality of memories  1 -R. Each DST execution unit may be implemented utilizing the DST execution unit  36  of  FIG. 1 . 
     The DSN functions to select a set of memories for storage of one or more sets of encoded data slices of data for storage, where the set of DST execution units includes a plurality of sets of memories that includes the selected set of memories, where each set of memories is associated with a unique DSN address range of a plurality of DSN address ranges associated with the set of DST execution units. In an example of operation of the selecting of the set of memories, the DST client module  34  obtains performance history from each DST execution unit of the DST execution unit set  590 , where the performance history includes performance level history of each memory device of the plurality of memory devices associated with the DST execution unit. 
     The performance level history includes one or more of an access frequency level, an access latency level, a throughput level, an available storage space level, a utilized storage space level, and a storage capacity level. The obtaining includes at least one of issuing a performance history request, receiving a performance history response, interpreting an error message, and performing a lookup. For example, the DST client module  34  receives performance history  1 - n  from the DST execution unit set in response to issuing, via the network  24 , the performance history request. 
     Having obtained the performance history, the DST client module  34  obtains a set of encoded data slices for storage in the set of DST execution units  1 - n . The obtaining includes at least one of receiving the set of encoded data slices and dispersed storage error encoding a data segment of the data to produce the set of encoded data slices. 
     Having obtained the set of encoded data slices, the DST client module  34  selects a set of memory devices of the plurality of sets of memory devices based on the performance history in accordance with a selection approach. As a specific example, the selection approach includes identifying a set of memory devices associated with the at least a threshold number (e.g., a write threshold number) of memory devices with favorable performance histories (e.g., similar history, above a threshold level, fastest, most available space, at least active). 
     Having selected the set of memory devices, the DST client module  34  generates a set of slice names corresponding to the selected set of memory devices. As a specific example, the DST client module  34  identifies a DSN address range associated with a set of memory devices based on lookup, generates a source name associated with the identify DSN address range that includes the generated source name, and generates the set of slice names to include the source name. 
     Having generated the set of slice names, the DST client module  34  generates a set of write slice requests  592  that includes the set of slice names of the set of encoded data slices. Having generated the set of write slice requests  592 , the DST client module  34  sends, via the network  24 , the set of write slice requests  592  to the DST execution unit set to store the set of encoded data slices in the selected set of memory devices. 
       FIG. 47B  is a flowchart illustrating an example of selecting a set of memory devices. The method begins or continues at step  594  where a processing module (e.g., of a distributed storage and task (DST) client module) obtains performance history from each storage unit of a set of storage units. The obtaining includes at least one of issuing a set of performance history requests to the set of storage units, receiving performance history responses from at least some of the storage units, interpreting an error message, and performing a lookup. 
     The method continues at step  596  where the processing module obtains a set of encoded data slices for storage in a set of memory devices of a plurality of sets of memory devices of the set of storage units. The obtaining includes at least one of receiving the set of encoded data slices and dispersed storage error encoding a data segment of data to produce the set of encoded data slices. 
     The method continues at step  598  where the processing module selects the set of memory devices of the plurality of sets of memory devices of the set of storage units based on the performance history of each storage unit. For example, the processing module identifies the set of memory devices associated with at least a threshold number of memory devices that exhibit favorable performance histories in accordance with a selection scheme. The selection schemes includes one or more of selecting similarly performing memory devices, selecting memory devices associated with the performance level above a performance threshold level, selecting fastest performing memory devices, selecting memory devices associated with highest levels of available storage space, and selecting storage units associated with lowest levels of access frequency. 
     The method continues at step  600  where the processing module generates a set of slice names corresponding to the selected set of memory devices. For example, the processing module generates a source name associated with a dispersed storage network address range affiliated with the set of memory devices and generates the set of slice names to include the generated source name. 
     The method continues at step  602  where the processing module generates a set of write slice requests that includes the set of encoded data slices and the set of slice names. The method continues at step  604  where the processing module sends the set of write slice requests to the set of storage units to store the set of encoded data slices in the selected set of memory devices. 
       FIG. 48A  is a schematic block diagram of another embodiment of a dispersed storage network (DSN) that includes the distributed storage and task (DST) processing unit  16  of  FIG. 1 , the network  24  of  FIG. 1 , and a DST execution (EX) unit set  610 . The DST processing unit  16  includes the DST client module  34  of  FIG. 1 . The DST execution unit set  610  includes a set of DST execution units  1 - n . Each DST execution unit may be implemented utilizing the DST execution unit  36  of  FIG. 1 . 
     The DSN functions to update a dispersed hierarchical index and to synchronize the dispersed hierarchical index with stored data. The dispersed hierarchical index may include a dispersed lockless concurrent index in a dispersed data structure that maintains and in-order transportable list of entries indexed by a one or more index keys. For example, for each data object stored in the DST execution unit set as sets of encoded data slices (e.g., A- 1 - 1  to A- 1 -M, A- 2 - 1  to A- 2 -M, etc., of a data object A), one or more dispersed hierarchical indexes may be updated to insert a data name associated with the data object A. The dispersed hierarchical index supports an ordered listing of stored data objects in a similar fashion to a directory hierarchy. A data object may be a reference to any number of dispersed hierarchical indexes  1 -D. Each dispersed hierarchical index is associated with an index type. Index types include at least one of by name, by time, and by any other common attribute. The index keys are affiliated with the index type. For example, a time based index key is utilized with a dispersed article index associated with a time index type. 
     An example of operation of the synchronizing of the dispersed hierarchical index with stored data is discussed in greater detail with reference to  FIG. 49 . In an example of operation of the updating of the dispersed hierarchical index, the DST client module  34  determines that an update is required to one or more dispersed hierarchical indexes  1 -D associated with data stored in the DST execution unit set. The determining may be based on one or more of updating storage of the data, receiving a request, and identifying a storage mismatch. 
     Having determined that the update is required, the DST client module  34  obtains a performance level of the DST execution unit set. The obtaining includes at least one of interpreting a test result, accessing a historical record, interpreting an error message, and monitoring and access level. Having obtained the performance level of the DST execution unit set, for each of the one or more dispersed hierarchical indexes, the DST client module  34  determines an update schedule based on the performance level of the DST execution unit set and in accordance with an update prioritization approach. The update prioritization approach includes one or more of updating a primary index immediately, updating in accordance with a priority level of each index, always updating all indexes, and updating a portion of an index based on an activity level of the DST execution unit set. As a specific example, the DST client  34  temporarily suspends updating at least some of the one or more dispersed hierarchical indexes associated with the data when access frequency levels of the DST execution units is greater than a high access frequency threshold level (e.g., too busy to update now) in favor of maintaining a temporary local directory to cache pending updates. 
     Having determined the update schedule, for each of the one or more dispersed hierarchical indexes, the DST client module  34  updates the dispersed hierarchical index in accordance with the update schedule. For example, the DST client module  34 , when scheduled, issues access requests  612  to the DST execution unit set to recover an index entry, receives access response  614 , updates the index entry (e.g., based on a recently determined required update, based on a cached update) to produce an updated index entry, and issues further access requests  612  to the DST execution unit set to store the updated index entry. Alternatively, the DST client module  34  may generate a new index entry for storage in the DST execution unit set. While updating the one or more dispersed hierarchical indexes, the DST client module  34  may further update the update schedule. The example of the operation of the updating of the dispersed hierarchical index is discussed in greater detail with reference to  FIG. 48B . 
       FIG. 48B  is a flowchart illustrating an example of updating a dispersed hierarchical index. The method begins or continues at step  616  where a processing module (e.g., of a distributed storage and task (DST) client module) determines that an update is required to one or more dispersed hierarchical indexes that are associated with data stored in a set of storage units. The determining may be based on one or more of detecting updating of storage of the data, receiving a request to update the data, and identifying a mismatch between a local representation of an index and a representation of the index recovered from the set of storage units. 
     The method continues at step  618  where the processing module obtains performance levels of the set of storage units. The obtaining includes at least one of initiating a performance test, interpreting a performance test result, accessing a record, interpreting an error message, and interpreting monitored storage unit set access messages. 
     For each of the one or more dispersed hierarchical indexes, the method continues at step  620  where the processing module determines an update schedule based on the performance level of the storage unit set and in accordance with an update prioritization approach. As a specific example, the processing module identifies the prioritization approach (e.g., performs a lookup, receives a request, determines based on one or more of a data type, a requesting entity identifier, a time of day, and a level of storage unit activity) and implements the prioritization approach to produce the updated schedule. 
     For each of the one or more dispersed hierarchical indexes, the method continues at step  622  where the processing module updates the dispersed hierarchical index in accordance with the update schedule. As a specific example, the processing module accesses the dispersed hierarchical index from the set of storage units to facilitate synchronization of the accessed dispersed hierarchical index with a local representation of the dispersed article index (e.g., recover, update, store). 
       FIG. 49  is a flowchart illustrating an example of synchronizing a dispersed hierarchical index with stored data. In the example of operation of the synchronizing of the dispersed hierarchical index with the stored data, the method begins or continues at step  624  where a processing module (e.g., of a distributed storage and task (DST) client module) identifies data objects stored in a DST execution unit set. The identifying includes at least one of accessing a list (e.g., a dispersed storage network directory) to identify a data object and verifying that the data object is stored in the DST execution unit set. 
     For each data object, the method continues at step  626  where the processing module determines an index key for an associated dispersed hierarchical index. As a specific example, the processing module analyzes the data object to identify a data object attribute (e.g., a name, a timestamp, a metric, a size, a data type, a data owner, etc.) and generates the index key based on the data object attribute. 
     When the index key is not found within the associated dispersed hierarchical index, the method continues at step  628  where the processing module inserts the index key into an index entry of the associated dispersed hierarchical index. As a specific example, the processing module updates the index entry and stores the updated index entry in the DST execution unit set (e.g., in accordance with an update schedule). 
     The method continues at step  630  where the processing module identifies index entries of the dispersed hierarchical index. As a specific example, the processing module traverses the dispersed hierarchical index to identify the index entries. For each index entry, the method continues at step  632  where the processing module determines whether a corresponding data object is stored in the DST execution unit set. As a specific example, the processing module accesses a dispersed storage network address associated with the index entry (e.g., issues a list request and receives a list response to determine whether a slice is exist for the data object). 
     When the corresponding data object is not found within the DST execution unit set, the method continues at step  634  where the processing module indicates an index discrepancy. The indicating includes at least one of indicating an identifier of the index, indicating identifier of the index entry, including a dispersed storage network address of the data object that is not stored in the DST execution unit set, issuing an error message, updating the list, and recovering the data object using another means. 
     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 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 also be used herein, the terms “processing module”, “processing circuit”, 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. 
     The present invention has 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. 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. 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. 
     The present invention may have also 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. 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. 
     While the transistors in the above described figure(s) is/are shown as field effect transistors (FETs), as one of ordinary skill in the art will appreciate, the transistors may be implemented using any type of transistor structure including, but not limited to, bipolar, metal oxide semiconductor field effect transistors (MOSFET), N-well transistors, P-well transistors, enhancement mode, depletion mode, and zero voltage threshold (VT) transistors. 
     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. 
     The term “module” is used in the description of the various embodiments of the present invention. A module includes a processing module, a functional block, hardware, and/or software stored on memory for performing one or more functions as may be described herein. Note that, if the module is implemented via hardware, the hardware may operate independently and/or in conjunction software and/or firmware. As used herein, a module may contain one or more sub-modules, each of which may be one or more modules. 
     While particular combinations of various functions and features of the present invention have been expressly described herein, other combinations of these features and functions are likewise possible. The present invention is not limited by the particular examples disclosed herein and expressly incorporates these other combinations.