Patent Publication Number: US-10324657-B2

Title: Accounting for data whose rebuilding is deferred

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     The present U.S. Utility Patent Application claims priority pursuant to 35 U. S.C. § 120 as a continuation-in-part of U.S. Utility application Ser. No. 15/082,887, entitled “TRANSFERRING ENCODED DATA SLICES IN A DISPERSED STORAGE NETWORK”, filed Mar. 28, 2016, which claims priority pursuant to 35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/168,145, entitled “TRANSFERRING ENCODED DATA SLICES BETWEEN STORAGE RESOURCES”, filed May 29, 2015, 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 or cloud storage. 
     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 a remote or Internet storage system. The remote or 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. 
     In a RAID system, a RAID controller adds parity data to the original data before storing it across an array of disks. The parity data is calculated from the original data such that the failure of a single disk typically will not result in the loss of the original data. While RAID systems can address certain memory device failures, these systems may suffer from effectiveness, efficiency and security issues. For instance, as more disks are added to the array, the probability of a disk failure rises, which may increase maintenance costs. When a disk fails, for example, it needs to be manually replaced before another disk(s) fails and the data stored in the RAID system is lost. To reduce the risk of data loss, data on a RAID device is often copied to one or more other RAID devices. While this may reduce the possibility of data loss, it also raises security issues since multiple copies of data may be available, thereby increasing the chances of unauthorized access. In addition, co-location of some RAID devices may result in a risk of a complete data loss in the event of a natural disaster, fire, power surge/outage, etc. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S) 
         FIG. 1  is a schematic block diagram of an embodiment of a dispersed, or distributed, storage network (DSN) in accordance with the present disclosure; 
         FIG. 2  is a schematic block diagram of an embodiment of a computing core in accordance with the present disclosure; 
         FIG. 3  is a schematic block diagram of an example of dispersed storage error encoding of data in accordance with the present disclosure; 
         FIG. 4  is a schematic block diagram of a generic example of an error encoding function in accordance with the present disclosure; 
         FIG. 5  is a schematic block diagram of a specific example of an error encoding function in accordance with the present disclosure; 
         FIG. 6  is a schematic block diagram of an example of slice naming information for an encoded data slice (EDS) in accordance with the present disclosure; 
         FIG. 7  is a schematic block diagram of an example of dispersed storage error decoding of data in accordance with the present disclosure; 
         FIG. 8  is a schematic block diagram of a generic example of an error decoding function in accordance with the present disclosure; 
         FIG. 9  is a schematic block diagram of an example of a dispersed storage network in accordance with the present disclosure; 
         FIG. 10A  is a schematic block diagram of another embodiment of a dispersed storage network (DSN) in accordance with the present invention; and 
         FIG. 10B  is a flowchart illustrating an example of accounting for data whose rebuilding is deferred. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       FIG. 1  is a schematic block diagram of an embodiment of a dispersed, or distributed, storage network (DSN)  10  that includes a plurality of dispersed storage (DS) computing devices or processing units  12 - 16 , a DS managing unit  18 , a DS integrity processing unit  20 , and a DSN memory  22 . The components of the DSN  10  are coupled to a network  24 , which may include one or more wireless and/or wire lined communication systems; one or more non-public intranet systems and/or public internet systems; and/or one or more local area networks (LAN) and/or wide area networks (WAN). 
     The DSN memory  22  includes a plurality of dispersed storage units  36  (DS units) that may be located at geographically different sites (e.g., one in Chicago, one in Milwaukee, etc.), at a common site, or a combination thereof. For example, if the DSN memory  22  includes eight dispersed storage units  36 , each storage unit is located at a different site. As another example, if the DSN memory  22  includes eight storage units  36 , all eight storage units are located at the same site. As yet another example, if the DSN memory  22  includes eight storage units  36 , a first pair of storage units are at a first common site, a second pair of storage units are at a second common site, a third pair of storage units are at a third common site, and a fourth pair of storage units are at a fourth common site. Note that a DSN memory  22  may include more or less than eight storage units  36 . 
     DS computing devices  12 - 16 , the managing unit  18 , and the integrity processing unit  20  include a computing core  26 , and network or communications interfaces  30 - 33  which can be part of or external to computing core  26 . DS computing devices  12 - 16  may each be a portable computing device and/or a fixed computing device. A portable computing device may be a social networking device, a gaming device, a cell phone, a smart phone, a digital assistant, a digital music player, a digital video player, a laptop computer, a handheld computer, a tablet, a video game controller, and/or any other portable device that includes a computing core. A fixed computing device may be a computer (PC), a computer server, a cable set-top box, a satellite receiver, a television set, a printer, a fax machine, home entertainment equipment, a video game console, and/or any type of home or office computing equipment. Note that each of the managing unit  18  and the integrity processing unit  20  may be separate computing devices, may be a common computing device, and/or may be integrated into one or more of the computing devices  12 - 16  and/or into one or more of the dispersed storage units  36 . 
     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 computing devices  14  and  16 . As another example, interface  32  supports communication links (e.g., a wired connection, a wireless connection, a LAN connection, and/or any other type of connection to/from the network  24 ) between computing devices  12  and  16  and the DSN memory  22 . As yet another example, interface  33  supports a communication link for each of the managing unit  18  and the integrity processing unit  20  to the network  24 . 
     In general and with respect to DS error encoded data storage and retrieval, the DSN  10  supports three primary operations: storage management, data storage and retrieval. More specifically computing devices  12  and  16  include a dispersed storage (DS) client module  34 , which enables the computing device to dispersed storage error encode and decode data (e.g., data object  40 ) as subsequently described with reference to one or more of  FIGS. 3-8 . In this example embodiment, computing device  16  functions as a dispersed storage processing agent for computing device  14 . In this role, computing device  16  dispersed storage error encodes and decodes data on behalf of computing device  14 . With the use of dispersed storage error encoding and decoding, the DSN  10  is tolerant of a significant number of storage unit failures (the number of failures is based on parameters of the dispersed storage error encoding function) without loss of data and without the need for a redundant or backup copies of the data. Further, the DSN  10  stores data for an indefinite period of time without data loss and in a secure manner (e.g., the system is very resistant to unauthorized attempts at accessing or hacking the data). 
     The second primary function (i.e., distributed data storage and retrieval) begins and ends with a DS computing devices  12 - 14 . For instance, if a second type of computing device  14  has data  40  to store in the DSN memory  22 , it sends the data  40  to the DS computing device  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 operation, the managing unit  18  performs DS management services. For example, the managing unit  18  establishes distributed data storage parameters (e.g., vault creation, distributed storage parameters, security parameters, billing information, user profile information, etc.) for computing devices  12 - 16  individually or as part of a group of user devices. As a specific example, the managing unit  18  coordinates creation of a vault (e.g., a virtual memory block associated with a portion of an overall namespace of the DSN) within the DSN memory  22  for a user device, a group of devices, or for public access and establishes per vault dispersed storage (DS) error encoding parameters for a vault. The managing unit  18  facilitates storage of DS error encoding parameters for each vault by updating registry information of the DSN  10 , where the registry information may be stored in the DSN memory  22 , a computing device  12 - 16 , the managing unit  18 , and/or the integrity processing unit  20 . 
     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 managing unit  18  creates and stores user profile information (e.g., an access control list (ACL)) in local memory and/or within memory of the DSN memory  22 . The user profile information includes authentication information, permissions, and/or the security parameters. The security parameters may include encryption/decryption scheme, one or more encryption keys, key generation scheme, and/or data encoding/decoding scheme. 
     The managing unit  18  creates billing information for a particular user, a user group, a vault access, public vault access, etc. For instance, the managing unit  18  tracks the number of times a user accesses a non-public vault and/or public vaults, which can be used to generate per-access billing information. In another instance, the managing unit  18  tracks the amount of data stored and/or retrieved by a user device and/or a user group, which can be used to generate per-data-amount billing information. 
     As another example, the managing unit  18  performs 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, storage units, and/or computing devices with a DS client module  34 ) to/from the DSN  10 , and/or establishing authentication credentials for the storage units  36 . Network operations can further include monitoring read, write and/or delete communications attempts, which attempts could be in the form of requests. Network administration includes monitoring devices and/or units for failures, maintaining vault information, determining device and/or unit activation status, determining device and/or unit loading, and/or determining any other system level operation that affects the performance level of the DSN  10 . Network maintenance includes facilitating replacing, upgrading, repairing, and/or expanding a device and/or unit of the DSN  10 . 
     To support data storage integrity verification within the DSN  10 , the integrity processing unit  20  (and/or other devices in the DSN  10  such as managing unit  18 ) may assess and perform rebuilding of ‘bad’ or missing encoded data slices. At a high level, the integrity processing unit  20  performs rebuilding by periodically attempting to retrieve/list encoded data slices, and/or slice names of the encoded data slices, from the DSN memory  22 . Retrieved encoded slices are assessed and checked for errors due to data corruption, outdated versioning, etc. If a slice includes an error, it is flagged as a ‘bad’ or ‘corrupt’ slice. Encoded data slices that are not received and/or not listed may be flagged as missing slices. Bad and/or missing slices may be subsequently rebuilt using other retrieved encoded data slices that are deemed to be good slices in order to produce rebuilt slices. A multi-stage decoding process may be employed in certain circumstances to recover data even when the number of valid encoded data slices of a set of encoded data slices is less than a relevant decode threshold number. The rebuilt slices may then be written to DSN memory  22 . Note that the integrity processing unit  20  may be a separate unit as shown, included in DSN memory  22 , included in the computing device  16 , managing unit  18 , stored on a DS unit  36 , and/or distributed among multiple storage units  36 . 
       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 ( 10 ) controller  56 , a peripheral component interconnect (PCI) interface  58 , an IO interface module  60 , at least one IO device interface module  62 , a read only memory (ROM) basic input output system (BIOS)  64 , and one or more memory interface modules. The one or more memory interface module(s) includes one or more of a universal serial bus (USB) interface module  66 , a host bus adapter (HBA) interface module  68 , a network interface module  70 , a flash interface module  72 , a hard drive interface module  74 , and a DSN interface module  76 . 
     The DSN interface module  76  functions to mimic a conventional operating system (OS) file system interface (e.g., network file system (NFS), flash file system (FFS), disk file system (DFS), file transfer protocol (FTP), web-based distributed authoring and versioning (WebDAV), etc.) and/or a block memory interface (e.g., small computer system interface (SCSI), internet small computer system interface (iSCSI), etc.). The DSN interface module  76  and/or the network interface module  70  may function as one or more of the interface  30 - 33  of  FIG. 1 . Note that the IO device interface module  62  and/or the memory interface modules  66 - 76  may be collectively or individually referred to as IO ports. 
       FIG. 3  is a schematic block diagram of an example of dispersed storage error encoding of data. When a computing device  12  or  16  has data to store it disperse storage error encodes the data in accordance with a dispersed storage error encoding process based on dispersed storage error encoding parameters. The dispersed storage error encoding parameters include an encoding function (e.g., information dispersal algorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding, non-systematic encoding, on-line codes, etc.), a data segmenting protocol (e.g., data segment size, fixed, variable, etc.), and per data segment encoding values. The per data segment encoding values include a total, or pillar width, number (T) of encoded data slices per encoding of a data segment (i.e., in a set of encoded data slices); a decode threshold number (D) of encoded data slices of a set of encoded data slices that are needed to recover the data segment; a read threshold number (R) of encoded data slices to indicate a number of encoded data slices per set to be read from storage for decoding of the data segment; and/or a write threshold number (W) to indicate a number of encoded data slices per set that must be accurately stored before the encoded data segment is deemed to have been properly stored. The dispersed storage error encoding parameters may further include slicing information (e.g., the number of encoded data slices that will be created for each data segment) and/or slice security information (e.g., per encoded data slice encryption, compression, integrity checksum, etc.). 
     In the present example, Cauchy Reed-Solomon has been selected as the encoding function (a generic example is shown in  FIG. 4  and a specific example is shown in  FIG. 5 ); the data segmenting protocol is to divide the data object into fixed sized data segments; and the per data segment encoding values include: a pillar width of 5, a decode threshold of 3, a read threshold of 4, and a write threshold of 4. In accordance with the data segmenting protocol, the computing device  12  or  16  divides the data (e.g., a file (e.g., text, video, audio, etc.), a data object, or other data arrangement) into a plurality of fixed sized data segments (e.g., 1 through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more). The number of data segments created is dependent of the size of the data and the data segmenting protocol. 
     The computing device  12  or  16  then disperse storage error encodes a data segment using the selected encoding function (e.g., Cauchy Reed-Solomon) to produce a set of encoded data slices.  FIG. 4  illustrates a generic Cauchy Reed-Solomon encoding function, which includes an encoding matrix (EM), a data matrix (DM), and a coded matrix (CM). The size of the encoding matrix (EM) is dependent on the pillar width number (T) and the decode threshold number (D) of selected per data segment encoding values. To produce the data matrix (DM), the data segment is divided into a plurality of data blocks and the data blocks are arranged into D number of rows with Z data blocks per row. Note that Z is a function of the number of data blocks created from the data segment and the decode threshold number (D). The coded matrix is produced by matrix multiplying the data matrix by the encoding matrix. 
       FIG. 5  illustrates a specific example of Cauchy Reed-Solomon encoding with a pillar number (T) of five and decode threshold number of three. In this example, a first data segment is divided into twelve data blocks (D 1 -D 12 ). The coded matrix includes five rows of coded data blocks, where the first row of X 11 -X 14  corresponds to a first encoded data slice (EDS  1 _ 1 ), the second row of X 21 -X 24  corresponds to a second encoded data slice (EDS  2 _ 1 ), the third row of X 31 -X 34  corresponds to a third encoded data slice (EDS  3 _ 1 ), the fourth row of X 41 -X 44  corresponds to a fourth encoded data slice (EDS  4 _ 1 ), and the fifth row of X 51 -X 54  corresponds to a fifth encoded data slice (EDS  5 _ 1 ). Note that the second number of the EDS designation corresponds to the data segment number. In the illustrated example, the value X 11 =aD 1 +bD 5 +cD 9 , X 12 =aD 2 +bD 6 +cD 10 , . . . X 53 =mD 3 +nD 7 +oD 11 , and X 54 =mD 4 +nD 8 +oD 12 . 
     Returning to the discussion of  FIG. 3 , the computing device also creates a slice name (SN) for each encoded data slice (EDS) in the set of encoded data slices. A typical format for a slice name  80  is shown in  FIG. 6 . As shown, the slice name (SN)  80  includes a pillar number of the encoded data slice (e.g., one of 1-T), a data segment number (e.g., one of 1-Y), a vault identifier (ID), a data object identifier (ID), and may further include revision level information of the encoded data slices. The slice name functions as at least part of a DSN address for the encoded data slice for storage and retrieval from the DSN memory  22 . 
     As a result of encoding, the computing device  12  or  16  produces a plurality of sets of encoded data slices, which are provided with their respective slice names to the storage units for storage. As shown, the first set of encoded data slices includes EDS  1 _ 1  through EDS  5 _ 1  and the first set of slice names includes SN  1 _ 1  through SN  5 _ 1  and the last set of encoded data slices includes EDS  1 _Y through EDS  5 _Y and the last set of slice names includes SN  1 _Y through SN  5 _Y. 
       FIG. 7  is a schematic block diagram of an example of dispersed storage error decoding of a data object that was dispersed storage error encoded and stored in the example of  FIG. 4 . In this example, the computing device  12  or  16  retrieves from the storage units at least the decode threshold number of encoded data slices per data segment. As a specific example, the computing device retrieves a read threshold number of encoded data slices. 
     In order to recover a data segment from a decode threshold number of encoded data slices, the computing device uses a decoding function as shown in  FIG. 8 . As shown, the decoding function is essentially an inverse of the encoding function of  FIG. 4 . The coded matrix includes a decode threshold number of rows (e.g., three in this example) and the decoding matrix in an inversion of the encoding matrix that includes the corresponding rows of the coded matrix. For example, if the coded matrix includes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2, and 4, and then inverted to produce the decoding matrix. 
       FIG. 9  is a diagram of an example of a dispersed storage network. The dispersed storage network includes a DS (dispersed storage) client module  34  (which may be in DS computing devices  12  and/or  16  of  FIG. 1 ), a network  24 , and a plurality of DS units  36 - 1  . . .  36 - n  (which may be storage units  36  of  FIG. 1  and which form at least a portion of DS memory  22  of  FIG. 1 ), a DSN managing unit  18 , and a DS integrity verification module (not shown). The DS client module  34  includes an outbound DS processing section  81  and an inbound DS processing section  82 . Each of the DS units  36 - 1  . . .  36 - n  includes a controller  86 , a processing module  84  (e.g. computer processor) including a communications interface for communicating over network  24  (not shown), memory  88 , a DT (distributed task) execution module  90 , and a DS client module  34 . 
     In an example of operation, the DS client module  34  receives 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 concerns over security and loss of data, distributed storage of 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 DS client module  34 , the outbound DS processing section  81  receives the data  92 . The outbound DS processing section  81  processes the data  92  to produce slice groupings  96 . As an example of such processing, the outbound DS processing section  81  partitions the data  92  into a plurality of data partitions. For each data partition, the outbound DS processing section  81  dispersed storage (DS) error encodes the data partition to produce encoded data slices and groups the encoded data slices into a slice grouping  96 . 
     The outbound DS processing section  81  then sends, via the network  24 , the slice groupings  96  to the DS units  36 - 1  . . .  36 - n  of the DSN memory  22  of  FIG. 1 . For example, the outbound DS processing section  81  sends slice group  1  to DS storage unit  36 - 1 . As another example, the outbound DS processing section  81  sends slice group #n to DS unit #n. 
     In one example of operation, the DS client module  34  requests retrieval of stored data within the memory of the DS units  36 . In this example, the task  94  is retrieve data stored in the DSN memory  22 . Accordingly, and according to one embodiment, the outbound DS processing section  81  converts the task  94  into a plurality of partial tasks  98  and sends the partial tasks  98  to the respective DS storage units  36 - 1  . . .  36 - n.    
     In response to the partial task  98  of retrieving stored data, a DS storage unit  36  identifies the corresponding encoded data slices  99  and retrieves them. For example, DS unit # 1  receives partial task # 1  and retrieves, in response thereto, retrieved slices # 1 . The DS units  36  send their respective retrieved slices  99  to the inbound DS processing section  82  via the network  24 . 
     The inbound DS processing section  82  converts the retrieved slices  99  into data  92 . For example, the inbound DS processing section  82  de-groups the retrieved slices  99  to produce encoded slices per data partition. The inbound DS processing section  82  then DS error decodes the encoded slices per data partition to produce data partitions. The inbound DS processing section  82  de-partitions the data partitions to recapture the data  92 . 
     In one example of operation, the DSN of  FIGS. 1 and 9  is used account for data whose rebuilding is deferred. Explanations of this process are set out below in conjunction with  FIGS. 10A and 10B . While described in the context of functionality provided by DS units  36 - 1  . . .  36 - n  and DS processing unit  16 , this function may be implemented utilizing any module and/or unit of a dispersed storage network (DSN) including the DS Managing Unit  18  and the Integrity Processing Unit  20  shown in  FIG. 1 . 
     According to one example, each DS 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. 
     For example, during operation, having received a data segment, the DS unit dispersed storage error encodes the data segment in accordance with dispersal parameters to produce a set of encoded data slices 1- n . The dispersal parameters include an information dispersal algorithm (IDA) width=n and a decode threshold k, where the decode threshold number of encoded data slices of the set of encoded data slices is required to reproduce the data segment. 
     When deferred rebuilding is applied in a dispersed storage network system, the average utilization will generally tend toward somewhere in the middle of (Width/IDA Threshold) and (Rebuild Threshold/IDA Threshold). For example, in a 10-of-16 system with a rebuild threshold of 14, there will be a roughly equal proportion of sources that have 14 slices, 15 slices, and 16 slices, with a usually negligible amount of data below 14 slices. Therefore, the average perceived expansion factor of the system is closer to (15/10) rather than (16/10) or (14/10). This excess in capacity, due to the large number of slices that are compromised (i.e. bad or missing) in the system, can lead to accounting problems if not carefully managed. For example, one might “over fill” their storage and then later rebuilding attempts may fail due to lack of capacity, or vault quotas might be exceeded by rebuilding or changing of the rebuild threshold in the future. To prevent these situations, the amount of compromised slices is determined and may be reported by each DS units, for receipt by a dispersed storage processing unit, such that the dispersed storage processing unit can use the report for purposes of quota enforcement, storage location selection, allotment enforcement, capacity planning, and other related purposes. When the number of existing slices on a DS unit plus the number of slices that need to be rebuilt (i.e. compromised: bad or missing) exceeds the storage capacity of the DS unit, then the DS unit may fail any write requests. Similarly, if the number of existing slices and the number of slices that need to be rebuilt do not exceed the storage capacity of the DS unit the DS unit may fail any write requests that would cause the DS unit to it to exceed its storage capacity. 
       FIG. 10A  is a schematic block diagram of another embodiment of a dispersed storage network (DSN) that includes a set of storage units  36 - 1  to  36 - n , the network  24  of  FIG. 1 , and the distributed storage (DS) processing unit  16  of  FIG. 1 . Each storage unit includes a respective processing module  84 - 1  to  84 - n  and at least one respective memory  88 - 1  to  88 - n . The processing module may be implemented utilizing the processing module  84  of  FIG. 9  and the memory may be implemented utilizing the memory  88  of  FIG. 9 . The storage unit may be implemented utilizing the DS execution unit  36  of  FIG. 1 . The DSN functions to account for data whose rebuilding is deferred. 
     As an example of accounting for data whose rebuilding is deferred generating the storage structure, the DS units  36 - 1 ,  36 - 2  . . .  36 - n  have existing dispersed storage error encoded data slices  502 - 1 ,  502 - 2  . . .  502 - n  and compromised (e.g. bad or missing) dispersed storage error encoded data slices  504 - 1 ,  504 - 2  . . .  504 - n  associated therewith. According to one example, the DS units  36 - 1 ,  36 - 2  . . .  36 - n  determine the respective number of compromised dispersed storage error encoded data slices and the respective number of existing slices associated with their respective memory  88 - 1 ,  88 - 2  . . .  88 - n . The respective processing modules  84 - 1 ,  84 - 2  . . .  84 - n  of DS units  36 - 1 ,  36 - 2  . . .  36 - n  then determine whether the respective number of compromised slices plus existing slices exceeds a respective storage capacity in order to produce a capacity determination. Based on the capacity determinations a respective DS unit will then fail write requests that would cause the DS unit to exceed its respective storage capacity when taking into account the respective existing and compromised slices associated therewith. The DS units  36 - 1 ,  36 - 2  . . .  36 - n  may also send respective compromised slice reports  506 - 1 ,  506 - 2  . . .  506 - n  including, but not limited to, an indication of the respective number of compromised slices  504 - 1 ,  504 - 2 . . .  504 - n  associated with the respective DS unit. DS processing unit  16  can then use the reports for purposes of quota enforcement, storage location selection, allotment enforcement, capacity planning and other related purposes. 
       FIG. 10B  is a flowchart illustrating an example of accounting for data whose rebuilding is deferred. The method includes a step  600  where a processing module  84  (e.g., of a distributed storage (DS) unit  36 ) determines a number of compromised slices associated with the DS unit. 
     The method continues at a step  602  where the processing module  84  determines a number of existing slices associated with the DS unit. 
     The method continues at a step  604  where the processing module  84  compares the number of compromised slices plus the number of existing slices associated with the dispersed storage unit to a storage capacity, in order to produce a capacity determination. Alternatively, in one example of operation a comparison of whether the number of compromised slices plus the number of existing slices associated with the dispersed storage unit exceeds a storage capacity is performed. 
     The method continues at the step  606  where the processing module may fail write requests directed towards the dispersed storage unit based on the capacity determination. For example, the processing module may fail write requests, when a write request would cause the dispersed storage unit to exceed the storage capacity of the dispersed storage unit taking into consideration existing slices as well as compromised slices that need to be rebuilt. 
     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) “configured to”, “operably coupled to”, “coupled to”, and/or “coupling” includes direct coupling between items and/or indirect coupling between items via an intervening item (e.g., an item includes, but is not limited to, a component, an element, a circuit, and/or a module) where, for an example of indirect coupling, the intervening item does not modify the information of a signal but may adjust its current level, voltage level, and/or power level. As may further be used herein, inferred coupling (i.e., where one element is coupled to another element by inference) includes direct and indirect coupling between two items in the same manner as “coupled to”. As may even further be used herein, the term “configured to”, “operable to”, “coupled to”, or “operably coupled to” indicates that an item includes one or more of power connections, input(s), output(s), etc., to perform, when activated, one or more its corresponding functions and may further include inferred coupling to one or more other items. As may still further be used herein, the term “associated with”, includes direct and/or indirect coupling of separate items and/or one item being embedded within another item. 
     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 A has a greater magnitude than signal B, a favorable comparison may be achieved when the magnitude of signal A is greater than that of signal B or when the magnitude of signal B is less than that of signal A. As may be used herein, the term “compares unfavorably”, indicates that a comparison between two or more items, signals, etc., fails to provide the desired relationship. 
     As may also be used herein, the terms “processing module”, “processing circuit”, “processor”, and/or “processing unit” may be a single processing device or a plurality of processing devices. Such a processing device may be a microprocessor, micro-controller, digital signal processor, microcomputer, central processing unit, field programmable gate array, programmable logic device, state machine, logic circuitry, analog circuitry, digital circuitry, and/or any device that manipulates signals (analog and/or digital) based on hard coding of the circuitry and/or operational instructions. The processing module, module, processing circuit, and/or processing unit may be, or further include, memory and/or an integrated memory element, which may be a single memory device, a plurality of memory devices, and/or embedded circuitry of another processing module, module, processing circuit, and/or processing unit. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. Note that if the processing module, module, processing circuit, and/or processing unit includes more than one processing device, the processing devices may be centrally located (e.g., directly coupled together via a wired and/or wireless bus structure) or may be distributedly located (e.g., cloud computing via indirect coupling via a local area network and/or a wide area network). Further note that if the processing module, module, processing circuit, and/or processing unit implements one or more of its functions via a state machine, analog circuitry, digital circuitry, and/or logic circuitry, the memory and/or memory element storing the corresponding operational instructions may be embedded within, or external to, the circuitry comprising the state machine, analog circuitry, digital circuitry, and/or logic circuitry. Still further note that, the memory element may store, and the processing module, module, processing circuit, and/or processing unit executes, hard coded and/or operational instructions corresponding to at least some of the steps and/or functions illustrated in one or more of the Figures. Such a memory device or memory element can be included in an article of manufacture. 
     One or more embodiments have been described above with the aid of method steps illustrating the performance of specified functions and relationships thereof. The boundaries and sequence of these functional building blocks and method steps have been arbitrarily defined herein for convenience of description. Alternate boundaries and sequences can be defined so long as the specified functions and relationships are appropriately performed. Any such alternate boundaries or sequences are thus within the scope and spirit of the claims. Further, the boundaries of these functional building blocks have been arbitrarily defined for convenience of description. Alternate boundaries could be defined as long as the certain significant functions are appropriately performed. Similarly, flow diagram blocks may also have been arbitrarily defined herein to illustrate certain significant functionality. 
     To the extent used, the flow diagram block boundaries and sequence could have been defined otherwise and still perform the certain significant functionality. Such alternate definitions of both functional building blocks and flow diagram blocks and sequences are thus within the scope and spirit of the claims. One of average skill in the art will also recognize that the functional building blocks, and other illustrative blocks, modules and components herein, can be implemented as illustrated or by discrete components, application specific integrated circuits, processors executing appropriate software and the like or any combination thereof. 
     In addition, a flow diagram may include a “start” and/or “continue” indication. The “start” and “continue” indications reflect that the steps presented can optionally be incorporated in or otherwise used in conjunction with other routines. In this context, “start” indicates the beginning of the first step presented and may be preceded by other activities not specifically shown. Further, the “continue” indication reflects that the steps presented may be performed multiple times and/or may be succeeded by other activities not specifically shown. Further, while a flow diagram indicates a particular ordering of steps, other orderings are likewise possible provided that the principles of causality are maintained. 
     The one or more embodiments are used herein to illustrate one or more aspects, one or more features, one or more concepts, and/or one or more examples. A physical embodiment of an apparatus, an article of manufacture, a machine, and/or of a process may include one or more of the aspects, features, concepts, examples, etc. described with reference to one or more of the embodiments discussed herein. Further, from Figure to Figure, the embodiments may incorporate the same or similarly named functions, steps, modules, etc. that may use the same or different reference numbers and, as such, the functions, steps, modules, etc. may be the same or similar functions, steps, modules, etc. or different ones. 
     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 one or more of the embodiments. A module implements one or more functions via a device such as a processor or other processing device or other hardware that may include or operate in association with a memory that stores operational instructions. A module may operate independently and/or in conjunction with software and/or firmware. As also used herein, a module may contain one or more sub-modules, each of which may be one or more modules. 
     As may further be used herein, a computer readable memory includes one or more memory elements. A memory element may be a separate memory device, multiple memory devices, or a set of memory locations within a memory device. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. The memory device may be in a form a solid state memory, a hard drive memory, cloud memory, thumb drive, server memory, computing device memory, and/or other physical medium for storing digital information. A computer readable memory/storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     While particular combinations of various functions and features of the one or more embodiments have been expressly described herein, other combinations of these features and functions are likewise possible. The present disclosure is not limited by the particular examples disclosed herein and expressly incorporates these other combinations.