Patent Publication Number: US-10789128-B2

Title: External healing mode for a dispersed storage network memory

Description:
CROSS REFERENCE TO RELATED PATENTS 
     The present U.S. Utility patent application also claims priority pursuant to 35 U.S.C. § 120, as a continuation-in-part (CIP) of U.S. Utility patent application Ser. No. 15/075,946, entitled “RE-ENCODING DATA IN A DISPERSED STORAGE NETWORK,” filed Mar. 21, 2016, which claims priority pursuant to 35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/168,114, entitled “RE-ENCODING DATA IN A DISPERSED STORAGE NETWORK,” 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 dispersing error encoded 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. 
     Data storage systems can be deleteriously affected and sometime even lose data. Prior art data storage systems do not provide acceptably effective means by which lost data can be recovered. There continues to be significant room for improvement by which lost data can be recovered and by which the data storage systems operate. 
    
    
     
       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 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 schematic block diagram of an example of dispersed storage error encoding of data in accordance with the present invention; 
         FIG. 4  is a schematic block diagram of a generic example of an error encoding function in accordance with the present invention; 
         FIG. 5  is a schematic block diagram of a specific example of an error encoding function in accordance with the present invention; 
         FIG. 6  is a schematic block diagram of an example of a slice name of an encoded data slice (EDS) in accordance with the present invention; 
         FIG. 7  is a schematic block diagram of an example of dispersed storage error decoding of data in accordance with the present invention; 
         FIG. 8  is a schematic block diagram of a generic example of an error decoding function in accordance with the present invention; 
         FIG. 9A  is a diagram of an example of a storage unit (SU) within the distributed computing system performing a distributed storage and task processing operation; 
         FIG. 9B  is a schematic block diagram of another embodiment of a distributed computing system in accordance with the present invention; and 
         FIG. 10  is a flowchart illustrating an example of selecting a storage error abatement function in accordance with the present invention. 
     
    
    
     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 computing devices  12 - 16 , a managing unit  18 , an integrity processing unit  20 , and a DSN memory  22 . The components of the DSN  10  are coupled to a network  24 , which may include one or more wireless and/or wire lined communication systems; one or more non-public intranet systems and/or public internet systems; and/or one or more local area networks (LAN) and/or wide area networks (WAN). 
     The DSN memory  22  includes a plurality of storage units  36  that may be located at geographically different sites (e.g., one in Chicago, one in Milwaukee, etc.), at a common site, or a combination thereof. For example, if the DSN memory  22  includes eight storage units  36 , each storage unit is located at a different site. As another example, if the DSN memory  22  includes eight storage units  36 , all eight storage units are located at the same site. As yet another example, if the DSN memory  22  includes eight storage units  36 , a first pair of storage units are at a first common site, a second pair of storage units are at a second common site, a third pair of storage units are at a third common site, and a fourth pair of storage units are at a fourth common site. Note that a DSN memory  22  may include more or less than eight storage units  36 . Further note that each storage unit  36  includes a computing core (as shown in  FIG. 2 , or components thereof) and a plurality of memory devices for storing dispersed error encoded data. 
     Each of the computing devices  12 - 16 , the managing unit  18 , and the integrity processing unit  20  include a computing core  26 , which includes network interfaces  30 - 33 . Computing devices  12 - 16  may each be a portable computing device and/or a fixed computing device. A portable computing device may be a social networking device, a gaming device, a cell phone, a smart phone, a digital assistant, a digital music player, a digital video player, a laptop computer, a handheld computer, a tablet, a video game controller, and/or any other portable device that includes a computing core. A fixed computing device may be a computer (PC), a computer server, a cable set-top box, a satellite receiver, a television set, a printer, a fax machine, home entertainment equipment, a video game console, and/or any type of home or office computing equipment. Note that each of the managing unit  18  and the integrity processing unit  20  may be separate computing devices, may be a common computing device, and/or may be integrated into one or more of the computing devices  12 - 16  and/or into one or more of the storage units  36 . 
     Each interface  30 ,  32 , and  33  includes software and hardware to support one or more communication links via the network  24  indirectly and/or directly. For example, interface  30  supports a communication link (e.g., wired, wireless, direct, via a LAN, via the network  24 , etc.) between computing devices  14  and  16 . As another example, interface  32  supports communication links (e.g., a wired connection, a wireless connection, a LAN connection, and/or any other type of connection to/from the network  24 ) between computing devices  12  &amp;  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 . 
     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 as subsequently described with reference to one or more of  FIGS. 3-8 . In this example embodiment, computing device  16  functions as a dispersed storage processing agent for computing device  14 . In this role, computing device  16  dispersed storage error encodes and decodes data on behalf of computing device  14 . With the use of dispersed storage error encoding and decoding, the DSN  10  is tolerant of a significant number of storage unit failures (the number of failures is based on parameters of the dispersed storage error encoding function) without loss of data and without the need for a redundant or backup copies of the data. Further, the DSN  10  stores data for an indefinite period of time without data loss and in a secure manner (e.g., the system is very resistant to unauthorized attempts at accessing the data). 
     In operation, the managing unit  18  performs DS management services. For example, the managing unit  18  establishes distributed data storage parameters (e.g., vault creation, distributed storage parameters, security parameters, billing information, user profile information, etc.) for computing devices  12 - 14  individually or as part of a group of user devices. As a specific example, the managing unit  18  coordinates creation of a vault (e.g., a virtual memory block associated with a portion of an overall namespace of the DSN) within the DSN memory  22  for a user device, a group of devices, or for public access and establishes per vault dispersed storage (DS) error encoding parameters for a vault. The managing unit  18  facilitates storage of DS error encoding parameters for each vault by updating registry information of the DSN  10 , where the registry information may be stored in the DSN memory  22 , a computing device  12 - 16 , the managing unit  18 , and/or the integrity processing unit  20 . 
     The DSN 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 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 DSN managing unit  18  creates billing information for a particular user, a user group, a vault access, public vault access, etc. For instance, the DSN 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 a per-access billing information. In another instance, the DSN 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. 
     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 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 . 
     The integrity processing unit  20  performs 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 . 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 the DSN memory  22 . 
       FIG. 2  is a schematic block diagram of an embodiment of a computing core  26  that includes a processing module  50 , a memory controller  52 , main memory  54 , a video graphics processing unit  55 , an input/output (IO) controller  56 , a peripheral component interconnect (PCI) interface  58 , an IO interface module  60 , at least one IO device interface module  62 , a read only memory (ROM) basic input output system (BIOS)  64 , and one or more memory interface modules. The one or more memory interface module(s) includes one or more of a universal serial bus (USB) interface module  66 , a host bus adapter (HBA) interface module  68 , a network interface module  70 , a flash interface module  72 , a hard drive interface module  74 , and a DSN interface module  76 . 
     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. 
     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  60  is shown in  FIG. 6 . As shown, the slice name (SN)  60  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. 
     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. 
     A dispersed or distributed storage network (DSN) module includes a plurality of distributed storage and/or task (DST) execution units  36  (e.g., storage units (SUs), computing devices, etc.) 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. 
       FIG. 9A  is a diagram of an example  901  of the distributed computing system performing a distributed storage and task processing operation. The distributed computing system may be implemented to include a plurality of storage units (SUs) such as may include two or more storage units (SUs)  36 - 1  such as with respect to (which form at least a portion of DSN memory  22  such as with respect to  FIG. 1 ). Each of the SUs  1 -n (shown as SU  36 - 1  in  FIG. 9A ) includes a controller  86 , a processing module  84 , memory  88 , a DT (distributed task) execution module  90 , and a DS client module  34 . The memory  88  is of sufficient size to store a significant number of encoded data slices (EDSs) (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.). Note that terms such as EDS or slice (or EDSs or slices) may be used interchangeably herein in various examples. 
       FIG. 9B  is a schematic block diagram  902  of another embodiment of a dispersed or distributed storage network (DSN) that includes a storage set, the network  24  of  FIG. 1 , and the integrity processing unit  20  of  FIG. 1 . Note that the integrity processing unit  20  may be implemented as a variant of the computing device  16  in some examples; in general, any computing device, SU, etc. as described herein may be implemented to perform the operations and support the functionality of an integrity processing unit  20  in operation such as with respect to  FIG. 9B . Alternatively, the integrity processing unit  20  may be implemented utilizing one or more of the DST processing unit  16  of  FIG. 1  and a storage unit (SU)  36  of  FIG. 1 . The storage set includes a set of storage units (SUs)  1 - 5  (shown as storage set  910 ). Each SU includes the memory  88  such as described with respect to  FIG. 9A . Each SU may be implemented utilizing the storage unit (SU)  36  of  FIG. 1 . Hereafter, each SU may be interchangeably referred to as a storage unit and the storage set may be interchangeably referred to as a set of storage units. The DSN functions to select a storage error abatement function. 
     In an example of operation of the selection of the storage error abatement function, the integrity processing unit  20  detects a storage error associated with one or more sets of a plurality of sets of stored EDSs of a store data object, where the data object is divided into a plurality of S data segments, and where each data segment is dispersed storage error encoded to produce a set of EDSs of the plurality of sets of EDSs for storage in the set of SUs  1 - 5 . For example, the integrity processing unit  20  interprets slice status information  1 - 5  received, via the network  24 , from the SUs  1 - 5 , to identify EDSs associated with the storage error. As another example, the integrity processing unit  20  interprets an error message. As yet another example, the integrity processing unit  20  receives a rebuilding request. 
     For each set of EDSs associated with the detected storage error, the integrity processing unit  20  determines and availability&#39;s dataset each EDS. For example, the integrity processing unit  20  counts a number of available EDSs based on the slice status information  1 - 5 . As another example, the integrity processing unit  20  counts a number of unavailable EDSs based on slice status information  1 - 5 . 
     When the set of EDSs associated with the detected storage error includes at least a decode threshold number of available EDSs, the integrity processing unit  20  and initiates a rebuilding function to abate the detected storage error. For example, for each available EDS, the integrity processing unit  20  facilitates utilization of the rebuilding function (e.g., decode a decode threshold number of available EDSs to produce a recovered data segment, re-encode the recovered data segment to produce a rebuilt EDS) to produce a rebuilt EDS, and for each rebuilt EDS, facilitate replacement of an unavailable EDS with a corresponding rebuilt EDS. 
     When the set of EDSs associated with the detected storage error includes less than the at least a decode threshold number of available EDSs, the integrity processing unit  20  initiates a slice repair function to abate the detected storage error. For example, for each unavailable EDS, the integrity processing unit  20  facilitates utilization of the slice repair function (e.g., issue slice repair requests  1 - 3  to the associated storage units) to produce a repaired EDS, and for each repaired EDS, facilitate replacement of an unavailable EDS with a corresponding repaired EDS, where a storage unit performs the slice repair function. The performing of the slice repair function by the storage unit includes one or more of performing a filesystem repair operation, performing a memory recovery technique, and performing an individual data block rebuilding of an EDS. 
     In an example of operation and implementation, a computing device includes an interface configured to interface and communicate with a dispersed or distributed storage network (DSN), a memory that stores operational instructions, and a processing module operably coupled to the interface and memory such that the processing module, when operable within the computing device based on the operational instructions, is configured to perform various operations. 
     In an example, based on a detected storage error, the computing device is configured to determine availability status of encoded data slices (EDSs) within a set of EDSs stored within one or more storage units (SUs) within the DSN. Note that a data object is segmented into a plurality of data segments, and a data segment of the plurality of data segments is dispersed error encoded in accordance with dispersed error encoding parameters to produce the set of EDSs. Then, when at least a threshold number of EDSs of the set of EDSs are available based on the availability status that is determined, the computing device is configured to initiate a rebuilding function to abate the detected storage error. Alternatively, when less than the threshold number of EDSs of the set of EDSs are available based on the availability status that is determined, the computing device is configured to initiate a slice repair function to at least one SU of the one or more SUs to abate the detected storage error. 
     In some examples, the computing device is further configured to initiate the rebuilding function to abate the detected storage error including to facilitate the rebuilding function using the at least a threshold number of EDSs of the set of EDSs to produce a recovered data segment. Then, the computing device is configured to dispersed error encode the recovered data segment to produce one or more rebuilt EDSs and facilitate replacement of one or more missing EDSs within the set of EDS with the one or more rebuilt EDSs within the one or more SUs within the DSN. 
     In even other examples, the computing device is further configured to initiate the slice repair function to the at least one SU of the one or more SUs to abate the detected storage error including to issue a slice repair request to at least one SU of the one or more SUs to direct the at least one SU of the one or more SUs to produce at least one repaired EDS. Then, for the at least one repaired EDS that is successfully generated by the at least one SU of the one or more SUs, the computing device is configured to facilitate replacement of an unavailable EDSs with the repaired EDS that is produced by the at least one SU of the one or more SUs. In some examples, the at least one SU of the one or more SUs configured to perform at least one of a filesystem repair operation, a memory recovery technique, and an individual data block rebuilding of an EDS to produce the repaired EDS based on the slice repair request received by the SU of the one or more SUs that directs the SU of the one or more SUs to produce the repaired EDS. 
     Also, within some examples, note that the set of EDSs is of pillar width, and the set of EDSs are distributedly stored among a plurality of SUs within the DSN. Also, the threshold number of EDSs includes at least one of a decode threshold number of EDSs, a read threshold number of EDSs, or a write threshold number of EDSs. Note that the decode threshold number of EDSs are needed to recover the data segment, and the read threshold number of EDSs provides for reconstruction of the data segment. Also, the write threshold number of EDSs provides for a successful transfer of the set of EDSs from a first at least one location in the DSN to a second at least one location in the DSN. 
     Note that the computing device may be located at a first premises that is remotely located from at least one SU of the one or more SUs within the DSN. Also, note that the computing device may be of any of a variety of types of devices as described herein and/or their equivalents including an integrity processing unit, a SU of the one or more SUs within the DSN, a wireless smart phone, a laptop, a tablet, a personal computers (PC), a work station, and/or a video game device. Note also that the DSN may be implemented to include or be based on any of a number of different types of communication systems including a wireless communication system, a wire lined communication systems, a non-public intranet system, a public internet system, a local area network (LAN), and/or a wide area network (WAN). 
       FIG. 10  is a flowchart illustrating an example of selecting a storage error abatement function. The method  1000  begins or continues at a step  1010  where a processing module (e.g., of a distributed storage and task (DST) integrity processing unit), for each set of EDSs associated with a detected storage error, determines an availability status of each EDS of the set of EDSs, where data is dispersed storage error encoded to produce a plurality of sets of EDSs that includes the set of EDSs. The determining includes one or more of interpreting slice status information, interpreting an error message, receiving a rebuilding request, counting the number of available EDSs, and counting a number of unavailable EDSs. The method  1000  branches to the step  1030  where the processing module initiates a slice repair function when the set of EDSs associated with the detected storage error includes less than the at least a decode threshold number of available EDSs. The method  1000  continues to the next step  1020  when the set of EDSs associated with the detected storage error includes at least the decode threshold number of available EDSs. 
     When the set of EDSs associated with the detected storage error includes the at least a decode threshold number of available EDSs, the method  1000  continues at the step  1020  where the processing module initiates a rebuilding function to abate the detected storage error. The initiating includes, for each unavailable EDS, the processing module facilitating utilization of the rebuilding function to produce a rebuilt EDS, and for each rebuilt EDS, replaces an unavailable EDS with a corresponding rebuilt EDS. For example, the processing module decodes a decode threshold number of available EDSs to produce a recovered data segment, re-encodes the recovered data segment to produce a rebuilt EDS, and sends the rebuilt EDS to a storage unit associated with the unavailable EDS for storage. 
     When a set of EDSs associated with the detected storage error includes less than the at least a decode threshold number of available EDSs, the method  1000  continues at the step  1030  where the processing module initiates a slice repair function to abate the detected storage error. The initiating includes, for each unavailable EDS, facilitating utilization of the slice repair function to produce a repaired EDS, and for each repaired EDS, replacing an unavailable EDS with a corresponding repaired EDS, where a storage unit performs the slice repair function. For example, the processing module issues slice repair requests to associated storage units to initiate the slice repair function. 
     In an example of operation, a variant of the method  1000  is for execution by a computing device operates by performing certain operations. For example, based on a detected storage error, the variant of the method  1000  operates by determining availability status of encoded data slices (EDSs) within a set of EDSs stored within one or more storage units (SUs) within a dispersed or distributed storage network (DSN). Note that a data object is segmented into a plurality of data segments, and a data segment of the plurality of data segments is dispersed error encoded in accordance with dispersed error encoding parameters to produce the set of EDSs. When at least a threshold number of EDSs of the set of EDSs are available based on the availability status that is determined, the variant of the method  1000  operates by initiating (e.g., via an interface configured to interface and communicate with the DSN) a rebuilding function to abate the detected storage error. Alternatively, when less than the threshold number of EDSs of the set of EDSs are available based on the availability status that is determined, the variant of the method  1000  operates by initiating (e.g., via the interface configured to interface and communicate with the DSN) a slice repair function to at least one SU of the one or more SUs to abate the detected storage error. 
     In some examples, the variant of the method  1000  operates by the initiating the rebuilding function to abate the detected storage error operates by including facilitating the rebuilding function using the at least a threshold number of EDSs of the set of EDSs to produce a recovered data segment. The variant of the method  1000  then operates by dispersed error encoding the recovered data segment to produce one or more rebuilt EDSs and facilitating replacement of one or more missing EDSs within the set of EDS with the one or more rebuilt EDSs within the one or more SUs within the DSN. 
     In some other examples, the variant of the method  1000  operates by initiating the slice repair function to the at least one SU of the one or more SUs to abate the detected storage error by including issuing a slice repair request to at least one SU of the one or more SUs to direct the at least one SU of the one or more SUs to produce at least one repaired EDS. For the at least one repaired EDS that is successfully generated by the at least one SU of the one or more SUs, the variant of the method  1000  then operates by facilitating replacement of an unavailable EDSs with the repaired EDS that is produced by the at least one SU of the one or more SUs. Also, in some other variants of the method  1000 , the at least one SU of the one or more SUs is configured to perform at least one of a filesystem repair operation, a memory recovery technique, and an individual data block rebuilding of an EDS to produce the repaired EDS based on the slice repair request received by the SU of the one or more SUs that directs the SU of the one or more SUs to produce the repaired EDS. 
     Also, in even other examples of variants of the method  1000 , note that the set of EDSs is of pillar width, and the set of EDSs are distributedly stored among a plurality of SUs within the DSN. Also, the threshold number of EDSs includes at least one of a decode threshold number of EDSs, a read threshold number of EDSs, or a write threshold number of EDSs. Note that the decode threshold number of EDSs are needed to recover the data segment, and the read threshold number of EDSs provides for reconstruction of the data segment. Also, the write threshold number of EDSs provides for a successful transfer of the set of EDSs from a first at least one location in the DSN to a second at least one location in the DSN. 
     This disclosure presents, among other things, various examples by which healing operations may be performed within a dispersed or distributed storage network (DSN) including to perform external healing to repair, replace, rebuild, etc. one or more missing EDSs. 
     For example, in normal situations, a rebuild module (e.g., such as may be included in a computing device, a storage unit (SU), and/or any other device in a dispersed or distributed storage network (DSN)) can remove encoded data slices (EDSs) associated with sources that are below an information dispersal algorithm (IDA) threshold, and hence unrecoverable. However, the view of which EDSs are ultimately permanently lost and which are not can be blurred. For example, a SU that suffered filesystem corruption may not believe a certain EDS file is still present, but advanced filesystem repair and recovery techniques may ultimately restore the EDS. In another example, a hard drive that failed for mechanical reasons may be repaired, ultimately allowing EDSs stored on it to be recovered. In an unhealthy system, that contains some sources near or at IDA threshold, the mistaken conclusion of a source&#39;s inability to perform recovery can be exacerbated by pro-active rebuild modules cleaning up data it presumes to not be recoverable. To prevent these situations, the concept of an External Healing Mode (EHM) may be introduced to rebuild modules. When a DSN memory is in a state where data loss events have occurred, are probable, or expected, then EHM may be activated. When in this mode, the threshold for removing EDSs by the rebuild module is lowered from a first specified threshold (e.g., IDA threshold−1) to some other lower number. This number may be set close to 0 to completely disable this function. This mode comes at the cost of wasted space for any incomplete delete, but comes with the benefit that the rebuilder will not complicate other external methods to heal and recover the system (e.g., sending memory devices in for advanced recovery techniques to be applied). When the external healing methods are stopped, or when the system is returned to a healthy state, then the clean-up threshold may be raised, up to the maximum of another specified threshold (e.g., IDA threshold−1). 
     It is noted that terminologies as may be used herein such as bit stream, stream, signal sequence, etc. (or their equivalents) have been used interchangeably to describe digital information whose content corresponds to any of a number of desired types (e.g., data, video, speech, audio, etc. any of which may generally be referred to as ‘data’). 
     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 1 has a greater magnitude than signal 2, a favorable comparison may be achieved when the magnitude of signal 1 is greater than that of signal 2 or when the magnitude of signal 2 is less than that of signal 1. As may be used herein, the term “compares unfavorably”, indicates that a comparison between two or more items, signals, etc., fails to provide the desired relationship. 
     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. 
     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.