Patent Publication Number: US-2023153033-A1

Title: Processing Access Anomalies in a Storage Network

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 of U.S. Utility application Ser. No. 17/301,470, entitled “Resolving Detected Access Anomalies in a Vast Storage Network”, filed Apr. 5, 2021, which is a continuation of U.S. Utility application Ser. No. 16/554,939, entitled “Selecting A Subset Of Storage Units In A Dispersed Storage Network”, filed Aug. 29, 2019, issued as U.S. Pat. No. 10,996,895 on May 4, 2021, which is a continuation of U.S. Utility application Ser. No. 15/841,863, entitled “Selecting Storage Units In A Dispersed Storage Network”, filed Dec. 14, 2017, issued as U.S. Pat. No. 10,437,515 on Oct. 8, 2019, which is a continuation-in-part of U.S. Utility application Ser. No. 15/837,705, entitled “Adding Incremental Storage Resources In A Dispersed Storage Network”, filed Dec. 11, 2017, issued as U.S. Pat. No. 10,387,070 on Aug. 20, 2019, which is a continuation-in-part of U.S. Utility application Ser. No. 15/006,735, entitled “Modifying Storage Capacity Of A Set Of Storage Units”, filed Jan. 26, 2016, issued as U.S. Pat. No. 10,079,887 on Sep. 18, 2018, which claims priority pursuant to 35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/140,861, entitled “Modifying Storage Capacity Of A Storage Unit Pool”, filed Mar. 31, 2015, all of which are hereby incorporated herein by reference in their entirety and made part of the present U.S. Utility patent application for all purposes. 
    
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT 
     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. 
    
    
     
       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.  9    is a schematic block diagram of an embodiment of a dispersed or distributed storage network (DSN) in accordance with the present invention; 
         FIG.  10    is a logic diagram of an example of a method of selecting storage units in accordance with the present invention; 
         FIG.  11    is a schematic block diagram of another embodiment of a dispersed storage network (DSN) in accordance with the present invention; and 
         FIG.  12    is a flowchart illustrating an example of resolving a detected access anomaly 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. 
     In various embodiments, each of the storage units operates as a distributed storage and task (DST) execution unit, and 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. Hereafter, a storage unit may be interchangeably referred to as a dispersed storage and task (DST) execution unit and a set of storage units may be interchangeably referred to as a set of DST execution units. 
     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 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 . In various embodiments, computing devices  12 - 16  can include user devices and/or can be utilized by a requesting entity generating access requests, which can include requests to read or write data to storage units in the DSN. 
     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 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 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  10  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. Here, the computing device stores data object  40 , which can include a file (e.g., text, video, audio, etc.), or other data arrangement. The dispersed storage error encoding parameters include an encoding function (e.g., information dispersal algorithm (IDA), 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 data object  40  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  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. 
     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 schematic block diagram of another embodiment of a dispersed storage network (DSN) that includes the computing device  16  of  FIG.  1   , the network  24  of  FIG.  1   , and a DST execution (EX) unit pool  490 . The computing device  16  includes the DS client module  34  of  FIG.  1   . The DST execution unit pool  490  includes a set of at least W number of DST execution units (e.g., DST execution units  1 -W). Each DST execution unit can include a memory  84 , which can be implemented utilizing the memory  54  of  FIG.  2    or another memory device. Each DST execution may be implemented utilizing the storage unit  36  of  FIG.  1   . 
     The DSN functions to select storage units for storage of sets of encoded data slices  494 , where a data object is dispersed storage error encoded utilizing an information dispersal algorithm (IDA) to produce a plurality of sets of encoded data slices  494 , where each set of encoded data slices includes an IDA width number=W of encoded data slices, and where a write threshold number=S of encoded data slices of each set of encoded data slices are stored and maintained in accordance with the rebuilding function (e.g., from time to time, missing and/or corrupted encoded data slices are replaced with rebuilt encoded data slices to maintain the write threshold number of encoded data slices for each set of encoded data slices). 
     In a write-all configuration where there are a width number of slices to be written, and a width number of possible locations to write them, there may be no possible optimization for selecting the write location, as there is only one possibility. However, when the number of slices to be written (S) is less than the number of possible storage locations (W), there are (W choose S) possible combinations for how to select a unique S locations out of the W possible, where the (W choose S) function is defined as (W!/((S!*(W−S)!)), where ! is the factorial operator. 
     Write ranking can be used by a computing device to select the optimal single choice out of the (W choose S) possible patterns of issuing writes. The selection process can be optimized according to historical performance, storage unit utilization, storage unit response times, storage utilization of storage units, and/or round-robin style load-leveling, among other possibilities. Once the computing device makes the selection of which S storage units to issues write requests to, it can submit the write requests to the selected S storage units. 
     In an example of operation of the selecting of the storage units, the DS client module  34  obtains resource information  498  for at least some DST execution units of the DST execution unit pool  490 . The resource information  498  can include one or more of a storage unit access latency level, a storage unit processing capability level, a storage unit utilization level, an available storage capacity, a storage unit availability level, and/or a storage unit reliability level. The obtaining can include at least one of interpreting a query response, interpreting an error message, interpreting a test result, interpreting a historical log of performance, and/or receiving the resource information. For example, the DS client module  34  receives, via the network  24 , the resource information  498  from the DST execution units  1 -W. 
     Having obtained the resource information  498 , the DS client module  34  can receive a store data request  492 . The store data request  492  can include one or more of a data object for storage, a store data request indicator, and/or a name that corresponds to the data object for storage. Having received the store data request  492 , the DS client module  34  can identify W available DST execution units of the DST execution unit pool  490  as candidates for utilization of storing of at least some encoded data slices of each set of encoded data slices. The identifying can be based on one or more of the resource information  498 , an interpretation of an error message, and/or an interpretation of a status query response. For example, the DS client module  34  selects all of DST execution units  1 -W when each DST execution unit is associated with favorable resource information (e.g., performance greater than a minimal performance threshold level). 
     Having identified the available DST execution units, the DS client module  34  identifies a plurality of combinations of selecting S number of DST execution units of the W available DST execution units. For example, the DS client module  34  identifies the plurality of combinations utilizing the formula, combination=W choose S=(W!/((S!*(W−S)!)). For each combination, the DS client module  34  assigns a rating level based on the resource information. The assigning includes interpreting the resource information associated with the selection of S DST execution units to produce the rating level. For example, a more favorable rating level is assigned when a more favorable performance level is associated with the particular combination of DST execution units. As another example, a more favorable rating is assigned when a more favorable storage capacity level is associated with the particular combination of DST execution units. 
     Having assigned the rating levels to each of the combinations, the DS client module  34  can select one combination based on one or more of the rating levels, a round-robin approach, a load leveling approach, a distributed agreement protocol function ranking, a predetermination, and/or a request. For example, the client module  34  selects a highest rating level. As another example, the DS client module  34  utilizes the round-robin approach when two or more combinations are associated with a common highest rating level. In various embodiments, the DS client module identifies a plurality of permutations of selecting the S number of DST execution units of the W available DST execution units, and selects from the plurality of permutations in the same fashion of selecting from the plurality of combinations. 
     Having selected the one combination, the DS client module  34  can facilitate storage of the data object of the store data request as a plurality of sets of encoded data slices in DST execution units associated with the selected combination, where S of the encoded data slices for each set of encoded data slices are stored in the associated DST execution units (e.g., S number) of the selected combination. For example, the DS client module  34  dispersed storage error encodes the data object to produce the plurality of sets of encoded data slices, selects S encoded data slices from each set of encoded data slices, and issues, via the network  24 , a write slice request  496  to each corresponding DST execution unit of the selected S storage units, where the write slice requests  496  includes the selected S encoded data slices  494  from each set of encoded data slices. 
     In various embodiments, a processing system of a computing device includes at least one processor and a memory that stores operational instructions, that when executed by the at least one processor cause the processing system to obtain resource information for a subset of storage units of a storage unit pool. W available storage units of the storage unit pool are identified in response to receiving a store data request. W choose S combinations of selecting S number of storage units of the W available storage units are identified. A plurality of rating levels is calculated based on the resource information, where each of the plurality of rating levels are assigned to a corresponding combination of the W choose S combinations. One combination of the W choose S combinations is selected based on the plurality of rating levels. Storage of data of the store data request is facilitated utilizing the S number of storage units of the selected one combination. 
     In various embodiments, obtaining the resource information includes interpreting a performance log. In various embodiments, identifying the W available storage units is based on the resource information. In various embodiments, calculating the plurality of rating levels is based on access performance metrics and storage availability metrics of the resource information. 
     In various embodiments, selecting the one combination includes identifying a highest rated combination of the W choose S combinations. In various embodiments, selecting the one combination includes selecting from a plurality of highest rated combinations utilizing one of a random selection and/or a round-robin selection. In various embodiments, facilitating storage of the data includes dispersed storage error encoding the data to produce at least one set of encoded data slices and storing the at least one set of encoded data slices in the S number of storage units of the selected one combination. 
       FIG.  10    is a flowchart illustrating an example of selecting storage units. In particular, a method is presented for use in association with one or more functions and features described in conjunction with  FIGS.  1 - 9   , for execution by a computing device that includes a processor or via another processing system of a dispersed storage network that includes at least one processor and memory that stores instruction that configure the processor or processors to perform the steps described below. 
     The method includes step  510  where a processing system (e.g., of a distributed storage and task (DS) client module and/or a computing device) obtains resource information for least some storage units of a storage unit pool. The obtaining can include at least one of interpreting a query request, interpreting an error message, interpreting a test result, interpreting a performance log, and/or receiving the resource information. When receiving a store data request, the method continues at step  512  where the processing system identifies W available storage units of the storage unit pool. The identifying may be based on one or more of the resource information, interpreting system registry information, and receiving identities of the W available storage units. 
     The method continues at step  514  where the processing system identifies W choose S combinations of selecting S number of storage units of the W available storage units. For example, the processing system calculates W choose S for each combination. For each combination, the method continues at step  516  where the processing system calculate a plurality of rating levels based on the resource information, where each of the plurality of rating levels are assigned to a corresponding combination of the W choose S combinations. The assigning may be based on one or more portions of the resource information (e.g., access performance, storage availability). 
     The method continues at step  518  where the processing system selects one combination based on the rating levels. The selecting can include at least one of identifying a highest rated combination or selecting from a plurality of highest rated combinations utilizing at least one of a random selection, a round-robin selection, and/or a predetermination. The method continues at step  520  where the processing system facilitates storage of data of the store data request utilizing the storage units of the selected combination. For example, the processing system dispersed storage error encodes the data to produce encoded data slices, and stores S encoded data slices for each set of encoded data slices at S storage units of the selected combination. 
     In various embodiments, a non-transitory computer readable storage medium includes at least one memory section that stores operational instructions that, when executed by a processing system of a dispersed storage network (DSN) that includes a processor and a memory, causes the processing system to obtain resource information for a subset of storage units of a storage unit pool. W available storage units of the storage unit pool are identified in response to receiving a store data request. W choose S combinations of selecting S number of storage units of the W available storage units are identified. A plurality of rating levels is calculated based on the resource information, where each of the plurality of rating levels are assigned to a corresponding combination of the W choose S combinations. One combination of the W choose S combinations is selected based on the plurality of rating levels. Storage of data of the store data request is facilitated utilizing the S number of storage units of the selected one combination. 
       FIG.  11    is a schematic block diagram of another embodiment of a dispersed storage network (DSN) that includes the user device  14  of  FIG.  1   , at least two distributed storage and task (DST processing units  1 - 2 , the network  24  of  FIG.  1   , and a set of DST (EX) execution units  1 - n . The DST processing units  1 - 2  may be implemented utilizing the DST processing unit  16  of  FIG.  1   . Each DST execution unit may be implemented utilizing the DST execution unit  36  of  FIG.  1   . 
     The DSN functions to resolve a detected access anomaly. The access anomaly includes a data access pattern invoked by the user device  14  to the set of DST execution units via one of the DST processing units  1 - 2  that is different by at least a difference threshold level than a historical and typical data access pattern. Specific examples of the access anomaly includes one or more of attempting access from a location that has never historically been used to initiate access, such as from a different Internet service provider, from a different Internet protocol address, and from a different remote physical location; too many failed authentication attempts in a row (e.g., a bad login password); an access pattern that statistically deviates from a normal access pattern; and an access type that requires a second level authentication check due to significance of the access type (e.g., a change of credentials, extending of a user permission, and deleting a significant amount of data). 
     In an example of operation of the resolving of the detected access anomaly, while monitoring typical access of the user device  14  via the DST processing unit  1  (e.g., processing data access message is  1  on a frequent basis), issuing slice access messages  1 , via the network  24 , to the set of DST execution units  1 - n , DST execution unit  3  detects access by the user device  14  via the DST processing unit  2  (e.g., receiving a data access message  2 ) and issuing, via the network  24 , slice access messages  2  to the DST execution unit  3 , and determines that the access via the DST processing unit  2  deviates from typical access thus identifying the detected anomaly. 
     Having detected the access anomaly, the DST execution unit  3  queues the slice access message  2 . The queuing includes at least one of storing the slice access message  2  locally in the DST execution unit  3  and sending an anomaly detection indicator to other DST execution units. Having queued the request, the DST execution unit  3  initiates a secondary authentication process with the user device  14 . For example, the DST execution unit  3  exchanges secondary proxy authentication messages  2  with the DST processing unit  2  and the DST processing unit  2  exchanges secondary dedication messages  2  with the user device  14 . The DST execution unit  3  indicates favorable second level authentication when receiving favorable secondary proxy authentication messages  2 . For example, the DST execution unit  3  indicates a favorable second level authentication when a good password and/or credential is received. As another example the DST execution unit  3  indicates the favorable second level authentication when a question (e.g., encrypting a nonce etc.) is answered correctly. Alternatively, the DST execution unit  3  indicates the favorable second-level authentication only when receiving a threshold number of favorable second-level authentication indications from other DST execution units based on the other DST execution units performing a similar second-level authentication on the secondary proxy authentication messages  2 . When the secondary authentication process indicates that the user device  14  has been authenticated, the DST execution unit  3  de-queues the queued slice access message  2  for processing. Alternatively, each other DST execution unit also de-queues the queued slice access messages  2  for processing. 
       FIG.  12    is a flowchart illustrating an example of resolving a detected access anomaly. The method includes step  610  where a processing module (e.g., of a storage unit of a set of storage units) receives an access request from one or more requesters (e.g., user devices). The receiving includes at least one of receiving the access request directly from a requester and receiving a proxied access request via a proxy agent (e.g., a DST processing unit) of the one or more requesters. 
     The method continues at step  612  where the processing module detects an access anomaly of the received access request. The detecting includes at least one of detecting an unfavorable access pattern, detecting a new requester, detecting a new requester location, detecting one or more of favorable access times, detecting an access type associated with an access anomaly, and receiving an anomaly detection indicator from another storage unit of the set of storage units. 
     The method continues at step  614  where the processing module queues the access request. The queuing includes at least one of storing the access request in a local memory and associating the stored access request with one or more other storage access requests associated with the access request (e.g., a write request and a commit request for the same encoded data slice). 
     The method continues at step  616  where the processing module issues an anomaly detection indicator to other storage units. For example, the processing module generates the anomaly detection indicator to include one or more of the access request, and anomaly type, and an identifier of the requester. The method continues at step  618  where the processing module initiates a secondary authentication process with at least some of the one or more requesters. For example, the processing module issues a secondary authentication request to a requester (e.g., generate the secondary authentication request and send the generated secondary authentication request to the requester and/or a proxy of the requester). 
     The method continues at step  620  where the processing module receives one or more of a secondary authentication response and an anomaly detection status from at least one other storage unit. For example, the processing module receives the secondary authentication response from the requester and processes the received secondary authentication response to determine validity (e.g., answering a question, utilizing key pair is to authenticate a response, etc.). 
     When the secondary authentication response and the anomaly detection status is favorable, the method continues at step  622  where the processing module processes one or more associated queued access requests. For example, the processing module extracts the one or more associated queued access requests and processes the request in a sequence in accordance with a request type (e.g., write request, commit request, and finalize request of a three-phase storage process). 
     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 system”, “processing module”, “processing circuit”, “processor”, and/or “processing unit” may be used interchangeably, and 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 system, 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 system, 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 system, 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 system, 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 system, 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.