Patent Document

CROSS REFERENCES TO PRIORITY APPLICATIONS 
     This application is a continuation of co-pending U.S. Utility patent application Ser. No. 11/403,684, filed Apr. 13, 2006, which is a continuation-in-part of co-pending U.S. Utility patent application Ser. No. 11/241,555, filed Sep. 30, 2005. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to a billing system and method for a distributed data storage system for storing data in subsets and more particularly, to a billing system and method in which information regarding the original file size and the times and types of transactions are maintained and stored separately from the stored data subsets and used to perform billing operations in a commercial information dispersal data storage system. 
     2. Description of the Prior Art 
     Various data storage systems are known for storing data. Normally such data storage systems store all of the data associated with a particular data set, for example, all the data of a particular user or all the data associated with a particular software application or all the data in a particular file, in a single dataspace (i.e., single digital data storage device). Critical data is known to be initially stored on redundant digital data storage devices. Thus, if there is a failure of one digital data storage device, a complete copy of the data is available on the other digital data storage device. Examples of such systems with redundant digital data storage devices are disclosed in U.S. Pat. Nos. 5,890,156; 6,058,454; and 6,418,539, hereby incorporated by reference. Although such redundant digital data storage systems are relatively reliable, there are other problems with such systems. First, such systems essentially double or further increase the cost of digital data storage. Second, all of the data in such redundant digital data storage systems is in one place making the data vulnerable to unauthorized access. 
     The use of such information dispersal algorithms in data storage systems is also described in various trade publications. For example, “How to Share a Secret”, by A. Shamir,  Communications of the ACM , Vol. 22, No. 11, November, 1979, describes a scheme for sharing a secret, such as a cryptographic key, based on polynomial interpolation. Another trade publication, “Efficient Dispersal of Information for Security, Load Balancing, and Fault Tolerance”, by M. Rabin,  Journal of the Association for Computing Machinery , Vol. 36, No. 2, April 1989, pgs. 335-348, also describes a method for information dispersal using an information dispersal algorithm. Unfortunately, these methods and other known information dispersal methods are computationally intensive and are thus not applicable for general storage of large amounts of data using the kinds of computers in broad use by businesses, consumers and other organizations today. Thus there is a need for a data storage system that is able to reliably and securely protect data that does not require the use of computation intensive algorithms. 
     Several companies offer commercial data storage servers using data storage systems that store copies of data files together with associated metadata. Many companies, such as Rackspace, Ltd, offer data storage services as a part of general managed hosting services. Other known companies, such as Iron Mountain Incorporated, offer data storage services as a part of an online backup service. These companies typically determine billing charges in relation to the size of the data stored. The original file size is stored together with the data as a metadata attribute associated with the data file. Billing for such services is based on the amount of data stored or transferred. In these cases, billing amounts are derived from the metadata attributes associated with each file. In some situations, it is necessary that the data being stored or transmitted be changed in size, for example, by compression, in order to reduce storage space or improve transmission speed. In these situations, known information dispersal storage systems are unable to keep track of the original data file size. Since billing in such known systems is based upon metadata attributes associated with the data being stored or transferred, billing options in such situations are rather limited. Thus, there is a need for more flexible billing options in such information dispersal storage systems. 
    
    
     
       DESCRIPTION OF THE DRAWING 
       These and other advantages of the present invention will be readily understood with reference to the following drawing and attached specification wherein: 
         FIG. 1  is a block diagram of an exemplary data storage system in accordance with the present invention which illustrates how the original data is sliced into data subsets, coded and transmitted to a separate digital data storage device or node. 
         FIG. 2  is similar to  FIG. 1  but illustrates how the data subsets from all of the exemplary six nodes are retrieved and decoded to recreate the original data set. 
         FIG. 3  is similar to  FIG. 2  but illustrates a condition of a failure of one of the six digital data storage devices. 
         FIG. 4  is similar  FIG. 3  but for the condition of a failure of three of the six digital data storage devices. 
         FIG. 5  is an exemplary table in accordance with the present invention that can be used to recreate data which has been stored on the exemplary six digital data storage devices. 
         FIG. 6  is an exemplary table that lists the decode equations for an exemplary six node storage data storage system for a condition of two node outages. 
         FIG. 7  is similar to  FIG. 6  but for a condition with three node outages. 
         FIG. 8  is a table that lists all possible storage node outage states for an exemplary data storage system with nine storage nodes for a condition with two node outages. 
         FIG. 9  is an exemplary diagram in accordance with the present invention which illustrates the various functional elements of a metadata management system for use with an information dispersal storage system which provides flexible billing options in accordance with the present invention. 
         FIG. 10  is an exemplary flow chart that shows the process for maintaining metadata for data stored on the dispersed data storage grid. 
         FIG. 11  shows the essential metadata components that are used during user transactions and during user file set lookup. 
         FIGS. 12  A and  12  B illustrate the operation of the system. 
         FIG. 13  is an exemplary flow chart that shows a billing process in accordance with the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     The present invention relates to a billing system for an information dispersal storage system or data storage system. The information dispersal storage system is illustrated and described in connection with  FIGS. 1-8 .  FIGS. 9-12  illustrate a metadata management system for managing the information dispersal storage system. The billing system in accordance with the present invention is illustrated and described in connection with  FIG. 13 . It is to be understood that the principles of the billing system are amenable to being utilized with all sorts of information dispersal storage systems. The information dispersal storage system illustrated in  FIGS. 1-8  is merely exemplary of one type of information dispersal storage system for use with the present invention. 
     Information Dispersal Storage System 
     In order to protect the security of the original data, the original data is separated into a number of data “slices” or subsets. The amount of data in each slice is less usable or less recognizable or completely unusable or completely unrecognizable by itself except when combined with some or all of the other data subsets. In particular, the system in accordance with the present invention “slices” the original data into data subsets and uses a coding algorithm on the data subsets to create coded data subsets. Each data subset and its corresponding coded subset may be transmitted separately across a communications network and stored in a separate storage node in an array of storage nodes. In order to recreate the original data, data subsets and coded subsets are retrieved from some or all of the storage nodes or communication channels, depending on the availability and performance of each storage node and each communication channel. The original data is recreated by applying a series of decoding algorithms to the retrieved data and coded data. 
     As with other known data storage systems based upon information dispersal methods, unauthorized access to one or more data subsets only provides reduced or unusable information about the source data. In accordance with an important aspect of the invention, the system codes and decodes data subsets in a manner that is computationally efficient relative to known systems in order to enable broad use of this method using the types of computers generally used by businesses, consumers and other organizations currently. 
     In order to understand the invention, consider a string of N characters d 0 , d 1 , . . . , d N  which could comprise a file or a system of files. A typical computer file system may contain gigabytes of data which would mean N would contain trillions of characters. The following example considers a much smaller string where the data string length, N, equals the number of storage nodes, n. To store larger data strings, these methods can be applied repeatedly. These methods can also be applied repeatedly to store computer files or entire file systems. 
     For this example, assume that the string contains the characters, O L I V E R where the string contains ASCII character codes as follows:
         d 0 =O=79   d 1 =L=76   d 2 ,=I=73   d 3 ,=V=86   d 4 ,=E=69   d 5 =R=82       

     The string is broken into segments that are n characters each, where n is chosen to provide the desired reliability and security characteristics while maintaining the desired level of computational efficiency—typically n would be selected to be below 100. In one embodiment, n may be chosen to be greater than four (4) so that each subset of the data contains less than, for example, ¼ of the original data, thus decreasing the recognizablity of each data subset. 
     In an alternate embodiment, n is selected to be six (6), so that the first original data set is separated into six (6) different data subsets as follows:
 
 A=d   0   ,B=d   1   ,C=d   2   ,D=d   3   ,E=d   4   ,F=d   5  
 
     For example, where the original data is the starting string of ASCII values for the characters of the text O L I V E R, the values in the data subsets would be those listed below:
         A=79   B=76   C=73   D=86   E=69   F=82       

     In this embodiment, the coded data values are created by adding data values from a subset of the other data values in the original data set. For example, the coded values can be created by adding the following data values:
 
 c[x]=d[n _mod( x+ 1)]+ d[n _mod( x+ 2)]+ d[n _mod( x+ 4)]
 
where:
         c[x] is the xth coded data value in the segment array of coded data values   d[x+1] is the value in the position 1 greater than x in a array of data values   d[x+2] is the value in the position 2 greater than x in a array of data values   d[x+4] is the value in the position 4 greater than x in a array of data values   n_mod( ) is function that performs a modulo operation over the number space 0 to n−1       

     Using this equation, the following coded values are created:
 
cA, cB, cC, cD, cE, cF
 
where cA, for example, is equal to B+C+E and represents the coded value that will be communicated and/or stored along with the data value, A.
 
     For example, where the original data is the starting string of ASCII values for the characters of the text O L I V E R, the values in the coded data subsets would be those listed below:
         cA=218   cB=241   cC=234   cD=227   cE=234   cF=241       

     In accordance with the present invention, the original data set  20 , consisting of the exemplary data ABCDEF is sliced into, for example, six (6) data subsets A, B, C, D, E and F. The data subsets A, B, C, D, E and F are also coded as discussed below forming coded data subsets cA, cB, cC, cD, cE and cF. The data subsets A, B, C, D, E and F and the coded data subsets cA, cB, cC, cD, cE and cF are formed into a plurality of slices  22 ,  24 ,  26 ,  28 , 30  and  32  as shown, for example, in  FIG. 1 . Each slice,  22 ,  24 ,  26 ,  28 ,  30  and  32 , contains a different data value A, B, C, D, E and F and a different coded subset cA, cB, cC, cD, cE and cF. The slices  22 ,  24 ,  26 ,  28 ,  30  and  32  may be transmitted across a communications network, such as the Internet, in a series of data transmissions to a series and each stored in a different digital data storage device or storage node  34 ,  36 ,  38 ,  40 ,  42  and  44 . 
     In order to retrieve the original data (or receive it in the case where the data is just transmitted, not stored), the data can reconstructed as shown in  FIG. 2 . Data values from each storage node  34 ,  36 ,  38 ,  40 ,  42  and  44  are transmitted across a communications network, such as the Internet, to a receiving computer (not shown). As shown in  FIG. 2 , the receiving computer receives the slices  22 ,  24 ,  26 ,  28 ,  30  and  32 , each of which contains a different data value A, B, C, D, E and F and a different coded value cA, cB, cC, cD, cE and cF. 
     For a variety of reasons, such as the outage or slow performance of a storage node  34 ,  36 ,  38 ,  40 ,  42  and  44  or a communications connection, not all data slices  22 ,  24 ,  26 ,  28 ,  30  and  32  will always be available each time data is recreated.  FIG. 3  illustrates a condition in which the present invention recreates the original data set when one data slice  22 ,  24 ,  26 ,  28 ,  30  and  32 , for example, the data slice  22  containing the data value A and the coded value cA are not available. In this case, the original data value A can be obtained as follows:
 
 A=cC−D−E  
 
where cC is a coded value and D and E are original data values, available from the slices  26 ,  28  and  30 , which are assumed to be available from the nodes  38 ,  40  and  42 , respectively. In this case the missing data value can be determined by reversing the coding equation that summed a portion of the data values to create a coded value by subtracting the known data values from a known coded value.
 
     For example, where the original data is the starting string of ASCII values for the characters of the text O L I V E R, the data value of the A could be determined as follows:
 
 A= 234−86−69
 
Therefore A=79 which is the ASCII value for the character, O.
 
     In other cases, determining the original data values requires a more detailed decoding equation. For example,  FIG. 4  illustrates a condition in which three (3) of the six (6) nodes  34 ,  36  and  42  which contain the original data values A, B and E and their corresponding coded values cA, cB and cE are not available. These missing data values A, B and E and corresponding in  FIG. 4  can be restored by using the following sequence of equations:
 
 B =( cD−F+cF−cC )/2  1.
 
 E=cD−F−B   2.
 
 A=cF−B−D   3.
 
     These equations are performed in the order listed in order for the data values required for each equation to be available when the specific equation is performed. 
     For example, where the original data is the starting string of ASCII values for the characters of the text O L I V E R, the data values of the B, E and A could be determined as follows:
 
 B =(227−82+241−234)/2  B= 76  1.
 
 E= 227−82−76  E= 69  2.
 
 A= 241−76−86  A= 79  3.
 
     In order to generalize the method for the recreation of all original data ABCDEF when n=6 and up to three slices  22 ,  24 ,  26 ,  28   30  and  32  are not available at the time of the recreation,  FIG. 5  contains a table that can be used to determine how to recreate the missing data. 
     This table lists the 40 different outage scenarios where 1, 2, or 3 out of six storage nodes are be not available or performing slow enough as to be considered not available. In the table in  FIG. 5 , an ‘X’ in a row designates that data and coded values from that node are not available. The ‘Type’ column designates the number of nodes not available. An ‘Offset’ value for each outage scenario is also indicated. The offset is the difference the spatial position of a particular outage scenario and the first outage scenario of that Type. 
     The data values can be represented by the array d[x], where x is the node number where that data value is stored. The coded values can be represented by the array c[x]. 
     In order to reconstruct missing data in an outage scenario where one node is not available in a storage array where n=6, the follow equation can be used:
 
 d[ 0+offset]= c 3 d (2,3,4,offset)
 
where c3d ( ) is a function in pseudo computer software code as follows:
 
     
       
         
               
               
               
             
           
               
                   
               
             
             
               
                   
                  c3d(coded_data_pos, known_data_a_pos, known_data_b_pos, offset) 
                   
               
               
                   
                  { 
                   
               
               
                   
                   unknown_data= 
                   
               
               
                   
                     c[n_mod(coded_data_pos+offset)]− 
                   
               
               
                   
                     d[n_mod(known_data_a_pos+offset)]− 
                   
               
               
                   
                     d[n_mod(known_data_b_pos+offset)]; 
                   
               
               
                   
                   return unknown_data 
                   
               
               
                   
                  } 
                   
               
               
                   
                 where n_mod( ) is the function defined previously. 
                   
               
               
                   
                 [ 
               
               
                   
               
             
          
         
       
     
     In order to reconstruct missing data in an outage scenario where two nodes are not available in a storage array where n=6, the equations in the table in  FIG. 6  can be used. In  FIG. 6 , the ‘Outage Type Num’ refers to the corresponding outage ‘Type’ from  FIG. 5 . The ‘Decode Operation’ in  FIG. 6  refers to the order in which the decode operations are performed. The ‘Decoded Data’ column in  FIG. 6  provides the specific decode operations which produces each missing data value. 
     In order to reconstruct missing data in an outage scenario where three nodes are not available in a storage array where n=6, the equations in the table in  FIG. 7  can be used. Note that in  FIG. 7 , the structure of the decode equation for the first decode for outage type=3 is a different structure than the other decode equations where n=6. 
     The example equations listed above are typical of the type of coding and decoding equations that create efficient computing processes using this method, but they only represent one of many examples of how this method can be used to create efficient information distribution systems. In the example above of distributing original data on a storage array of 6 nodes where at least 3 are required to recreate all the data, the computational overhead of creating the coded data is only two addition operations per byte. When data is decoded, no additional operations are required if all storage nodes and communications channels are available. If one or two of the storage nodes or communications channels are not available when n=6, then only two additional addition/subtraction operations are required to decode each missing data value. If three storage nodes or communications channels are missing when n=6, then just addition/subtraction operations are required for each missing byte in 11 of 12 instances—in that twelfth instance, only 4 computational operations are required (3 addition/subtractions and one division by an integer). This method is more computationally efficient that known methods, such as those described by Rabin and Shamir. 
     This method of selecting a computationally efficient method for secure, distributed data storage by creating coded values to store at storage nodes that also store data subsets can be used to create data storage arrays generally for configurations where n=4 or greater. In each case decoding equations such as those detailed above can be used to recreate missing data in a computationally efficient manner. 
     Coding and decoding algorithms for varying grid sizes which tolerate varying numbers of storage node outages without original data loss can also be created using these methods. For example, to create a 9 node grid that can tolerate the loss of 2 nodes, a candidate coding algorithm is selected that uses a mathematical function that incorporates at least two other nodes, such as:
 
 c[x]=d[n _mod( x+ 1)]+ d[n _mod( x+ 2)]
 
where:
         n=9, the number of storage nodes in the grid   c[x] is the xth coded data value in the segment array of coded data values   d[x+1] is the value in the position 1 greater than x in a array of data values   d[x+2] is the value in the position 2 greater than x in a array of data values   n_mod( ) is function that performs a mod over the number space 0 to n−1       

     In this example embodiment, n=9, the first data segment is separated into different data subsets as follows:
 
 A=d   0   ,B=d   1   ,C=d   2   ,D=d   3   ,E=d   4   ,F=d   5   ,G=d   6   ,H=d   7   ,I=d   8  
 
     Using this candidate coding algorithm equation above, the following coded values are created:
 
cA, cB, cC, cD, cE, cF, cG, cH, cI
 
     The candidate coding algorithm is then tested against all possible grid outage states of up to the desired number of storage node outages that can be tolerated with complete data restoration of all original data.  FIG. 8  lists all possible storage grid cases for a 9 storage node grid with 2 storage node outages. Although there are 36 outage cases on a 9 node storage grid with 2 storage node outages, these can be grouped into 4 Types as shown in  FIG. 8 . Each of these 4 Types represent a particular spatial arrangement of the 2 outages, such as the 2 storage node outages being spatially next to each other in the grid (Type  1 ) or the 2 storage node outages being separated by one operating storage node (Type  2 ). The offset listed in  FIG. 8  shows the spatial relationship of each outage case within the same Type as they relate to the first outage case of that Type listed in that table. For example, the first instance of a Type  1  outage in  FIG. 8  is the outage case where Node 0  and Node 1  are out. This first instance of a Type  1  outage is then assigned the Offset value of 0. The second instance of a Type  1  outage in  FIG. 8  is the outage case where Node 1  and Node 2  are out. Therefore, this second instance of a Type  1  outage is assigned the Offset value of 1 since the two storage nodes outages occur at storage nodes that are 1 greater than the location of the storage node outages in the first case of Type  1  in  FIG. 8 . 
     The validity of the candidate coding algorithm can them be tested by determining if there is a decoding equation or set of decoding equations that can be used to recreate all the original data in each outage Type and thus each outage case. For example, in the first outage case in  FIG. 8 , Node 0  and Node 1  are out. This means that the data values A and B are not directly available on the storage grid. However, A can be recreated from cH as follows:
 
 cH=I+A  
 
 A=cH−I  
 
     The missing data value B can then be created from cI as follows:
 
 cI=A+B  
 
 B=cI−A  
 
     This type of validity testing can then be used to test if all original data can be obtained in all other instances where 2 storage nodes on a 9 node storage grid are not operating. Next, all instances where 1 storage node is not operating on a 9 node storage grid are tested to verify whether that candidate coding algorithm is valid. If the validity testing shows that all original data can be obtained in every instance of 2 storage nodes not operating on a 9 node storage grid and every instance of 1 storage node not operating on a 9 node storage grid, then that coding algorithm would be valid to store data on a 9 node storage grid and then to retrieve all original data from that grid if up to 2 storage nodes were not operating. 
     These types of coding and decoding algorithms can be used by those practiced in the art of software development to create storage grids with varying numbers of storage nodes with varying numbers of storage node outages that can be tolerated by the storage grid while perfectly restoring all original data. 
     Metadata Management System 
     A metadata management system, illustrated in  FIGS. 9-12 , is used to manage dispersal and storage of information that is dispersed and stored in several storage nodes coupled to a common communication network forming a grid, for example, as discussed above in connection with  FIGS. 1-8 . In order to enhance the reliability of the information dispersal system, metadata attributes of the transactions on the grid are stored in separate dataspace from the dispersed data. 
     As discussed above, the information dispersal system “slices” the original data into data subsets and uses a coding algorithm on the data subsets to create coded data subsets. In order to recreate the original data, data subsets and coded subsets are retrieved from some or all of the storage nodes or communication channels, depending on the availability and performance of each storage node and each communication channel. As with other known data storage systems based upon information dispersal methods, unauthorized access to one or more data subsets only provides reduced or unusable information about the source data. For example as illustrated in  FIG. 1 , each slice  22 ,  24 ,  26 ,  28 ,  30  and  32 , contains a different data value A, B, C, D, E and F and a different “coded subset” (Coded subsets are generated by algorithms and are stored with the data slices to allow for restoration when restoration is done using part of the original subsets) cA, cB, cC, cD, cE and cF. The slices  22 ,  24 ,  26 ,  28 ,  30  and  32  may be transmitted across a communications network, such as the Internet, in a series of data transmissions to a series and each stored in a different digital data storage device or storage node  34 ,  36 ,  38 ,  40 ,  42  and  44 . Each data subset and its corresponding coded subset may be transmitted separately across a communications network and stored in a separate storage node in an array of storage nodes. 
     A “file stripe” is the set of data and/or coded subsets corresponding to a particular file. Each file stripe may be stored on a different set of data storage devices or storage nodes  57  within the overall grid as available storage resources or storage nodes may change over time as different files are stored on the grid. 
     A “dataspace” is a portion of a storage grid  49  that contains the data of a specific client  64 . A grid client may also utilize more than one data. The dataspaces table  106  in  FIG. 11  shows all dataspaces associated with a particular client. Typically, particular grid clients are not able to view the dataspaces of other grid clients in order to provide data security and privacy. 
       FIG. 9  shows the different components of a storage grid, generally identified with the reference numeral  49 . The grid  49  includes associated storage nodes  54  associated with a specific grid client  64  as well as other storage nodes  56  associated with other grid clients (collectively or individually “the storage nodes  57 ”), connected to a communication network, such as the Internet. The grid  49  also includes applications for managing client backups and restorations in terms of dataspaces and their associated collections. 
     In general, a “director” is an application running on the grid  49 . The director serves various purposes, such as:
     1. Provide a centralized-but-duplicatable point of User-Client login. The Director is the only grid application that stores User-login information.   2. Autonomously provide a per-User list of stored files. All User-Client&#39;s can acquire the entire list of files stored on the Grid for each user by talking to one and only one director. This file-list metadata is duplicated across one Primary Directory to several Backup Directors.   3. Track which Sites contain User Slices.   4. Manager Authentication Certificates for other Node personalities.   

     The applications on the grid form a metadata management system and include a primary director  58 , secondary directors  60  and other directors  62 . Each dataspace is always associated at any given time with one and only one primary director  58 . Every time a grid client  64  attempts any dataspace operation (save/retrieve), the grid client  64  must reconcile the operation with the primary director  58  associated with that dataspace. Among other things, the primary director  58  manages exclusive locks for each dataspace. Every primary director  58  has at least one or more secondary directors  60 . In order to enhance reliability of the system, any dataspace metadata updates (especially lock updates) are synchronously copied by the dataspace&#39;s primary director  58  and to all of its secondary or backup directors  60  before returning acknowledgement status back to the requesting grid client.  64 . In addition, for additional reliability, all other directors  62  on the Grid may also asynchronously receive a copy of the metadata update. In such a configuration, all dataspace metadata is effectively copied across the entire grid  49 . 
     As used herein, a primary director  58  and its associated secondary directors  60  are also referred to as associated directors  60 . The secondary directors  60  ensure that any acknowledged metadata management updates are not lost in the event that a primary director  58  fails in the midst of a grid client  64  dataspace update operation. There exists a trade-off between the number of secondary directors  60  and the metadata access performance of the grid  49 . In general, the greater the number of secondary directors  60 , the higher the reliability of metadata updates, but the slower the metadata update response time. 
     The associated directors  66  and other directors  62  do not track which slices are stored on each storage node  57 , but rather keeps track of the associated storage nodes  57  associated with each grid client  64 . Once the specific nodes are known for each client, it is necessary to contact the various storage nodes  57  in order to determine the slices associated with each grid client  64 . 
     While the primary director  58  controls the majority of Grid metadata; the storage nodes  57  serve the following responsibilities:
         1. Store the user&#39;s slices. The storage nodes  57  store the user slices in a file-system that mirrors the user&#39;s file-system structure on the Client machines(s).   2. Store a list of per-user files on the storage node  57  in a database. The storage node  57  associates minimal metadata attributes, such as Slice hash signatures (e.g., MD5s) with each slice “row” in the database.       

     The Grid identifies each storage node  57  with a unique storage volume serial number (volumeID) and as such can identify the storage volume even when it is spread across multiple servers. In order to recreate the original data, data subsets and coded subsets are retrieved from some or all of the storage nodes  57  or communication channels, depending on the availability and performance of each storage node  57  and each communication channel. Each primary director  58  keeps a list of all storage nodes  57  on the grid  49  and therefore all the nodes available at each site. 
     Following is the list of key metadata attributes used during backup/restore processes: 
     
       
         
               
               
             
           
               
                   
               
               
                 Attribute 
                 Description 
               
               
                   
               
             
             
               
                 iAccountID 
                 Unique ID number for each account,  
               
               
                   
                 unique for each user. 
               
               
                 iDataspaceID 
                 Unique ID for each user on all the volumes,  
               
               
                   
                 it is used to keep track of the user data on  
               
               
                   
                 each volume 
               
               
                 iDirectorAppID 
                 Grid wide unique ID which identifies a  
               
               
                   
                 running instance of the director. 
               
               
                 iRank 
                 Used to insure that primary director always  
               
               
                   
                 has accurate metadata. 
               
               
                 iVolumeID 
                 Unique for identifying each volume on the Grid, 
               
               
                   
                 director uses this to generate a volume map 
               
               
                   
                 for a new user (first time) and track volume 
               
               
                   
                 map for existing users. 
               
               
                 iTransactionContextID 
                 Identifies a running instance of a client. 
               
               
                 iApplicationID 
                 Grid wide unique ID which identifies  
               
               
                   
                 running instance of an application. 
               
               
                 iDatasourceID 
                 All the contents stored on the grid is in  
               
               
                   
                 the form of data source, each unique file  
               
               
                   
                 on the disk is associated with this unique ID. 
               
               
                 iRevision 
                 Keeps track of the different revisions for a  
               
               
                   
                 data source. 
               
               
                 iSize 
                 Metadata to track the size of the data source 
               
               
                 sName 
                 Metadata to track the name of the data source 
               
               
                 iCreationTime 
                 Metadata to track the creation time of the  
               
               
                   
                 data source 
               
               
                 iModificationTime 
                 Metadata to track the last modification time  
               
               
                   
                 of the data source, 
               
               
                   
               
             
          
         
       
     
       FIG. 10  describes a flow of data and a top level view of what happens when a client interacts with the storage system.  FIG. 11  illustrates the key metadata tables that are used to keep track of user info in the process. 
     Referring to  FIG. 10 , initially in step  70 , a grid client  64  starts with logging in to a director application running on a server on the grid. After a successful log in, the director application returns to the grid client  64  in step  72 , a DataspaceDirectorMap  92  ( FIG. 11 ). The director application includes an AccountDataspaceMap  93 ; a look up table which looks up the grid client&#39;s AccountID in order to determine the DataspaceID. The DataspaceID is then used to determine the grid client&#39;s primary director (i.e., DirectorAppID) from the DataspaceDirectorMap  92 . 
     Once the grid client  64  knows its primary director  58 , the grid client  64  can request a Dataspace VolumeMap  94  ( FIG. 11 ) and use the DataspaceID to determine the storage nodes associated with that grid client  64  (i.e., VolumeID). The primary director  58  sets up a TransactionContextID for the grid client  64  in a Transactions table  102  ( FIG. 11 ). The TransactionContextID is unique for each transaction (i.e., for each running instance or session of the grid client  64 ). In particular, the Dataspace ID from the DataspaceDirectorMap  92  is used to create a unique transaction ID in a TransactionContexts table  96 . The transaction ID stored in a Transaction table  102  along with the TransactionContextID in order to keep track of all transactions by all of the grid clients for each session of a grid client with the grid  49 . 
     The “TransactionContextID” metadata attribute is a different attribute than TransactionID in that a client can be involved with more than one active transactions (not committed) but at all times only one “Transaction context Id” is associated with one running instance of the client. These metadata attributes allow management of concurrent transactions by different grid clients. 
     As mentioned above, the primary director  58  maintains a list of the storage nodes  57  associated with each grid client  64 . This list is maintained as a TransactionContexts table  96  which maintains the identities of the storage nodes (i.e., DataspaceID) and the identity of the grid client  64  (i.e., ID). The primary director  58  contains the “Application” metadata (i.e., Applications table  104 ) used by the grid client  64  to communicate with the primary director  58 . The Applications table  64  is used to record the type of transaction (AppTypeID), for example add or remove data slices and the storage nodes  57  associated with the transaction (i.e., SiteID). 
     Before any data transfers begins, the grid client  64  files metadata with the primary director  58  regarding the intended transaction, such as the name and size of the file as well as its creation date and modification date, for example. The metadata may also include other metadata attributes, such as the various fields illustrated in the TransactionsDatasources table  98 . ( FIG. 11 ) The Transaction Datasources metadata table  98  is used to keep control over the transactions until the transactions are completed. 
     After the above information is exchanged between the grid client  64  and the primary director  58 , the grid client  64  connects to the storage nodes in step  74  in preparation for transfer of the file slices. Before any information is exchanged, the grid client  64  registers the metadata in its Datasources table  100  in step  76  in order to fill in the data fields in the Transaction Datasources table  98 . 
     Next in step  78 , the data slices and coded subsets are created in the manner discussed above by an application running on the grid client  64 . Any data scrambling, compression and/or encryption of the data may be done before or after the data has been dispersed into slices. The data slices are then uploaded to the storage nodes  57  in step  80 . 
     Once the upload starts, the grid client  64  uses the transaction metadata (i.e., data from Transaction Datasources table  98 ) to update the file metadata (i.e., DataSources table  100 ). Once the upload is complete, only then the datasource information from the Transaction Datasources table  98  is moved to the Datasource table  100  and removed from the Transaction Datasources table  98  in steps  84 ,  86  and  88 . This process is “atomic” in nature, that is, no change is recorded if at any instance the transaction fails. The Datasources table  100  includes revision numbers to maintain the integrity of the user&#39;s file set. 
     A simple example, as illustrated in  FIGS. 12  A and  12 B, illustrates the operation of the metadata management system  50 . The example assumes that the client wants to save a file named “Myfile.txt” on the grid  49 . 
     Step 1: The grid client connects to the director application running on the grid  49 . Since the director application is not the primary director  58  for this grid client  64 , the director application authenticates the grid client and returns the DataspaceDirectorMap  92 . Basically, the director uses the AccountID to find its DataspaceID and return the corresponding DirectorAppID (primary director ID for this client). 
     Step 2: Once the grid client  64  has the DataspaceDirectorMap  92 , it now knows which director is its primary director. The grid client  64  then connects to this director application and the primary director creates a TransactionContextID, as explained above, which is unique for the grid client session. The primary director  58  also sends the grid client  64  its DataspaceVolumeMap  94  (i.e., the number of storage nodes  57  in which the grid client  64  needs to a connection). The grid client  64  sends the file metadata to the director (i.e., fields required in the Transaction Datasources table). 
     Step 3: By way of an application running on the client, the data slices and coded subsets of “Myfile.txt” are created using storage algorithms as discussed above. The grid client  64  now connects to the various storage nodes  57  on the grid  49 , as per the DataspaceVolumeMap  94 . The grid client now pushes its data and coded subsets to the various storage nodes  57  on the grid  49 . 
     Step 4: When the grid client  64  is finished saving its file slices on the various storage nodes  57 , the grid client  64  notifies the primary director application  58  to remove this transaction from the TransactionDatasources Table  98  and add it to the Datasources Table  100 . The system is configured so that the grid dent  64  is not able retrieve any file that is not on the Datasources Table  100 . As such, adding the file Metadata on the Datasources table  100  completes the file save/backup operation. 
     As should be clear from the above, the primary director  58  is an application that decides when a transaction begins or ends. A transaction begins before a primary director  58  sends the storage node  57  metadata to the grid client  64  and it ends after writing the information about the data sources on the Datasources table  100 . This configuration insures completeness. As such, if a primary director  58  reports a transaction as having completed, then any application viewing that transaction will know that all the other storage nodes have been appropriately updated for the transaction. This concept of “Atomic Transactions” is important to maintain the integrity of the storage system. For example, if the entire update transaction does not complete, and all of the disparate storage nodes are not appropriately “synchronized,” then the storage system is left in a state of disarray, at least for the Dataspace table  100  of the grid client  64  in question. Otherwise, if transactions are interrupted for any reason (e.g., simply by powering off a client PC in the middle of a backup process) and are otherwise left in an incomplete state, the system&#39;s overall data integrity would become compromised rather quickly. 
     Billing System for Information Dispersal Storage System 
     In accordance with an important aspect of the invention, metadata tables that include information about the original files are created and maintained separate from the file shares as illustrated in  FIGS. 9-12 . These separate files are used to provide information required to bill for commercial usage of the information dispersal grid. Although the system is described and illustrated for use with the information dispersal storage system, illustrated in  FIGS. 1-8 , the principles of the present invention are applicable to virtually any such system, such as systems configured as Storage Area Networks (SAN), for example as disclosed in U.S. Pat. Nos. 6,256,688 and 7,003,688 as well as US Patent Application Publications US 2005/0125593 A1 and US 2006/0047907 A1, hereby incorporated by reference. 
     As mentioned above, the metadata management system includes a primary director  58  and one or more secondary directors  60  (collectively or individually “the associated directors  66 ”). These directors  66  are used to create the metadata tables, illustrated in  FIG. 12  that are associated with each grid client  64 . These metadata tables include information regarding transactions of the files that are stored on the storage nodes  57  and are maintained separately from the dispersed files in the storage nodes  57 . 
     In accordance with the present invention each associated director  66  generally stores a Storage Transaction Table with an exemplary structure as illustrated below for each node: 
     
       
         
               
             
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
             
           
               
                   
               
               
                 Storage Transaction Table 
               
             
          
           
               
                   
                   
                   
                   
                 Original- 
                   
                   
               
               
                 Date/ 
                 Trans- 
                   
                   
                 FileSize 
                   
                 Com- 
               
               
                 Time 
                 actionID 
                 AccountID 
                 FileID 
                 (Bytes) 
                 Type 
                 pleted 
               
               
                   
               
             
          
           
               
                 Mar. 20, 
                 4218274 
                 0031321123 
                 06693142 
                 55312 
                 Add 
                 True 
               
               
                 2005 
                   
                   
                   
                   
                   
                   
               
               
                 14:32:05 
                   
                   
                   
                   
                   
                   
               
               
                 Mar. 20,  
                 4218275 
                 0031321123 
                 06774921 
                 621921 
                 Add 
                 True 
               
               
                 2005 
                   
                   
                   
                   
                   
                   
               
               
                 14:32:06 
                   
                   
                   
                   
                   
                   
               
               
                 Mar. 20,  
                 4218276 
                 0019358233 
                 04331131 
                 4481 
                 Re- 
                 True 
               
               
                 2005 
                   
                   
                   
                   
                 move 
                   
               
               
                 14:32:12 
                   
                   
                   
                   
                   
                   
               
               
                 Mar. 20,  
                 4218277 
                 0019358233 
                 05823819 
                 8293100219 
                 Add 
                 False 
               
               
                 2005 
                   
                   
                   
                   
                   
                   
               
               
                 14:32:35 
               
               
                   
               
             
          
         
       
     
     For each storage transaction, the storage transaction table logs the file size prior to dispersal for storage on the dispersal grid (OriginalFileSize) and optionally other information regarding the transaction, for example, the date and time of the transaction; a unique transaction identification number (TransactionID); an account identification number associated with that transaction (AccountID); a file identification number associated with that transaction (File ID); a transaction type of add or delete; and a completed flag for that transaction. As such, the storage transaction table is able to maintain the original size of the files before dispersal even though the file is dispersed into file slices on the grid which may be different in size from the original file size. These file slices may be further reduced in size by the information dispersal system in order to reduce storage space or improve transmission time. Accordingly, the storage transaction table allows more flexible options which include billing for file storage based upon the original file size even though the files are dispersed and/or compressed. 
     In order to create a billing invoice, a separate Billing Process requests information from the Grid using the process shown in  FIG. 13 . First, a Billing Process logs onto a director  66  in step  106 . Next in step  108 , the billing process requests the amount of original storage associated with each billing account in step  106 . Specifically, the Billing Process retrieves the account identification numbers (AccountID) and the file size prior to dispersal for storage on the dispersal grid (OriginalFileSize) for each transaction. Then the Billing Process sums all the original storage amounts associated with each Billing Account to create a table as structured below: 
     
       
         
               
             
               
               
               
             
               
               
               
             
           
               
                   
               
               
                 Summary Billing Information Table 
               
             
          
           
               
                   
                   
                 TotalOriginalStorage 
               
               
                   
                 AccountID 
                 (Bytes) 
               
               
                   
               
             
          
           
               
                   
                 0031321123 
                 1388239 
               
               
                   
                 0019358233 
                 8457309384 
               
               
                   
               
             
          
         
       
     
     With the information in the Summary Billing Information Table, the Billing Process creates invoices for each Billing Account. This method may be used for commercial dispersed data storage services that bill an amount based on a rate per byte storage or that bill an amount based on an amount of data storage within a range of storage amounts or that use some other method to determine billing amounts based on storage amounts. 
     Obviously, many modifications and variations of the present invention are possible in light of the above teachings. Thus, it is to be understood that, within the scope of the appended claims, the invention may be practiced otherwise than is specifically described above.

Technology Category: g