Patent Publication Number: US-7596585-B2

Title: Object replication using information quality of service

Description:
TECHNICAL FIELD 
     The present invention generally relates to computer databases and more specifically to synchronization of duplicate databases on a network. 
     BACKGROUND 
     A typical computer network comprises a plurality of computers linked by a communications network. Applications running on these computers need to share information. One solution is to host a database on one of the computers, and have applications, regardless of their location, read and update this database. This client-server solution works well in many situations, but is less effective when network bandwidth or availability is marginal, when the server cannot accommodate the applications&#39; database access workload, and when the server is an unacceptably risky single point of failure. 
     An alternative that addresses these issues is to replicate the database on some or all of the computers. On each computer, applications read and update the local database by sending it requests and the database sends a response to each request once it is completed. The challenge in this configuration is to keep the database replicas consistent with each other. One way this can be accomplished is through eager replication in which each update is propagated and applied to every database before confirming completion to the requesting application. Furthermore, updates are applied in the same order to all databases to ensure that the databases remain consistent with each other. Compared to the client-server approach, eager replication provides improved query performance and query availability in the face of network and node failure. However, update performance is actually lower and updates cannot be performed during network or node outages because communication links between nodes are broken. These issues can be addressed using a quorum-based approach, at the cost of poorer query performance and availability. Another way to keep the database replicas consistent is through lazy replication. With lazy replication, each application sends requests to its local database, and receives a confirming response as soon as the request is processed locally. Subsequently, a replication function propagates the local updates to other computers and applies them to those computers&#39; databases. If communications connectivity is temporarily broken, update propagation is delayed but local database request processing continues. 
     One problem with the state of the art in database replication is that neither eager replication nor existing lazy replication methods adequately addresses reducing communication network resource costs while still maintaining reasonable levels of database synchronization. 
     Commercial database management products such as Oracle implement lazy replication. Two approaches commonly used by these products are data-level replication and procedure-level replication. With data-level replication, the requests propagated are inserts, updates, and deletes. Request reordering and compression are used. With procedure-level replication, the requests are arbitrary procedures but request reordering and compression are not used. Neither approach attempts to reduce communication network resource costs while still ensuring that information in database replicas meet reasonable synchronization requirements for the defined mission of the database. 
     One form of database compression, used typically to reduce data storage costs, is known as process data historians. Process data historians use a compression technique called the “swinging door algorithm” that eliminates samples that can be reconstructed with acceptable accuracy through interpolation from surrounding samples. However, interpolation was not defined for arbitrarily complex state spaces, so it is limited in application to simple scalar data such as found in process control systems. 
     For the reasons stated above and for other reasons stated below which will become apparent to those skilled in the art upon reading and understanding the specification, there is a need in the art for database replication which reduces communication network resource costs while providing reasonably reliable data. 
     SUMMARY 
     The Embodiments of the present invention address the problem of reducing network resource costs through the application of information quality of service requirements, as well as other problems and will be understood by reading and studying the following specification. 
     Embodiment of the present invention provide levels of database synchronization, as defined by the accuracy of data contained in database replicas and the amount of delay in propagating updates to database replicas (i.e. the freshness of the information in the database replica), that are acceptable given the particular mission of the database, while reducing communication network resource costs. 
     In one embodiment, a database replication function for a source object and one or more destination objects where one or more local applications that make requests of the source object, is disclosed. A request logging function records the requests sent by the one or more applications to the source object into a memory log. An update propagation function determines which source object states must be propagated to the one or more destination objects based on information quality of service accuracy requirements. 
     In another embodiment, a database system is disclosed. The database system comprising a local object having a container object structure, one or more destination objects having the same container object structure as the local object, and one or more local applications adapted to make requests to the source object. A request logging function within the database system is adapted to record a sequence of requests sent by the one or more applications to the source object. An update propagation function determines which source object states to propagate to the one or more destination objects, based on information quality of service accuracy requirements. 
     In yet another embodiment, a method for propagating database updates is disclosed. The method comprises logging requests made to a source object by one or more applications. The method continues with determining the magnitude of the source object state change since the most recent previous update propagation and determining if the magnitude of the source object state change is greater than or equal to an information quality of service accuracy parameter Δs. Then the method continues with determining if the present source object state has existed on the source object for a time greater than or equal to an information quality of service accuracy parameter Δt without being propagated to the destination object. The method then proceeds with propagating updates to one or more destination databases, if the magnitude of the source object state change is greater than or equal to Δs and the present source object state has existed on the source object for a time greater than or equal to Δt without being propagated to the one or more destination objects. 
     In yet another embodiment, another method for propagating database updates is disclosed. The method comprises logging requests made to a source object by one or more applications, periodically determining if the magnitude of a source object state change, since the time of the most recent previous update propagation, is greater than or equal to an information quality of service accuracy parameter Δs and if the present source object state has existed on the source object for a time greater than or equal to an information quality of service accuracy parameter Δt without being propagated to the destination object. When the magnitude of the source object state change is greater than or equal to Δs and the present source object state has existed on the source object for a time greater than or equal to Δt without being propagated to the one or more destination objects, the method continues with applying a compression algorithm that transforms the logged requests into a compressed sequence of requests and propagating the compressed sequence of request to one or more destination objects, wherein the cycle period for periodically determining the magnitude of the source object state change is chosen so that information quality of service timing requirements are not exceeded. 
     In yet another embodiment, another method for propagating database updates is disclosed. The method comprising logging requests made to a source object by one or more applications, determining the magnitude of a source object state change since the time of the most recent previous update propagation and determining if the magnitude of the source object state change is greater than or equal to an information quality of service accuracy parameter Δs. When the magnitude of the source object state change is greater than or equal to Δs, the method continues with propagating a sequence of updates based on the logged requests to one or more destination databases. 
     In yet another embodiment, another method for propagating database updates is disclosed. The method comprising logging requests made to a source object by one or more applications, determining the magnitude of a source object state change since the time of the most recent previous update propagation and determining if the magnitude of the source object state change is greater than or equal to an information quality of service accuracy parameter Δs. When the magnitude of the source object state change is greater than or equal to Δs, the method continues with applying a compression algorithm that transforms the logged requests into a compressed sequence of requests to produce the sequence of updates and propagating a sequence of updates based on the logged requests to one or more destination databases. 
     In still another embodiment, a method for propagating database updates, where the method is embedded in a computer-readable medium, is disclosed. The method comprises logging requests made to a source object by one or more applications. The method continues with determining the magnitude of the source object state change since the most recent previous update propagation and determining if the magnitude of the source object state change is greater than or equal to an information quality of service accuracy parameter Δs. Next, the method continues with determining if the present source object state has existed on the source object for a time greater than or equal to an information quality of service accuracy parameter Δt without being propagated to the destination object. The method then proceeds with propagating updates to one or more destination databases, if the magnitude of the source object state change is greater than or equal to Δs and the present source object state has existed on the source object for a time greater than or equal to Δt without being propagated to the one or more destination objects. 
    
    
     
       DRAWINGS 
       The present invention can be more easily understood and further advantages and uses thereof more readily apparent, when considered in view of the description of the preferred embodiments and the following figures in which: 
         FIG. 1  is a diagram illustrating the synchronization of objects of one embodiment of the present invention; 
         FIG. 2  is a diagram illustrating information quality of service definitions for a container object of one embodiment of the present invention; 
         FIGS. 3   a  and  3   b  are diagrams illustrating propagation of container objects of one embodiment of the present invention; 
         FIG. 4  is a flow diagram illustrating container object request compression of one embodiment of the present invention; 
         FIG. 5  is a flow diagram illustrating a method of one embodiment of the present invention; and 
         FIG. 6  is a flow diagram illustrating another method of one embodiment of the present invention. 
     
    
    
     In accordance with common practice, the various described features are not drawn to scale but are drawn to emphasize features relevant to the present invention. Reference characters denote like elements throughout Figures and text. 
     DETAILED DESCRIPTION 
     Reducing communication cost is important in environments, such as wireless networks, where bandwidth is at a premium and availability is intermittent. Lazy replication keeps database replicas synchronized by propagating updates applied to one object (e.g. a source database) to other objects (e.g. destination databases). Embodiments of the present invention take into consideration information quality of service requirements (in terms of tolerable synchronization time freshness requirements and accuracy requirements) to reduce network resource related communication costs for propagating database updates between computers on a network. 
     Embodiments of the present invention have two elements. The first element involves compression of the sequence of source database updates into a shorter sequence of destination database updates, while still keeping the replicas acceptably synchronized. The second element is to define precisely, in terms of information quality of service (IQoS) freshness and accuracy requirements, what constitutes acceptable synchronization of the database replicas. 
       FIG. 1  illustrates the components of a database system  102  of one embodiment of the present invention generally at  100 . Within a database replication function  106 , a request logging function  110  records the sequence of requests that local applications  104  send to a local information object  108 . In one embodiment, local information object  108  comprises a source database  108 . Record logging function  110  filters out any requests that do not actually cause a state change in local object  108 , so that each logged request represents an update to local object  108 . An update propagation function  114  determines which object states to propagate based on IQoS accuracy requirements selected by a database administrator or owner as disclosed in this specification. Update propagation function  114  transforms logged results into a compressed sequence of requests that will cause states of local object  108  to be propagated via communication link  118  to a destination object  120 . In one embodiment, destination object  120  comprises a destination database  120 . Update propagation function  114  sends the compressed sequence of requests to an update reception function  122  for destination object  120  based on IQoS propagation freshness requirements. In one embodiment, database system  102  further comprises an update reception function  116  that receives similar updates from one or more other database systems  124  and applies the updates to the local information object  108 . 
     Although this specification discusses embodiments of the present invention in terms of database replicas, the approaches presented in the embodiments are valid for any information object that has a defined state and a set of operations by which applications can read and update that state. Such information objects may also include, but are not limited to a simple discrete variable, a collection of files, or a relational database. 
     An object&#39;s behavior is completely defined by its type T=(S, s 0 , Q, R, !, ?). S is the object&#39;s state space—the set of possible object values. S can range from a small set of enumerated choices to a very large, highly structured set defined by a relational, object-oriented or similar model. This structure is visible to applications only indirectly, by the object&#39;s response to each request. The object&#39;s initial state is ε. 
     An application reads or updates the object by sending it a request and receiving a response. Q is the set of possible requests and R is the set of possible responses. Q and R can range from small to very large, defined in terms of request and response forms with variable-content fields. 
     Object behavior is deterministic, meaning that whenever an application sends a request q to the local object  108 , the object&#39;s response r, and its state s′ after executing the request, are completely determined by (i.e. functions of) the request q and the object state s prior to receiving the request. The response and state transition functions are denoted by the infix operators ? and !, so that r=s?q and s′=s!q. The requests sent to an object are assumed to be executed in some serial order, or at least in a serializable fashion, and that the sequence of requests executed by an object can be recorded for use in replication. 
     As an example, in one embodiment, a simple object type Table consists of a single relational table with n columns, the first k of which are key columns. Keys in a database (such as key columns or key values) are typically used to uniquely identify an individual record such that no two records have a duplicate key value. Non-key column values have significance, and changes to non-key column values from one database state s i  to another s j  typically represent a change in some real world parameter (e.g., a checking account balance or remaining fuel in a fuel tank). In one embodiment, the example object type Table accepts four kinds of requests: insert(x 1 , . . . , x n ), delete(x 1 , . . . x k ), set(x 1 , . . . x k ,i,x j ) where k&lt;i≦n and get(x 1 , . . . x k ,i) where k&lt;i≦n. 
     A state is a set of rows of the form (x 1 , . . . , x n ) such that no two distinct rows have the same values for x 1 , . . . , x k . The state space S is the set of all such sets of rows. It can be defined more precisely in terms of state variables. A state variable is an identifiable, updatable element of the state space S. State variables are used merely for type specification; the object&#39;s implementation may record state in a different way. In one embodiment, for type Table, the state variables are:
         exists: array(D 1 , . . . , D k ) of {0,1}   col i : array(D 1 , . . . , D k ) of D i  for k&lt;i≦n       

     where D 1 , . . . , D n  are the domains of columns 1, . . . , n. The state variable exists(x 1 , . . . , X k ) indicates whether a row with key values x 1 , . . . , x k  exists (1=yes, 0=no); the state variable col i (x 1 , . . . , x k ) holds the value of non-key column i for key x 1 , . . . , x k . Strictly speaking, col i (x 1 , . . . , x k ) can be undefined if exists(x 1 , . . . , x k )=0. The initial state has exists(x 1 , . . . , x k )=0 for all possible key values. 
     Request Sequence Compression. The destination database  120  can be kept adequately fresh and accurate without applying each and every update that was applied to the source database  108 . Initially, both source database  108  and destination database  122  have state s 0  according to their behavioral type. Applications  104  on source computer  102  send requests q 1 , q 2 , q 3 , q 4 , q 5 , . . . to the source database  108 , causing the database  108  to achieve new states s 1 , s 2 , s 3 , s 4 , s 5 , . . . at times t 1 , t 2 , t 3 , t 4 , t 5 , . . . . In one embodiment, replication function  106  records these requests in a log  112  on the source computer  102 . The update propagation function  114  applies a compression algorithm that transforms the logged requests into a compressed sequence of requests, say q′ 1 , q′ 3 , q′ 5 , . . . , and sends these to the destination database  120 . As a result, destination database  120  achieves a subsequence s 1 , s 3 , s 5 , . . . of source database  108 &#39;s states at times t′ 1 , t′ 3 , t′ 5 , . . . . The compressed sequence of requests can be communicated at a lower cost than the sequence of requests originally applied to the source database. 
     In one embodiment, the compression algorithm starts with a sequence of requests recorded in the source computer&#39;s log  112 , and repeatedly applies a set of compression transformations until no further transformation is possible. Each compression transformation replaces a consecutive subsequence of requests q 1 , . . . , q n  by a subsequence q′ 1 , . . . , q′ m  (0≦m&lt;n). The source database  108 &#39;s behavior type determines the set of permissible compression transformations. A compression transformation q 1 , . . . , q n →q′ 1 , . . . , q′ m  is permissible if it satisfies two conditions. First, the transformation must cause the destination database  120  to achieve the same state as the source object  108  after executing the compressed sequence of requests. More precisely, s!q′ 1 ! . . . !q′ m =s!q 1 ! . . . !q n  for any state s (read as: a database state s operated on by a sequence of requests q′ 1  through q′ m  results in the same resulting state as state s operated on by a sequence of requests q 1  through q n ) Second, the transformation must not cause the destination database  120  to achieve a state that the source database  108  did not achieve. The introduction of extraneous states in the destination database  120  would complicate the definition of accuracy. More precisely, s, s!q′ 1 , s!q′ 1 !q′ 2 , . . . , s!q′ 1 ! . . . !q′ m  must be a subsequence of s, s!q 1 , s!q 1 !q 2 , . . . , s!q 1 ! . . . !q n  for any state s. A common kind of compression transformation is to eliminate a request that does not change the object&#39;s state. 
     Table 1 lists example compression transformations for an object type Table of one embodiment of the present invention. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Example compression transformations. 
               
            
           
           
               
               
               
            
               
                   
                 Original Sequence 
                 Transformed Sequence 
               
               
                   
                   
               
               
                   
                 insert(x 1 , . . . , x n ) 
                 (empty sequence) 
               
               
                   
                 delete(x 1 , . . . , x k ) 
               
               
                   
                 insert(x 1 , . . . , x n ) 
                 insert(x 1 , . . . , x′ i , . . . , x n ) 
               
               
                   
                 set(x 1 , . . . , x k , i, x′ i ) 
               
               
                   
                 set(x 1 , . . . , x k , i, x′ i ) 
                 delete(x 1 , . . . , x k ) 
               
               
                   
                 delete(x 1 , . . . , x k ) 
               
               
                   
                 set(x 1 , . . . , x k , i, x′ i ) 
                 set(x 1 , . . . , x k , i, x″ i ) 
               
               
                   
                 set(x 1 , . . . , x k , i, x″ i ) 
               
               
                   
                   
               
            
           
         
       
     
     In one embodiment, the compression algorithm reorders one or more commutative requests. Two requests q and q′ commute if for any object state s, applying the requests in either order results in the same final object state and the responses to the requests:
         s!q!q′=s!q′!q   s!q?q′=s!q′   s!q′!q=s!q       

     If requests don&#39;t commute, they are said to conflict. Requests with different key values x 1 , . . . , x k  commute. So do set and get requests for different columns of the same row. The get operation has no effect on the object state, so two instances of the same get request also commute. Request commutativity permits reordering that can open further opportunities for compression. Assume that the request logging function  110  for a table object has eliminated get requests and failed insert/set/delete requests. The sequence can be reordered so that requests on the same row(x 1 , . . . , x k ) object are adjacent. Furthermore, the set requests between an insert request and a delete request can be reordered so that set requests for the same column are adjacent. Once this reordering is done, the compression transformations can be applied to drastically reduce the number of requests that must be sent to the destination object. 
     Commutative requests can be reordered, compressed, and propagated independently. More significant state changes can be propagated immediately, while others are held back and possibly compressed further before being propagated. For example, an application may require higher accuracy for some object columns than for others. 
     In one embodiment, the source database  108  is a container object. When requests commute, such as requests with different key values x 1 , . . . , x k , it suggests that the object&#39;s state space has some structure, and that the requests operate on different parts of the object. The object is behaving as a container object that has contained objects as part of its state. 
     The behavior type of a container object reveals a structure in the object&#39;s internal state space and an association between requests and specific parts of the state space. These properties are captured in a container specification. 
       FIG. 2  illustrates a container specification  200  for the above example object type Table for one embodiment of the present invention. A container specification, such as container specification  200 , includes a containment tree  202 , assignment of state variables to objects (or nodes) in the tree  208  and  212 , and assignment of requests to object  208  and  212 . 
     First, a container specification  200  for the above example object type Table includes a containment tree  202 . A root object  208  represents the entire container object. In one embodiment, the root object is an object that contains a row(x 1 , . . . , x k ) object  210  for each key value (x 1 , . . . , x k ). The root object  208  has no assigned state variables or requests. The objects other than root object  208  represent contained objects  212 . Each contained object  212  is identified by a name and zero or more “key” values. 
     Second, container specification  200 , for the above example object type Table, assigns state variables to contained objects  212  in the containment tree  202 . The state of an object  212  is held by the state variables assigned to that object and its descendants. In one embodiment, each row(x 1 , . . . , x k ) object  210  is assigned the state variable exists(x 1 , . . . , x k ). In one embodiment, each row(x 1 , . . . , x k ) object  210  in turn contains descendants, which are one or more cell(x 1 , . . . , x k , i) objects  214  for k&lt;i≦n. In turn, each cell(x 1 , . . . , x k , i) object  214  is assigned state variable col i  (x 1 , . . . , x k ). 
     Third, container specification  200 , for the above example object type Table, assigns every request q in Q to an object  212  in containment tree  202 . Request q may be assigned to an object (for example, object  214 - 1 ) if 1) for any container object  208  states, the new state s!q and the response s?q are functions only of the state of contained object  214 - 1  and the state variables assigned to contained object  214 - 1 &#39;s ancestors in the containment tree  202 , and 2) any differences between the old state s and the new state s!q must be in the state of contained object  214 - 1 . Whenever two requests q and q′ are assigned to independent contained objects  212  (distinct contained objects, neither of which is an ancestor of the other in the containment tree), the requests must commute, because neither can update a state variable that the other can read or update. However, it is possible for two commutative requests to be assigned to objects that are not independent. Illustrated in  FIG. 2 , in one embodiment for the above example object type Table, each row(x 1 , . . . , x k ) object  210  is assigned requests insert(x 1 , . . . , x k ) and delete(x 1 , . . . , x k ) and each cell(x 1 , . . . , x k , i) object  214  is assigned requests set(x 1 , . . . , x k , i, x i ) and get(x 1 , . . . , x k , i). 
     In one embodiment, request reordering can be controlled by using an appropriate container specification  200 . If two requests are assigned to independent contained objects  212 , they must commute. By making this assignment, the system designer is stating that reordering these requests is acceptable. Alternatively, the system designer may assign the requests to the same object, or to distinct objects, one of which contains the other. This indicates that reordering is not acceptable even if the requests are commutative. 
     Given a container specification, a sequence of requests can be transformed to a partially-ordered set of requests. In  FIG. 3   a , a partially-ordered set of requests for a container specification  300  for one embodiment of the present invention is illustrated. A partial order is a set of sequencing constraints  310  among the requests  320 , indicated by the arrows between requests, selected by the database system designer. 
     A partial order is a set of sequencing constraints among requests that represents all permissible opportunities for request reordering. Requests  320  are arranged in a horizontal position that reflects their order in log  112 , and a vertical position that reflects their assignment to objects  330 - a  through  330 - e  in a containment tree  340 . For convenience, special nodes start  350  and now  355  are shown. The partial order is constructed by update propagation function  114 , by placing a sequencing constraint  310  (indicated by the arrows) between any two requests  320  that are assigned to the same object  330 - a  through  330 - e  in the containment tree  340  or assigned to two objects one of which is an ancestor of the other (e.g. object  330 - b  is an ancestor of objects  330 - d  and  330 - e ). 
     Object states are represented in this graph by dashed lines  360  and dotted lines  370 - 1 ,  370 - 2  and  370 - 3 , as illustrated in  FIG. 3   b . Dashed lines  360  represent the states of all objects  330 - a  through  330 - e  in source object  108 , from state so at time to, to the current source object  108  state s j  at time t j  (shown generally at  380 ). Dotted lines  370 - 1 ,  370 - 2  and  370 - 3  represent other states that could be propagated to the destination object. When replicating a container object, it is possible to propagate a state of the entire container object (i.e. object  303 - a  and its descendant objects  303 - b  through  303 - e ) or of any of its contained objects (i.e. objects  303 - b  through  303 - e ). 
     For any state s i  of the source object  108  at time t i , s in  is the corresponding state of one of the contained objects n (i.e. objects  303 - b  through  303 - e ). For example, in one embodiment, S lb  is the state of object  330 - b  at time t 1 . To propagate state s in , update propagation function  114  identifies the highest k≦i such that request q k  (one of requests  320 ) is assigned to object n (i.e. one of objects  330 - a  through  330 - e ) or one of its descendants. It then propagates request q k  and all its predecessors in the partial order that have not yet been sent to destination object  120 .  FIG. 3   b  illustrates one embodiment where requests  320  must be sent to propagate state s id    370 - 1 , state s ib    370 - 2 , and s ia  (=s i )  370 - 3 . In this embodiment, to propagate state s ib , request sequence q i−3 , q i−2 , q i−l  or q i−3 , q i−1 , q i−2  must be sent. Notice that if q i−l  is propagated before q i−2 , the destination object will achieve a state that the source object never achieved. 
     In one embodiment, update propagation function  114  propagates requests  320  in an order consistent with the partial order. In one embodiment, update propagation function  114  compresses requests  320  consistent with the partial order prior to propagation to destination object  120 . 
     In  FIG. 4 , a method for compressing a sequence of requests  400  affecting source object  108 , where source object  108  is a container object, before performing an update propagation to a destination object  120 , is disclosed. The partial order of requests induced by a container specification allows requests to be reordered to allow further compression. In one embodiment, requests are first reordered so that requests that are assigned to the same node in the containment tree are adjacent to each other ( 410 ). The relative order of requests assigned to the same node, and requests assigned to distinct but not independent nodes, must be maintained. Then, apply the compression transformations as described earlier ( 420 ). The destination object  120  must have the same type as source object  108  and therefore also be a container object 
     Information Quality of Service (IQoS). Information quality of service for database state propagation is measured in terms of freshness and accuracy. Freshness measures how long it takes updates to propagate from source database  108  to destination database  120 . Accuracy measures how closely destination databases  120 &#39;s states track those of source database  108 . If system  100 &#39;s replication freshness and accuracy requirements can be explicitly defined, the replication function  106  can exploit the flexibility provided by those requirements to optimize use of communications network  118  resources. 
     IQoS Accuracy Requirements. The IQoS accuracy requirement controls the selection of which states will be propagated from the source database  108  to the destination database  120 . A source database  108  state must be propagated only if the change from the previously propagated state is large enough (Δs) and the state has persisted for a sufficient time interval (Δt). The two parameters (Δs, Δt) constitute the accuracy IQoS requirement. In one embodiment, the accuracy requirement (0, Δt) causes every state that persists for a time interval of Δt to be propagated to destination database  120 . In one embodiment, the accuracy requirement (Δs, 0) causes every state representing a change of magnitude Δs or larger to be propagated to the destination database  120 . In one embodiment, the accuracy requirement (0, 0) causes every state to be propagated to destination database  120 . 
     In other embodiments, the two accuracy parameters (Δs, Δt) are chosen to reduce network resource costs. In one embodiment the accuracy requirement parameter Δt is a function Δt(Δs). With Δt expressed as this function, a state change of magnitude Δs must be propagated if it persists for a time interval of at least Δt(Δs) (i.e., the accuracy requirement fully expressed is (Δs, Δt(Δs).) In one embodiment, this function is monotonically non-increasing, so that larger changes are propagated sooner, but smaller changes are propagated over a relatively longer period. 
     In one embodiment, the magnitude of an object state change is measured using a distance function. A distance function d(s, s′) returns a non-negative real number and has the following properties:
         d(s, s′)=0 if and only if s=s′   d(s, s′)=d(s′, s)   d(s, s′)+d(s′, s″)≧d(s, s″)       

     The sequence of source database  108  states to be propagated to destination database  120  is determined as follows. State so (the initial state of both the source database  108  and destination database  120 ) is assumed to be “propagated” because both the source and destination objects are initially in this state. Suppose that source database  108  state s i  has previously been propagated to destination database  120 , and that t is the current time. Source database  108  state s j  is the next state to propagate to destination database  120  if j is the lowest integer greater than i that meets the propagation condition:
 
 d ( s   i   ,s   j )≧Δ s  and  t≧t   j   +Δt.   (1)
 
In other words, the propagation condition is met if the magnitude of the source database  108  state change from s i  to s j  is greater than or equal to the parameter Δs, and the state s j  has existed on the source database  108  for a time greater than or equal to Δt without being propagated to the destination database  120 .
 
     In one embodiment, the following function defines a distance function for the previously discussed example object type Table: 
               d   ⁡     (     s   ,     s   ′       )       =       ∑       (       x   1     ,           ⁢   …   ⁢           ,     x   k       )     ∈       D   1     ×   …   ×     D   k                   ⁢           ⁢     (              exists   ⁡     (       x   1     ,   …   ⁢           ,     x   k       )       -       exists   ′     ⁡     (       x   1     ,   …   ⁢           ,     x   k       )              +           ∑     k   &lt;   i   ≤   n           exists   ⁡     (       x   1     ,           ⁢   …   ⁢           ,     x   k       )       =   1             exists   ′     ⁡     (       x   1     ,           ⁢   …   ⁢           ,     x   k       )       =   1       ⁢       d   i     ⁡     (         col   i     ⁡     (       x   1     ,   …   ⁢           ,     x   k       )       ,       col   i   ′     ⁡     (       x   1     ,   …   ⁢           ,     x   k       )         )           )             
Here, the state variables exists and col i  represent state s, and state variables exists′ and col′ i  represent state s′. In this embodiment, the distance function measures the difference between two states s and s′ as the sum of two terms: the number of key values that exist in either s or s′, but not both, and the distances between non-key column values (measured by a more primitive distance function d i ), summed over all columns and all key values that occur in both states. In other embodiments, variants of this distance function apply weights to the various terms, representing their application significance.
 
     The update propagation function must evaluate the propagation condition (1) above, which references past states of the source object in the term d(s i , s j ). Storing a complete representation of past object states is not always practical, but is also not always necessary. In many cases, much less information needs to be stored. For example, the distance function above is a sum of terms, where each term references a single table row and possibly a single table column. Each request affects a single row or a single column, so it causes an incremental change to the distance function. To determine this incremental change, it is sufficient to record the old value x i  with each set(x 1 , . . . , x k , i, x′ i ) request and the old non-key values x k+1 , . . . , x n  with each delete(x 1 , . . . , x k ) request. Using just this information, it is possible to compute the distance d(s i , s j ) by accumulating the effect of requests q i+1 , . . . , q j . 
     In one embodiment, IQoS accuracy requirements are defined for a container object. In this embodiment, the source object  108  starts in state so and achieves states s 1 , s 2 , . . . , at times t 1 , t 2  . . . in response to requests q 1 , q 2 , . . . . The IQoS accuracy requirement (Δs n , Δt n ) controls state propagation for a container object  208  or contained object  212  as follows. Suppose that S jn  is a source object  108  state, s in  is the latest previous state that the replication function has propagated to the destination object, and t is the current time. State s jn  must be propagated if it meets the propagation condition:
 
 d   n ( s   in   ,s   jn )≧Δ s   n  and  t≧t   jn   +Δt   n   (2)
 
     For any state s i  of the source object  108 , s in  is the corresponding state of contained object n and t in  is the time when contained object n achieved this state. When object n represents the container object, this propagation condition (2) is the same as propagation condition (1) stated earlier for arbitrary objects. For contained objects, the definition is slightly different in that t jn  may be earlier than t j . Specifically, choose the largest k≦j such that q k  is assigned to an object n (i.e. node  108  or  112 ) or one of its descendants in the containment tree  202 . That is, q k  is the latest request reflected in state s j  that altered the state of object n. Then, t jn =t k . 
     As stated earlier, IQoS accuracy requirements control the selection of states to be propagated from the source to the destination object  120 . In one embodiment each object  208  and  212  in a container specification  200  has an associated IQoS accuracy requirement (Δs n , Δt n ) and an associated IQoS freshness requirement T pn , to control state propagation for that object according to application needs determined by the database designer. In another embodiment, one or more of the objects  208  and  212  in a container specification  200  have an associated IQoS accuracy requirement (Δs n , Δt n ) and associated distance function d n  (shown generally at  220 ). 
     In one embodiment, the following distance function is assigned to the root container object  208 : 
                 d   root     ⁡     (     s   ,     s   ′       )       =       ∑       (       x   1     ,           ⁢   …   ⁢           ,     x   k       )     ∈       D   1     ×   …   ×     D   k           ⁢            exists   ⁡     (       x   1     ,   …   ⁢           ,     x   k       )       -       exists   ′     ⁡     (       x   1     ,   …   ⁢           ,     x   k       )                      
and separate distance functions d i : D i ×D i →R are assigned to one or more of the contained objects  212 .
 
     In one embodiment, when n represents a container object, each distance function d n  is of the form: 
                 d   n     ⁡     (       s   n     ,     s   n   ′       )       =       ∑     m   ⁢           ⁢   is   ⁢           ⁢   a   ⁢           ⁢   descendant   ⁢           ⁢   of   ⁢           ⁢   n       ⁢       d     n   ⁢           ⁢   m       ⁡     (       v   m     ,     v   m   ′       )               
where v m  represents the state variables assigned to subspace m and d nm  is the contribution of subspace m to subspace n&#39;s distance function. The descendants of subspace n include n itself.
 
     IQoS Freshness Requirements. The IQoS freshness requirement places a propagation delay bound T p  on the propagation time for all propagated states s i  (i.e. t′ i ≦t i +T p ). In general, the larger the delay bound, the more flexibility the communications network  118  has in allocating resources to propagate the requests and the more resilient the replication function  106  is to temporary network unavailability. In other words, if an update can be propagated from the local object  108  to the destination object  120  over a longer period of time, then fewer network  118  resources, at a given point in time, are required to propagate the update. The IQoS freshness requirement T p  defines how long of a propagation time is tolerable once d(s i , s j )≧Δs. For a container object, the IQoS freshness requirement T pn  states that s jn  must be realized in the destination object  120  by time t jn +T pn . 
     According to the IQoS freshness requirement, any propagated state s j  must be realized in the destination object  120  at time t′ j ≦t j +T p . In the propagation condition (i.e. d(s i , s j )≧Δs and t≧t j +Δt), t references the current time. Accordingly, update propagation function  114  must wait until the current time t equal t j +Δt to determine whether state s j  should be propagated. The update propagation function  114  operates asynchronously with respect to local applications  104  that update local object  108 . A simple approach is to execute update propagation function  114  cyclically, with period T c . The database designer should choose period T c  so that the IQoS freshness requirement can be met as disclosed in this specification. 
     The constraints on T c  are as follows: Let T r  be the time required to realize a state s j  in destination object  120 , once update propagation function  114  begins execution. In a networking environment, especially one with intermittent connectivity such as a wireless network, this time cannot be fixed with certainty. Therefore, assume a value for T r  that can be met with sufficient assurance for the particular system and applications. In the worst case, update propagation function  114  begins execution just before time t j +Δt. Because the propagation condition d(s i , s j )≧Δs and t≧t j +Δt is not met, update propagation function  114  waits until time t j +Δt+T c  to determine that state s j  should be propagated to destination object  120 . The state s j  is then realized in destination object  120  at time t j +Δt+T c +T r . Because t j +Δt+T c +T r  should not exceed t j +T p , then T c  should be less than or equal to T p −T r −Δt. Accordingly, there are three cases to consider. First, if T r &gt;T p , the IQoS freshness requirement clearly can&#39;t be met regardless of the choice of T c . Second, if T r &lt;T p −Δt, then choosing T c =T p −T r −Δt will satisfy the IQoS freshness requirement. Third, if T p −Δt≦T r ≦T p , the IQoS freshness requirement can only be met by propagating additional states besides those identified by the propagation condition. In that case, update propagation function  114  can choose a smaller value of Δt so that T r &lt;T p −Δt and the second case holds. 
     For embodiments having container objects, because each node  208  and  212  can have its own IQoS freshness requirement T pn  and accuracy requirement (Δs n , Δt n ), in one embodiment, T c  is determined by computing a T cn  for each node, as describer above, and letting T c  equal the minimum valued T cn . 
     In  FIG. 5 , a method for propagating database updates  500  of an embodiment of the present invention is illustrated. The method starts by logging requests made to a source object by one or more applications ( 510 ). The magnitude of the source object state change since the time of the previous update propagation is determined ( 520 ). If the magnitude of the source object state change is greater than or equal to an information quality of service accuracy parameter Δs ( 530 ) and if the current source object state has existed for a time greater than or equal to an information quality of service accuracy parameter Δt without being propagated to the destination object ( 540 ), then the method proceeds with propagating a sequence of updates based on the logged requests to one or more destination databases ( 550 ). In some embodiments, the magnitude of the source object state change is determined by a distance function. In some embodiments, the method further comprises applying a compression algorithm that transforms the logged requests into a compressed sequence of requests to produce the sequence of updates. In some embodiments, the method further comprises determining how long of a duration of time is tolerable to propagate the source object state to the one or more destination objects once the magnitude of the source object state change is greater than or equal to Δs, and completing the propagation of the sequence of updates within that duration of time. 
     In  FIG. 6 , a method for propagating database updates  600  for a container object of an embodiment of the present invention is illustrated. The method starts by logging requests made to a source object by one or more applications ( 610 ). The magnitude of one or more source object node state change since the time of the previous update propagation is determined ( 620 ). If the magnitude of the one or more source object node state change is greater than or equal to an information quality of service accuracy parameter Δs defined for the one or more nodes ( 630 ) and if the current source object node state has existed for a time greater than or equal to an information quality of service accuracy parameter Δt (defined for the one or more nodes) without being propagated to the destination object ( 640 ), then the method proceeds with reordering requests so that requests assigned to the same node are adjacent ( 645 ). Next, compression transformations are applied to the reordered request ( 648 ) and the sequence of updates is propagated based on the compressed requests to one or more destination databases within time T pn  after the magnitude of the one or more source object node state change is greater than or equal to Δs ( 650 ). In one embodiment, the magnitude of the source object node state change is determined by a distance function. 
     Several means are available to implement the database replication embodiments of the current invention. These means include, but are not limited to, digital computer systems, programmable controllers, or field programmable gate arrays. Therefore other embodiments of the present invention are program instructions resident on computer readable media which when implemented by such controllers, enable the controllers to implement embodiments of the present invention. Computer readable media include any form of computer memory, including but not limited to magnetic disk or tape, CD-ROMs, DVD-ROMs, or any optical data storage system, flash ROM, non-volatile ROM, or RAM. 
     Although specific embodiments have been illustrated and described herein, it will be appreciated by those of ordinary skill in the art that any arrangement, which is calculated to achieve the same purpose, may be substituted for the specific embodiment shown. This application is intended to cover any adaptations or variations of the present invention. Therefore, it is manifestly intended that this invention be limited only by the claims and the equivalents thereof.