Patent Publication Number: US-7720841-B2

Title: Model-based self-optimizing distributed information management

Description:
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT 
   This invention was made with Government support under Contract No. H98230-05-3-0001 awarded by U.S. Department of Defense. The Government has certain rights in this invention. 

   FIELD OF THE INVENTION 
   The present invention generally relates to the field of distributed stream processing systems, and more particularly relates to managing information within a distributed stream processing system. 
   BACKGROUND OF THE INVENTION 
   Federated computing infrastructures such as Computational Grids and service overlay networks (“SON”) have become increasingly important to many emerging applications such as web service composition, distributed stream processing, and workflow management. As these computing infrastructures continue to grow, the efficient management of such large-scale dynamic distributed systems to better support application needs has become a challenging problem. Distributed information management services (which are further described in Robbert van Renesse, Kenneth Birman and Werner Vogels. Astrolab: A robust and scalable technology for distributed system monitoring, management, and data mining.  ACM Transactions on Computer Systems,  21(2):164-206, May 2003; P. Yalagandula and M. Dahlin. A Scalable Distributed Information Management System.  Proc. of SIGCOMM  2004, August 2004; and David Oppenheimer, Jeannie Albrecht, David Patterson and Amin Vahdat. Design and implementation trade-offs for wide area resource discovery. In HPDC-14, July 2005, respectively, and are herein incorporated by reference in their entireties) is one of the fundamental building blocks of system management, which can track dynamic system information and make it available via some query interfaces. 
   Applications running in the distributed environment can then query the current status of the system and make appropriate management decisions. For example, when a new application needs to be executed on a Grid system, a query “find 10 machines that have at least 20% free CPU time, 20 MB memory, and 2G disk space” can be issued to discover necessary resources. 
   However, providing scalable and efficient information management service for large-scale, dynamic distributed systems such as SONs is a challenging task. On one hand, quality sensitive applications running in such environment desire up-to-date information about the current system in order to better accomplish their application goals. On the other hand, the system can include a large number of geographically dispersed nodes (e.g., the World Community Grid consists of many thousands of nodes), and each node can be associated with many dynamic attributes (e.g., CPU load, memory space, disk storage, and other application level attributes). Obtaining accurate information about all nodes with their complete information inevitably involves high system overhead. 
   Distributed information management is critical for any large-scale system management infrastructure. For example, both the CoMon PlanetLab monitoring service and the Grid Monitoring/Discovery Service, (which are further described in K. Park and V. S. Pai. Comon: A mostly-scalable monitoring system for planetlab.  Operating Systems Review , Vol 40, No 1, January 2006, and K. Czajlowski, S. Fitzgerald, I. Foster, and C. Kesselman. Grid information services for distributed resource sharing. In HPDC-10, 2001, respectively, and are herein incorporated by reference in their entireties), have proven extremely useful for their user communities. However, both systems are statically configured. Every node pushes all attribute data to a central server at fixed intervals, even when the attribute data are unlikely to satisfy application queries. 
   Astrolabe and SDIMS, (which are further described in enter Robbert van Renesse, Kenneth Birman and Werner Vogels. Astrolab: A robust and scalable technology for distributed system monitoring, management, and data mining.  ACM Transactions on Computer Systems,  21(2):164-206, May 2003; P. Yalagandula and M. Dahlin. A Scalable Distributed Information Management System.  Proc. of SIGCOMM  2004, August 2004, respectively, and are herein incorporated by reference in their entireties), are two representative scalable distributed information management systems. The primary focus of these systems is aggregation queries such as MIN, MAX, and SUM. 
   Other systems such as Mercury, SWORD and PIER, (which are further described in Ashwin R. Bharambe, Mukesh Agrawal, and Srinivasan Seshan. Mercury: Supporting scalable multi-attribute range queries. In  SIGCOMM  2004, August 2004; David Oppenheimer, Jeannie Albrecht, David Patterson and Amin Vahdat. Design and implementation trade-offs for wide area resource discovery. In HPDC-14, July 2005, and Ryan Huebsch, Joseph M. Hellerstein, Nick Lanham, Boon Thau Loo, Scott Shenker and Ion Stoica. Querying the internet with PIER. In  Proceedings of  29 th    VLDB Conference,  2003, respectively, and are herein incorporated by reference in their entireties), can support multi-attribute queries. However, their focus is on how to resolve queries in different decentralized architectures. 
   Additionally, there has been work on query pattern/workload estimation (such as that described in N. Bruno, S. Chaudhuri, and L. Gravano. Stholes: A multidimensional workload-aware histogram. In  ACM SIGMOID  2001, May 2001, and Yi-Leh Wu, Divyakant Agrawal, and Amr El Abbadi. Query estimation by adaptive sampling. In 18 th    International Conference on Data Engineering  ( ICDE&#39; 02), 2002, which are hereby incorporated by reference in their entireties), in the database community. The goal is often to build appropriate histograms to estimate the data distribution, so that different query plans can be evaluated more accurately. 
   Therefore a need exists to overcome the problems with the prior art as discussed above. 
   SUMMARY OF THE INVENTION 
   Briefly, in accordance with the present invention, disclosed are a method, information processing stream, and computer readable medium for managing data collection in a distributed processing system. The method includes dynamically collecting at least one statistical query pattern associated with a selected group of information processing nodes. The statistical query pattern is dynamically collected from a plurality of information processing nodes in a distributed processing system. At least one operating attribute distribution associated with an operating attribute that has been queried for the selected group is dynamically monitored. The selected group is dynamically configured, based on the query pattern and the operating attribute distribution, to periodically push a set of attributes associated with the each information processing node in the selected group 
   In another embodiment an information processing system for managing data collection in a distributed processing system is disclosed. The information processing system comprises a memory and a processor that is communicatively coupled to the memory. An information management system is coupled to the memory and the processor. The information management system is for dynamically collecting at least one statistical query pattern associated with a selected group of information processing nodes. The statistical query pattern is dynamically collected from a plurality of information processing nodes in a distributed processing system. At least one operating attribute distribution associated with an operating attribute that has been queried for the selected group is dynamically monitored. The selected group is dynamically configured, based on the query pattern and the operating attribute distribution, to periodically push a set of attributes associated with the each information processing node in the selected group. 
   In yet another embodiment, a computer readable medium for managing data collection in a distribute processing system disclosed. The computer readable medium comprises instructions for dynamically collecting at least one statistical query pattern associated with a selected group of information processing nodes. The statistical query pattern is dynamically collected from a plurality of information processing nodes in a distributed processing system. At least one operating attribute distribution associated with an operating attribute that has been queried for the selected group is dynamically monitored. The selected group is dynamically configured, based on the query pattern and the operating attribute distribution, to periodically push a set of attributes associated with the each information processing node in the selected group. 
   One advantage of the present invention is that it provides a self-optimized distributed information management system. The information management system can dynamically/adaptively configure its data collection and query resolution operations based on dynamic query patterns and system conditions. Based on the statistical and node attribute distribution information, the information management system can dynamically configure a subset of worker nodes to periodically push a subset of their attribute data. The subset of nodes and attributes are selected so that most queries can be resolved by the push data. For the remaining queries, the information management system invokes pull operations on-demand to acquire the necessary information for their resolution 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The accompanying figures where like reference numerals refer to identical or functionally similar elements throughout the separate views, and which together with the detailed description below are incorporated in and form part of the specification, serve to further illustrate various embodiments and to explain various principles and advantages all in accordance with the present invention. 
       FIG. 1  is a block diagram illustrating a distributed processing system according to an embodiment of the present invention; 
       FIG. 2  is a block diagram illustrating an information management system according to an embodiment of the present invention; 
       FIG. 3  is a two dimensional graph illustrating the effect of attribute solution according to an embodiment of the present invention; 
       FIG. 4  is a two dimensional graph illustrating the effect of a filtering threshold according to an embodiment of the present invention; 
       FIG. 5  is a two dimensional graph illustrating a two-dimensional subspace selection according to an embodiment of the present invention; 
       FIG. 6  is a two dimensional graph illustrating query positioning according to an embodiment of the present invention; 
       FIG. 7  is a block diagram illustrating an exemplary system architecture according to an embodiment of the present invention; 
       FIG. 8  is a more detailed view of the processing nodes of  FIG. 7  according to an embodiment of the present invention; 
       FIG. 9  is an operational flow diagram illustrating overall process of dynamically configuring overlay nodes in a distributed processing system according to an embodiment of the present invention; 
       FIG. 10  is an operational flow diagram illustrating an exemplary process of processing selecting an attribute set to be pushed by an overlay node according to an embodiment of the present invention; 
       FIG. 11  is an operational flow diagram illustrating an exemplary process of configuring a filtering threshold according to an embodiment of the present invention; and 
       FIG. 12  is an operational flow diagram illustrating an exemplary process of configuring a push interval according to an embodiment of the present invention. 
   

   DETAILED DESCRIPTION 
   As required, detailed embodiments of the present invention are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the invention, which can be embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present invention in virtually any appropriately detailed structure. Further, the terms and phrases used herein are not intended to be limiting; but rather, to provide an understandable description of the invention. 
   The terms “a” or “an”, as used herein, are defined as one or more than one. The term plurality, as used herein, is defined as two or more than two. The term another, as used herein, is defined as at least a second or more. The terms including and/or having, as used herein, are defined as comprising (i.e., open language). The term coupled, as used herein, is defined as connected, although not necessarily directly, and not necessarily mechanically. The terms program, software application, and the like as used herein, are defined as a sequence of instructions designed for execution on a computer system. A program, computer program, or software application may include a subroutine, a function, a procedure, an object method, an object implementation, an executable application, an applet, a servlet, a source code, an object code, a shared library/dynamic load library and/or other sequence of instructions designed for execution on a computer system. 
   Distributed Processing System 
   According to an embodiment of the present invention, as shown in  FIG. 1 , a high level overview of an exemplary distributed processing system  100  is shown. The distributed processing system  100  of  FIG. 1  includes one or more overlay nodes  102 , management nodes  104 , and monitoring sensor modules  106 .  FIG. 1  also shows query clients  108  such personal computer, work stations, and the like. In one embodiment, the overlay nodes  102 , in one embodiment, execute various application tasks. For example, an overlay node  102  (host node) is a processing node in the distributed processing system  100  that performs one or more stream processing functions (e.g., correlation, aggregation, select, etc.) 
   The management nodes  104  monitor the status of all overlay nodes  102  and perform system management tasks, for example, job scheduling, resource allocation, system trouble-shooting, and the like. The monitoring sensor modules  106 , in one embodiment, monitor each host overlay node  102  and provide information associated with the host overlay node  102  to each of the management nodes  104 . In one embodiment, the sensor module  106  can be monitoring software that collects dynamic information about a local overlay node. An information management system  200  ( FIG. 2 ) resides within each of the management nodes  104  and is discussed in further detail below. The information management system  200  resolves information queries from other system management modules or user applications. 
   In one embodiment, each overlay node  102 , which can be a personal computer, workstation, or the like is monitored by one or more of the management nodes  104 . Each overlay node  112  is associated with a set of attributes, for example, CPU load, number of disk accesses, and the like. In one embodiment the set of attributes for an overlay node  102  can be denoted as A={a 1 , . . . ,a |A| }. Table 1 below summarizes the notations used throughout this discussion. 
   
     
       
         
             
           
             
               TABLE I 
             
           
          
             
                 
             
             
               Notations 
             
          
         
         
             
             
             
             
          
             
               notation 
               meaning 
               notation 
               meaning 
             
             
                 
             
             
               N 
               total number of overlay nodes 
               a 
               system state attribute 
             
             
               A 
               set of all attributes 
               A* 
               Subset of attributes to be pushed 
             
             
                 
             
             
               
                 
                   
                     
                       
                         f 
                         1 
                       
                       = 
                       
                         
                            
                           
                             A 
                             * 
                           
                            
                         
                         
                            
                           A 
                            
                         
                       
                     
                   
                 
               
               fraction of pushed attributes 
               T 
               Push interval 
             
             
                 
             
             
               T i   *   
               optimal push interval for a i   
               T i   
               Staleness constraint of a query 
             
             
               S 1   
               size of push message 
               S 2   
               Size of probe message 
             
             
               λ 
               average query arrival rate 
               n 
               Average probing overhead 
             
             
               p 1   
               % of resolvable queries using A *   
               l 1   
               Lower bond requirement for a i   
             
             
               l i   *   
               (optimal) filtering threshold for a i   
               f 2   
               % nodes in the push subspace 
             
             
               p 2   
               % of queries in the push subspace 
               p 3   
               % queries satisfied by the push intervals 
             
             
                 
             
          
         
       
     
   
   Each attribute a i  is denoted by a name, for example, CPU, memory, or the like) and a value, for example 10%, 20 KB, or the like. It should be noted that unless stated otherwise a i  is used throughout this discussion to represent both the name and value of the attribute. The management node  104 , which in one embodiment can be an information processing system such as a personal computer, workstation, or the like, is responsible for monitoring the distributed system  100 . The management node  104 , in one embodiment, provides information to one or more query nodes  108  comprising applications requesting the information The query nodes  108  send a query  114  to the management node  104  wherein the management node  104  returns an answer  116  to the query  114 . In one embodiment, the management node  104  is pushed information from the overlay nodes  102  as shown by the dashed-dotted lines  110  and/or pulls information from the overlay nodes  102  as shown by the dashed lines  112 . This information is used by the management node  104  to provide the requested information to the query nodes  108 . In one embodiment, an information push occurs when one or more monitoring sensory modules  106  reports its current attribute data to the management nodes  104 . An information pull occurs when one or more management nodes  104  dynamically requests information from sensors to resolve one or more queries  114 . 
   In one embodiment, the queries  114  received by the management node  104  such as those for service composition and distributed stream processing applications can expressed as locating a set of overlay nodes  102  that have certain resources. In other words, this set of overlay nodes  102  can be represented as (a 1 ε[l 1 ,h 1 ])Λ(a 2 ε[l 2 ,h 2 ]). . . Λ . . . (a k ε[l k ,h k ]), where l i  and h i  are the desired lower bound and upper bound for a i , respectively. Each query  114  can also specify the number of overlay nodes  102  that are needed. The query answer  116 , in one embodiment, returns the specified number of overlay nodes  102 , each of which satisfies the query predicate. Additionally, each query  114  can also specify a staleness constraint T i  on a required attribute a i . The staleness constraint T i , in one embodiment, indicates a threshold for how old the attribute value used to resolve this query  114  can be. For example, the staleness constraint T i  can indicate that the attribute value has to be less than or equal to T i  seconds old. The staleness constraint gives applications more specific control on their query result. In one embodiment, if a query  114  does not specify such constraint, a default value (e.g., 30 seconds) can be used instead. 
   In one embodiment, each overlay node  102  includes a monitoring sensor module  106 . The monitoring sensor module  106  can be implemented as hardware and/or software. The monitoring sensor module  106  can be configured by the management node  104  to periodically push its information only when certain conditions are satisfied. The monitoring sensing module  106  can also respond to a dynamic probe with its current information. Such configurability allows the management node  104  to achieve adaptiveness based on statistical query patterns. 
   Exemplary Information Management System 
     FIG. 2  shows one example of an information management system  200  according to an embodiment of the present invention. The information management system  200 , in one embodiment, is model-based and self-optimized. Therefore, the information management system  200  can adaptively configure its data collection and query resolution operations based on dynamic query patterns and system conditions.  FIG. 2  shows the information management system  200  comprising host nodes  202  (overlay nodes), sensor modules  206  filtering modules  220 , and analysis modules  222 . 
   A host node  202 , in one embodiment, is a processing node in the distributed processing system  100  that performs one or more stream processing functions (e.g., correlation, aggregation, select, etc.). A sensor module  206  can be monitoring software that collects dynamic information about local host  202 . The filter module  220  is dynamically configured by the information management system  200  to filter out some raw monitoring data that is not needed by current queries. The selected information from the sensor  206  is sent to different analysis modules  222  (e.g., A 1 , A 2 , A 3 ) that issue queries about different hosts  202 . The information management system  200  dynamically derives query patterns from the queries generated by the different analysis modules  222 . Based on the derived query patterns and attribute distributions, the information management system  200  dynamically configure the filters  220  on different hosts  2002  to minimize overall information management cost. 
   In one embodiment, the information management system  200  achieves its adaptivity by maintaining dynamic statistical information such as query patterns and system attribute distribution associated with the distributed system. The information management system  200  can then derive analytical models that characterize the system cost under different configurations. In one embodiment, the information management system  200  uses the statistical information and analytical models to dynamically configure a subset of the worker nodes (overlay nodes  102 ) to periodically push a subset of their attribute data. The subset of overlay nodes  102  and attributes are selected so that most queries can be resolved by the push data. For the remaining queries not in the subset, the information management system  200  invokes pull operations on-demand to acquire necessary information for their resolution. 
   The self-adaptive information management system  200 , in one embodiment, can use a set of parameters for dynamically configuring the distributed processing system  100 . The information management system  200  can dynamically configure the subset of attributes that should be pushed by the overlay nodes  102 . The information management system  200  can also dynamically configure the push triggering threshold for each selected attribute, which filters out overlay nodes  102  that are unlikely to satisfy a query. An update interval for each pushed attribute can also be dynamically configured by the information management system  200  so that data is pushed at a frequency that the system can meet the staleness requirements of all queries with minimum push and pull cost. In one embodiment, the information management system  200  derives analytical models that characterize the system cost under different configurations and determines algorithms that can best configure the system parameters based on current query patterns and system conditions. 
   The information management system  200 , in one embodiment, is optimized by using patterns so that queries are satisfied with minimum information monitoring overhead. The information management system  200  can exploit various query patterns such as frequently queried attributes, frequently queried range values, and frequent staleness constraints. When combined with statistical information about the distributed stream processing system  100  itself, these query patterns allow the information management system  200  to automatically configure itself in order to minimize its management cost. 
   The automatic self-configuration, in one embodiment is based on dynamically maintained statistical information about the queries and distributed processing system  100  conditions. The first statistical pattern, the frequently queried attributes, which can be denoted as A*, is collected because even though overlay nodes  102  can be associated with many attributes, it is likely only a subset of these attributes are frequently queried by current applications. For example, in distributed applications where computing jobs are mainly CPU-bound, most queries specify requirements on the CPU resource, but not on other attributes. By keeping track of those popular attributes and configuring the overlay nodes  102  to only report these attributes periodically, the information management system  200  can improve the system efficiency and avoid unnecessary system cost. 
   For example,  FIG. 3  shows a graph  300  illustrating the advantage of pushing only a subset of attributes. The example of  FIG. 3  is based on a distributed processing system with 50 attributes. The x-axis  302  denotes the number of (most popular) attributes being pushed and the y-axis  304  denotes the corresponding system cost.  FIG. 3  shows that for different query patterns, pushing a subset of the attributes is more advantageous than pushing no attributes (i.e., pure pull) or all attributes (i.e., pure push). 
   The statistical pattern of frequently queried range values, in one embodiment, allows the information management system  200  to further reduce the system cost by filtering out unqualified attribute values. For example, if most queries on CPU time require a node to have at least 20% free CPU time, the overlay nodes  102  with less than 20% CPU free time do not need to push their CPU value since they are unlikely to satisfy the query predicate. In one embodiment, the monitoring sensor module  106  can be configured by the information management system  200  with a push triggering range [l i ,∞) for each selected popular attribute a i εA*. It should be noted that query predicates such as in resource queries often do not have upper-bound constraints. However, embodiments of the present invention can be extended to include a finite upper-bound. 
   In the above example, the monitoring sensor module  106  periodically pushes the attribute data only when the attribute value falls into the push triggering range. The range lower bound l i , in one embodiment is a filtering threshold for the attribute. By setting a filtering threshold, the information management system  200  can filter out unnecessary data pushes without significantly decreasing the query hit ratio (i.e., the percentage of queries that can be resolved by the pushed data).  FIG. 4  shows a graph  400  illustrating a filtering threshold selection for one attribute. The solid line  402  is the cumulative distribution function (“CDF”) of an attribute a 1  across all N nodes. The dashed line  404  is the CDF of the lower bound requirements from the current queries. As  FIG. 4  shows, 90% of the queries require the attribute to be greater than I, and only 74% of the overlay nodes  102  satisfy this requirement. Therefore, if the information management system  200  configures the filtering threshold to be I, then 74% of the overlay nodes  102  push their attribute data and 90% of the queries can be resolved by the pushed data. 
   However, if the information management system  200  increases the filtering threshold from I to I′, then only 20% of the overlay nodes  102  need to push their attribute data with a slight decrease of query hit ratio. Thus, the query pattern range requirement distribution of recent queries is monitored by the information management system  200  to configure proper filtering thresholds. As discussed above, the information management system  200  also monitors the frequent staleness constraints query pattern. For example, when an application makes a query  114 , the application can specify a staleness constraint T i . The staleness constraint indicates that the attribute data used to resolve the query  114  cannot be greater than T i  seconds old for attribute a i . In one embodiment, different queries for any attribute a i εA* can have different staleness requirements. As a result, the push interval (i.e., update period) of a i  is dynamically configured by the information management system  200  so that the push frequency is high enough to satisfy the staleness constraints of most queries. For example, if the staleness requirement is that the information should be no more than T seconds old, then the push frequency should be no lower than (1/T) times per second. 
   In addition to the query patterns, the information management system  200  also maintains an estimate of node attribute distribution (i.e., attribute distribution among all overlay nodes). The distribution can be used for two purposes. First, the information management system  200  can estimate the probing cost (i.e., the number of probes that are to be generated) based on the node attribute distributions. Second, the attribute distributions allow the information management system  200  to estimate the push cost reduction and pull cost increase when the filtering thresholds are configured for different attributes. In one embodiment, because the overlay nodes  102  can be associated with multiple attributes, the information management system  200  maintains multi-dimensional histograms to estimate the attribute distribution. The node attribute distribution can be obtained by executing infrequent aggregate queries (e.g., {\tt histogram}) over all the nodes. 
   As discussed above, the information management system  200  combines the push and pull for data collection thereby creating a management cost of a push cost and a pull cost. The push cost, in one embodiment, is the amount of data periodically delivered from different overlay nodes to the management node. The pull cost, in one embodiment, is the amount of data generated per time unit for pulling the attribute data in response to queries that cannot be resolved by the information management system  200  locally. One of the goals of the information management system  200  is to dynamically configure the monitoring sensor modules  106  so that the total system cost is minimized. 
   Corresponding to the application query patterns, there are at least three configuration parameters that the information management system  200  can tune. The first is the subset A* of attributes that are pushed. In other words, each monitoring sensor monitor  106  only periodically pushes a subset A* of attributes. When a query  114  arrives, if all the attributes the query  114  specifies is in A*, no additional cost is incurred. Otherwise, the information management system  200  uses an on-demand probing protocol identify enough nodes that satisfy the query. It should be noted that there are different ways for dynamic probing, e.g., using random sampling or on-demand spanning trees, or the like. Irrespective of the particular probing protocol, the information management system  200 , in one embodiment, assumes (in order to resolve a query by probing) that on average n nodes need to be contacted with 2n messages. In one embodiment, n can be obtained from previous probes. 
   Since each monitoring sensor module  106  periodically (every T seconds) pushes 
             f   1     =            A   *               A                
percentage of the attributes, it can be assumed that the message size is proportional to the number of attributes pushed, and S 1  is the size of the message if all |A| attributes are pushed. The push cost of the system can be expressed as
 
             1   T     ⁢     Nf   1     ⁢       S   1     .           
For example, suppose the average query arrival rate is λ and on average the information management system  200  needs to probe n nodes with 2n messages (probes and replies) to resolve a query by pull. Let p 1  denote the query hit ratio, and S 2  denote the size of a probe message. It should be noted that it is unlikely for a query  114  to specify requirements on many attributes, (as discussed in Ashwin R. Bharambe, Mukesh Agrawal, and Srinivasan Seshan. Mercury: Supporting scalable multi-attribute range queries. In  SIGCOMM  2004, August 2004; which is hereby incorporated by reference in its entirety). Therefore, in one embodiment, it can be assumed that the message size for both probe and reply is S 2 , which is a constant smaller than S 1 . However, this is only notational simplicity and does not limit the present invention.
 
   The pull cost of the entire distributed processing system  100  can then be 2n(1−p 1 )λS 2 . As a result, if only popular attributes are configured, and A* is the set of selected attributes, the total system cost is 
   
     
       
         
           
             
               
                 
                   
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   A larger A* implies larger push cost (i.e., higher f 1 ) but a lower pull cost (i.e., lower 1−p 1 ). Therefore, the information management system  200  dynamically selects A* based on the dynamically maintained statistical information, so that the overall system cost in Equation 1 above is minimized. Given a subset A* that has been selected, the information management system  200  can further reduce the system cost by selecting a filtering threshold l i * for each attribute a i εA*, and filtering out the overlay nodes  102  that do not satisfy the filtering thresholds. The set of filtering thresholds define a subspace {(a 1 ,a 2 , . . . ,a |A*| )|a 1 &gt;l i *,1≦i≦|A*|} in the |A*|-dimensional space. 
   In one embodiment, an overlay node  102  is “covered” by the subspace, if its value for each attribute a 1 εA* is above the filtering threshold. In one embodiment, a query  114  is “covered” by the subspace, if its lower bound requirement on each a i εA* is above the filtering threshold. If a query  114  is covered by the subspace, then all of the overlay nodes  102  that satisfy the query  114 , which are called the answer set of the query  114 , are covered by the subspace. Therefore, the query  114  can be locally resolved safely. For a query  114  not covered by the subspace, its answer set is not completely available. In this case, the information management system  200  assumes a probing operation is invoked so that the query result is not biased toward a subset of the answer set. 
   In one embodiment, an overlay node  102  reports its attribute data A* only if the node is covered by the subspace, and f 2  percent of the overlay nodes are covered by the subspace defined by the filtering thresholds. The push cost of the system is reduced to 
             1   T     ⁢     f   2     ⁢   N   ⁢           ⁢     f   1     ⁢     S   1           
since only the f 2  percentage of overlay nodes  102  perform periodic pushes. Correspondingly, if p 2  percent of the queries (among those that only specify attributes in A* are covered by the subspace, a total of (1−p 1 p 2 ) percent queries need to be resolved by dynamic pull. As a result, the total system cost becomes
 
   
     
       
         
           
             
               
                 
                   
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   A lower l i *, 1≦i≦|A*| implies larger push cost (i.e., higher f 2 ) but lower pull cost (i.e., lower (1−p 1 p 2 )). Therefore, another goal of the information management system  200  is to select a set of proper filtering thresholds l i * for all attributes a i εA* such that the total system cost in Equation 2 above is minimized. 
   To further reduce the system cost, each overlay node  102  can push the value of a i εA* every T i * seconds when the value is above the filtering threshold. The push cost for attribute a i  becomes 
               1     T   i   *       ⁢     f   2     ⁢   N   ⁢           ⁢     f   1     ⁢     S   1       +     2   ⁢     n   ⁡     (     1   -       p   2     ⁢     p   1         )       ⁢       λS   2     .             
Thus, the total push cost for all selected attributes is
 
             ∑       a   i     ∈     A   *         ⁢       1     T   i   *       ⁢     f   2     ⁢   N   ⁢           ⁢         S   1          A          .             
Suppose under the above configuration, p 3  percent of queries (among the p 2 p 1  percent of queries that specify attributes in A* and are covered by the subspace defined by the filtering thresholds) can satisfy their staleness constraints. Then a total of (1−p 3 p 2 p 1 ) percent queries need to invoke pull operations. Therefore, the total system cost for all three configuration parameters is
 
   
     
       
         
           
             
               
                 
                   
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   A lower T i *, in one embodiment, means higher push cost but lower pull cost due to a higher p 3 . Besides the monitoring cost, the information management system  200  also considers the query response time requirements. Generally, if the query  114  can be answered by the push data (i.e., a query hit) then the query response time is shorter than the when the query resolution involves pull operations (i.e., a query miss). Suppose the average response time for a query hit is R 1  and the average response time for a query miss is R 2 . α can denote the query hit ratio and R req  can denote the user required query response time constraint. Then, the optimal distributed information management problem can be formulated into the following constrained optimization problem, wherein the problem of optimal distributed information management is to adaptively select a subset of attributes A*, a filtering threshold l*, and a push interval T * , for each attribute εA*, such that 
                     ∑       a   i     ∈     A   *         ⁢     (       1     T   i   *       ⁢     f   2     ⁢   N   ⁢           ⁢       S   1          A            )       +     2   ⁢     n   ⁡     (     1   -       p   3     ⁢     p   2     ⁢     p   1         )       ⁢   λ   ⁢           ⁢     S   2               (     EQ   ⁢           ⁢   4     )               
is minimized subject to
 α· R   1 +(1−α)· R   2   ≦R   req   (EQ 5). 
   As discussed above, the pattern-driven self-configuring information management system  200  minimizes the system management cost by observing both query patterns and attribute distributions. Each management node  104 , in one embodiment, performs this cost minimization process in response to the changes of query patterns and node attribute distributions. 
   Attribute Selection 
   One of the goals of the attribute selection process performed by the information management system  200  is to select a subset of attributes A* ⊂ A so that the total system cost is minimized. According to Equation 1 above, A* can affect the push cost (i.e., f 1 =A*/A percent of complete attribute push cost) and the percentage p 1  of queries  114  that can be resolved by a management node  104  using the push data (i.e., query hit ratio). A larger A* implies a larger push cost but also a larger query hit ratio, while smaller A* implies a smaller push cost but also lower query hit ratio and thus higher pull cost. Therefore, the selection A*, in one embodiment, represents the trade-off between the push cost and pull cost. In one embodiment, the information management system  200  selects a proper subset A* such that the combined push and pull cost is minimized. 
   To quantify the relative merit of pushing a subset of attributes A i , the information management system  200  groups the queries  114  based on the subset of attributes specified by the queries  114 . For example, the information management system  200  uses a subset A i ={a 1 ,a 2 } to represent all queries  114  that specify requirements on attributes a 1  and a 2 . For each subset A i  the information management system  200  can determine a query frequency, denoted by freq(A i ), which means the percentage of all queries that are represented by A i . Suppose the monitoring sensor modules  106  are configured by the information management system  200  to push the attribute data in A i . For any A j   ⊂ A i , the queries that are represented by A j  can also be resolved by the push data. Therefore, cumulative query frequency of A i  can be defined as freq′(A i )=Σ A     j       ⊂ A     i   freq(A j ). This indicates the percentage of queries that can be resolved by the push data if the attributes in A i  are pushed. 
   Given the above, the relative cost reduction of a subset A i  can be defined to be the amount of pull cost saved minus the additional push cost incurred, if all attributes in A i  are pushed, which can be calculated as follows, 
   
     
       
         
           
             
               
                 
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   One example of a push attribute selection algorithm is described as follows and whose pseudo-code is given in Table 2 below. 
   
     
       
         
             
           
             
               TABLE 2 
             
             
                 
             
             
               Push attribute selection algorithm. 
             
             
                 
             
           
          
             
                 
             
          
         
         
             
             
          
             
                 
               Attribute Selection (T, N, A, S 1 , S 2 , n, λ) 
             
          
         
         
             
             
          
             
               1. 
               let f 1  = p 1  = 0, and A *  = θ 
             
             
               2. 
               compute min_cost using Equation(1) 
             
             
               3. 
               let C = {A i  ∈ A|freq(A i ) &gt; 0} 
             
             
               4. 
               while C ≠ θ do 
             
          
         
         
             
             
          
             
               5. 
               for each A i  ∈ C compute freq′(A i ) 
             
             
               6. 
               select A i  from C that has the largest cost reduction 
             
             
               7. 
               if the cost reduction of A i  is negative than break 
             
             
                 
             
             
               8. 
               
                 
                   
                     
                       
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               9. 
               p 1  = p 1  + freq′(A i ) 
             
             
               10. 
               compute min_cost using Equation(1) 
             
             
               11. 
               A *  = A *  ∪ A i   
             
             
               12. 
               for each A j  ∈ C set A j  = A j \A i   
             
             
               13. 
               merge duplicate subsets in C 
             
          
         
         
             
             
          
             
               14. 
               return A *   
             
             
                 
             
          
         
       
     
   
   Let C denote the collection of attribute subsets, each corresponding to a set of queries that specify the same attributes. Initially, A* is set to be empty, on other words, no attributes are pushed. Thereafter, the subset A i  with the largest cost reduction is repeatedly selected, and add A i  to A*. The attributes in A i  are removed from all other subsets in C. This can create duplicate subsets in C. For example, after the attributes in A i ={a 1 ,a 2 } are removed, the two subsets {a 1 ,a 3 } and {a 2 ,a 3 } are the same as each other. These subsets are then merged, and the cumulative query frequency is recomputed. 
   The above process is repeated, until either all attributes have been added to A* or if a new attribute subset is added total system cost increases. To implement the algorithm, the information management system  200  within a management node  104  keeps a sliding window of recently received queries and a moving average of p 1 . p 1  is the percentage of queries that only specify attributes in A*. When the observed p 1  is significantly different from the value predicted by the information management system  200 , a reconfiguration is triggered. It should be noted that the size of the sliding window and the reconfiguration triggering threshold decide how promptly the information management system  200  can respond to query pattern changes, and how often push attribute selection is performed. 
   In a worst case scenario, the while loop at line 4 in Table 2 is executed |C| times. For each loop, line 5 in Table 2 takes O(|C| 2 ) time because every pair of subsets need to be compared for inclusion test. The inclusion test for two subsets takes O(k 2 ) time, assuming k is the maximum number of attributes in a query. As a result, the worst case time complexity of the algorithm is O(|C| 3 k 2 ). 
   Filtering Threshold 
   As discussed above, the information management system  200  selects a filtering threshold. In other words, the information management system  200  selects a multi-dimensional subspace that can cover the optimal set of overlay nodes  102  and queries  114 .  FIG. 5  shows a graph  500  illustrating the subspace selection problem in a two-dimensional space. Each star  502  in the space corresponds to a query  114 , and each plus sign  504  corresponds to an overlay node  102 . As can be seen from  FIG. 5 , if the filtering threshold for a 1  and a 2  is set to be l 1  and l 2  respectively, one overlay node  102  does not push its attribute data. This is because the overlay node  102  is not covered by the subspace {(a 1 ,a 2 )|a 1 ≧l 1 Λa 2 ≧l 2 }. One query  114  needs to be resolved by the pull operation, because it is not covered by the subspace. However, if the filtering threshold is set to be l 1 ′and l 2 ′, five overlay nodes do not need to push their data, and three queries need to be resolved by the pull operations. 
   In the above description, it is assume that each query  102  has all |A*| coordinates, which means that it specifies requirements on each attribute a i εA*. In one embodiment, a query  114  may only specify a subset of the attributes in A*. Under those circumstances, the information management system  200  determines where to place the query  114  in the |A*|-dimensional space such that the subspace selection process can correctly classify it as resolvable by push data or not. This procedure is referred to “query positioning”. The following is an example illustrating the positioning procedure and is shown in  FIG. 6 .  FIG. 6  shows a graph  600  illustrating a two-dimensional space (i.e., A*={a 1 ,a 2 }) and a query q=(a 1 ≧l 1 ). 
   One intuitive way to place the query in the two dimensional space is to rewrite the query as q′=(a 1 ≧l 1 Λa 2 ≧0). Hence, the query is placed on the a 1  axis  602 . This, however, greatly limits the filtering capability of threshold selection since in order to cover this query, the threshold for a 2  must be 0. Therefore, the information management system  200  utilizes the node attribute distribution information to achieve more accurate query placement. For example, if the information management system  200  determines that among the overlay nodes  102  that satisfy a 1 ≧l 1 , the smallest a 2  value is l 2 . the query  114  can be rewritten as q″=(a 1 ≧l 1 Λa 2 ≧0). It should be note that this does not change the set of nodes that satisfy the query. However, it does affect the classification of queries as locally resolvable or not. If the push attributes for a 1  and a 2  are set to l 1  and l 2 , respectively, q″ is covered by the subspace, while q′ is not. Using the (conditional) attribute distribution, the queries  114  can be placed more accurately. 
   In one embodiment, query positioning requires the queries to be ran against the node attribute distribution. Multi-dimensional histograms can be used to estimate the attribute distribution of the nodes and queries. It should be noted that the query distribution is incrementally updated as queries arrive at the management node  104 . The node attribute distribution is periodically updated by executing an information aggregation query over all the nodes. Since the dimension might be high, only keep the bins that are non-empty are kept. Suppose all the attribute values are normalized to [0, 1.0], and the bin size for each dimension is d. Let B be the list of non-empty bins for the node attribute distribution. Each bin b 1 εB is described by a tuple of |A*|+1 fields. 
   The first |A*| fields define the bin, and the last field is the percentage of nodes in the bin. For example, b=(v 1 ,v 2 , . . . , v |A*| ,0.1) means 10% of the machines have attribute a i ε[v 1 ,v i +d),1≦i≦|A*|. Similarly, let B′ be the set of bins for the queries. B and B′ are bounded by the number of nodes in the system and the number of historical queries that are kept for estimating query patterns, which are smaller than a complete multi-dimensional histogram. Suppose the current filtering threshold is l i * for attribute a i . If a particular attribute a j  is analyzed and l j * is increased to l j *+d, the information management system  200  can determine how many overlay nodes  102  are removed from the subspace. The information management system  200  can also determine how many queries are removed from the subspace. This allows the information management system  200  to determine a cost reduction (i.e., the amount of push cost reduced minus the pull cost increased) for increasing l j * to l j *+d. 
   Therefore, the information management system  200  performs at least one or more of the following for configuring a filtering threshold. First, each filtering threshold l i * is initialized to be zero, which means every overlay node  102  periodically pushes its attribute data without any threshold filtering. Next, at each step, the information management system  200  selects one attribute a i  that has the largest cost reduction and increases the filtering threshold l i * by a step size d. The information management system  200  the removes the overlay nodes  102  and queries  114  that are not covered by the new subspace. The above process is repeated until the increase of any filtering threshold does not cause the system cost to decrease, or all overlay nodes  102  have been removed. Removal of all overlay nodes  102  indicates that all of the queries  114  have been resolved by the pull operations. 
   In the algorithm for configuring a filtering threshold whose pseudo-code is shown below in Table 3, the while loop at line 4 executes at most |B|=O(N) times. In each loop, line 5 computes the cost reduction for each dimension a i . To do this, the number of nodes and queries that are removed is computed when l i * is increased. This takes O(|A*|(|N|+|B′|)) time. As a result, line 5 takes O(|A*|(N+|B′|)N) time. In one embodiment, N is often smaller than |B′| decided by the number of queries. Thus, the computational complexity of the algorithm is O(|A*|·N·|B′|). 
   
     
       
         
             
           
             
               TABLE 3 
             
             
                 
             
             
               Filtering threshold selection algorithm. 
             
             
                 
             
           
          
             
                 
             
          
         
         
             
             
          
             
                 
               FilteringThresholdSelection(T, N, A, S 1 , S 2 , n, λ, A*) 
             
             
                 
               1. let l* i  = 0,1≦i≦|A*| and f 2  = p 2  = 1 
             
             
                 
               2. compute min_cost according to Equation (2) 
             
             
                 
               3. let B and B′ be the bins for nodes and queries 
             
             
                 
               4. while B ≠ θ do 
             
             
                 
               5.  select a i  that has the largest cost reduction 
             
             
                 
               6.  if the cost reduction is &lt; 0 then break 
             
             
                 
               7.  increase l i * to l i * + δ 
             
             
                 
               8.  remove all nodes and queries not covered by {l i *} 
             
             
                 
               9.  reduce the cost reduction from min_cost 
             
             
                 
               10. return {l i *} 
             
             
                 
                 
             
          
         
       
     
   
   Push Interval 
   The push interval configuration process performed by the information management system  200  can be described as follows. Suppose the information management system  200  selects a push interval T i * for each attribute a i εA*. In one embodiment, the push interval determines how often a monitoring sensor module  106  reports up-to-date attribute values to the management node  104  when the value is above the filtering threshold. On one hand, push intervals can affect the system&#39;s push cost since they decide the push frequency of the selected attributed data. On the other hand, push intervals also affect how many queries can be resolved by the push data satisfying their stableness constraints. A larger T i * means the attribute is pushed less frequently, and the pushed data is less likely to satisfy the staleness constraint of a query. The push interval configuration algorithm is similar to the filtering threshold configuration algorithm. Starting from the minimum push interval for each attribute, the information management system  200  repeatedly selects an attribute a i  and increases its corresponding push interval T i *. The attribute a i  is selected such that the increase of T i * results in the largest cost reduction. The above process is repeated until either the increase of T i * leads to increased system cost, or when all the push intervals have reached their maximum values. 
   System Architecture 
     FIG. 7  is a block diagram illustrating an exemplary architecture for the distributed processing system of  FIG. 1 . In one embodiment, the distributed processing system  100  can operate in an SMP computing environment. The distributed processing system  100  executes on a plurality of processing nodes  702 ,  704  coupled to one another node via a plurality of network adapters  706 ,  708 . Each processing node  702 ,  704  is an independent computer with its own operating system image  710 ,  712 , channel controller  714 ,  716 , memory  718 ,  720 , and processor(s)  722 ,  724  on a system memory bus  726 ,  728 , a system input/output bus  730 ,  732  couples  110  adapters  734 ,  736  and network adapter  706 ,  708 . Although only one processor  722 ,  724  is shown in each processing node  702 ,  704 , each processing node  702 ,  704  is capable of having more than one processor. Each network adapter is linked together via a network switch  738 . In some embodiments, the various processing nodes  702 ,  704  are able to be part of a processing cluster. All of these variations are considered a part of the claimed invention. It should be noted that the present invention is also applicable to a single information processing system. 
   Information Processing System 
     FIG. 8  is a block diagram illustrating a more detailed view of the processing node  704  of  FIG. 7 , which from hereon in is referred to as information processing system  800 . In one embodiment, the information processing system  800  is the management node  104  of  FIG. 1 . The information processing system  704  is based upon a suitably configured processing system adapted to implement the exemplary embodiment of the present invention. Any suitably configured processing system is similarly able to be used as the information processing system  704  by embodiments of the present invention, for example, a personal computer, workstation, or the like. The information processing system  704  includes a computer  802 . The computer  802  includes a processor  722 , main memory  718 , and a channel controller  714  on a system bus  726 . A system input/output bus  730  couples a mass storage interface  804 , a terminal interface  806  and a network hardware  706 . The mass storage interface  804  is used to connect mass storage devices such as data storage device  808  to the information processing system  704 . One specific type of data storage device is a computer readable medium such as a CD drive or DVD drive, which may be used to store data to and read data from a CD  810  (or DVD). Another type of data storage device is a data storage device configured to support, for example, NTFS type file system operations. 
   The main memory  718 , in one embodiment, includes the information management system  200 , which dynamically configures a subset of overlay nodes  102  to periodically push a subset of their attribute data. The subset of nodes and attributes are selected so that most queries can be resolved by the push data. For the remaining queries, the information management system  200  invokes pull operations on-demand to acquire the necessary information for their resolution. The information management system  200  has been discussed above in greater detail. The information management system  200 , in one embodiment, includes a dynamic statistics collector  812 , a node attribute distribution monitor  814 , and a dynamic node configurator  816 . 
   The dynamic statistics collector  812 , in one embodiment, collects statistics such as frequently queried attributes, frequently queried range values and frequent staleness constraints. These statistics have been discussed in greater detail above. The node attribute distribution monitor  814 , in one embodiment, monitors attribute distribution for estimating probing costs and the push cost reduction and pull cost increase when filtering thresholds are configured. Node attribute distribution has been discussed above in greater detail. The dynamic node configurator  816 , in one embodiment, configures a subset of overlay nodes  102  to periodically push a subset of their attribute data based on the dynamic statistical and node attribute distribution information. The dynamic node configurator  816  also dynamically configures the subset of attributes that are to be pushed, the push triggering threshold (filtering threshold) for each selected attribute, and the update interval for each pushed attribute. 
   Although only one CPU  722  is illustrated for computer  802 , computer systems with multiple CPUs can be used equally effectively. Embodiments of the present invention further incorporate interfaces that each includes separate, fully programmed microprocessors that are used to off-load processing from the CPU  722 . The terminal interface  806  is used to directly connect the information processing system  704  with one or more terminals  818  to the information processing system  704  for providing a user interface to the computer  802 . These terminals  818 , which are able to be non-intelligent or fully programmable workstations, are used to allow system administrators and users to communicate with the information processing system  104 . A terminal  818  is also able to consist of user interface and peripheral devices that are connected to computer  802 . 
   An operating system image  710  included in the main memory  718  is a suitable multitasking operating system such as the Linux, UNIX, Windows XP, and Windows Server  2003  operating system. Embodiments of the present invention are able to use any other suitable operating system. Some embodiments of the present invention utilize architectures, such as an object oriented framework mechanism, that allows instructions of the components of operating system (not shown) to be executed on any processor located within the information processing system  106 . The network adapter hardware  106  is used to provide an interface to a network  820  such as a wireless network, WLAN, LAN, or the like. Embodiments of the present invention are able to be adapted to work with any data communications connections including present day analog and/or digital techniques or via a future networking mechanism. 
   Although the exemplary embodiments of the present invention are described in the context of a fully functional computer system, those skilled in the art will appreciate that embodiments are capable of being distributed as a program product via a CD/DVD, e.g. CD  810 , or other form of recordable media, or via any type of electronic transmission mechanism. 
   Overall Process of Dynamically Configuring Nodes in a Distributed System 
     FIG. 9  illustrates an overall process for dynamically configuring the overlay nodes  102  to reduce system costs. The operational flow diagram of  FIG. 9  begins at step  902  and flows directly to step  904 . The information management system  200 , at step  904 , detects a query pattern of attribute distribution changes. The information management system  200 , at step  906 , selects a push attribute set. A filtering threshold, at step  908 , is determined by the information management system  200  for each attribute in the push attribute set. The information processing system  200 , at step  910 , determines a push interval for each attribute in the push attribute set. The information management system  200 , at step  912 , dynamically configures the overlay nodes  102  via their monitoring sensor modules  106  based on the above selected configuration parameters. This process is repeated each time a query pattern of node attribute distribution change is detected. 
   Exemplary Process of Selecting Attributes 
     FIG. 10  illustrates an exemplary process of selecting an attribute set. The operational flow diagram of  FIG. 10  begins at step  1002  and flows directly to step  1004 . The information management system  200 , at step  1004 , monitors received queries. The queries, at step  1006 , are analyzed to determined the request attributes. The queries, at step  1008 , are grouped together based on the subset of attributes specified by the queries. The information management system  200 , a step  1010 , determines a query frequency for each subset of attributes. The query frequency, as discussed above, is the percentage of all queries that are represented by each subset of attributes. A cumulative query frequency, at step  1012 , is determined, which is the percentage of queries that can be resolved by push data if the attributes in a given set of attributes are pushed. 
   The information management system  200 , at step  1014 , determines the cost reduction of each subset of attributes. The cost reduction, in one embodiment, is the amount of pull cost saved minus the additional push cost incurred if all attributes in a set of attributes are pushed. The information management system  200 , at step  1016 , selects the attribute set with the largest cost reduction. The information management system  200 , at step  1018 , determines if every attribute has been added to the group of attributes that are to be pushed. If the result of this determination is positive, the control flow exits at step  1020 . If the result of this determination is negative, the information management system  200 , at step  1022 , determines if adding another attribute subset increases the system cost. If the result of this determination is positive, the control flow exits at step  1024 . If the result of this determination is negative, the control returns to step  1018 , where the information management system  200  selects the next attribute set reduces the system cost the most. 
   Exemplary Process of Configuring a Filtering Threshold 
     FIG. 11  illustrates an exemplary process of configuring a filtering threshold discussed above. The operational flow diagram of  FIG. 11  begins at step  1102  and flows directly to step  1104 . The information management system  200 , at step  1104 , initializes each filtering threshold to zero. The information management system  200 , at step  1106 , selects at least one attribute that has the largest cost reduction. The filtering threshold, at step  1108 , is increased by a step size d. The information management system  200 , at step  1110 , removes the nodes and queries that are not covered by the new subspace. The information management system  200 , at step  1112 , determines if all the nodes have been removed. If the result of this determination is positive, the control flow exits at step  1114 . If the result of this determination is negative, the information management system  200 , at step  1116 , determines if the system cost has been decreased. If the result of this determination is negative, the control flow exits at step  1118 . If the result of this determination is positive the control flow retunes to step  1104  and the above process is repeated. 
   Exemplary Process of Configuring a Push Interval 
     FIG. 12  illustrates an exemplary process of configuring a push interval discussed above. The operational flow diagram of  FIG. 12  begins at step  1202  and flows directly to step  1204 . The information management system  200 , at step  1204 , selects a minimum push interval for each attribute in the collection of attribute sets. The push interval determines how often an overlay node  102  reports up-to-date attribute values to the information management system  200  when the value is above the filtering threshold. An attribute, at step  1206  is repeatedly selected and its corresponding push interval, at step  1208 , is increased. In one embodiment, an attribute is selected such that the increase of the its push interval results in the largest cost reduction. The information management system  200 , at step  1210 , if all push intervals reached their maximum values. If the result of this determination positive, the control flow exits at step  1212 . If the result of this determination is negative, the information management system  200 , at step  1214 , determines if the system cost has increased. If the result of this determination is positive, the control flow exits at step  1216 . If the result of this determination is negative, the control flow returns to step  1204  and the above process is repeated. 
   Non-Limiting Examples 
   The present invention as would be known to one of ordinary skill in the art could be produced in hardware or software, or in a combination of hardware and software. However in one embodiment the invention is implemented in software. The system, or method, according to the inventive principles as disclosed in connection with the preferred embodiment, may be produced in a single computer system having separate elements or means for performing the individual functions or steps described or claimed or one or more elements or means combining the performance of any of the functions or steps disclosed or claimed, or may be arranged in a distributed computer system, interconnected by any suitable means as would be known by one of ordinary skill in the art. 
   According to the inventive principles as disclosed in connection with the preferred embodiment, the invention and the inventive principles are not limited to any particular kind of computer system but may be used with any general purpose computer, as would be known to one of ordinary skill in the art, arranged to perform the functions described and the method steps described. The operations of such a computer, as described above, may be according to a computer program contained on a medium for use in the operation or control of the computer, as would be known to one of ordinary skill in the art. The computer medium, which may be used to hold or contain the computer program product, may be a fixture of the computer such as an embedded memory or may be on a transportable medium such as a disk, as would be known to one of ordinary skill in the art. 
   The invention is not limited to any particular computer program or logic or language, or instruction but may be practiced with any such suitable program, logic or language, or instructions as would be known to one of ordinary skill in the art. Without limiting the principles of the disclosed invention any such computing system can include, inter alia, at least a computer readable medium allowing a computer to read data, instructions, messages or message packets, and other computer readable information from the computer readable medium. The computer readable medium may include non-volatile memory, such as ROM, Flash memory, floppy disk, Disk drive memory, CD-ROM, and other permanent storage. Additionally, a computer readable medium may include, for example, volatile storage such as RAM, buffers, cache memory, and network circuits. 
   Furthermore, the computer readable medium may include computer readable information in a transitory state medium such as a network link and/or a network interface, including a wired network or a wireless network that allows a computer to read such computer readable information. 
   Although specific embodiments of the invention have been disclosed, those having ordinary skill in the art will understand that changes can be made to the specific embodiments without departing from the spirit and scope of the invention. The scope of the invention is not to be restricted, therefore, to the specific embodiments, and it is intended that the appended claims cover any and all such applications, modifications, and embodiments within the scope of the present invention.