Patent Publication Number: US-8972223-B2

Title: Platform matching systems and methods

Description:
PRIORITY AND RELATED APPLICATION 
     This application is a continuation of and claims priority under 35 U.S.C. §120 to prior U.S. patent application Ser. No. 12/117,966 filed May 9, 2008, now U.S. Pat. No. 8,219,358, entitled “Platform Matching Systems and Methods”. The entire content of this application is hereby incorporated by reference. 
    
    
     FIELD 
     The present invention relates to platform matching. In particular, the present invention relates to systems and methods to profile applications and benchmark platforms such that the applications may be matched to a most suitable computing platform. 
     BACKGROUND OF THE INVENTION 
     Businesses and other entities are becoming increasingly dependent upon custom or semi-custom software applications to perform a variety of processing. For example, financial services businesses develop and utilize a wide range of applications to perform important activities, such as trade processing, decisioning, settlement, and the like. Each application may have different processing characteristics. For example, one application may be particularly dependent on database operations and may require use of a computing platform that has efficient memory and disk storage operation. Another application may be computation intensive, and require a computing platform that is suited for performing efficient floating point operations. As a result, different applications may perform differently on different hardware or computing platforms. 
     Advances in computing hardware and software are continuing at a rapid pace. This rapid advancement has provided a wide range of choices in computing platforms, with different operating systems, processors, storage devices, and memory configurations. A business or other entity may run custom or other software applications on a variety of computing platforms. Unfortunately, however, there is no “one size fits all” computing platform. An application requiring efficient floating point operations may not perform as well on a computing platform that is designed for efficient memory and disk storage applications. It is desirable to provide systems and methods to allow a business or other entity to select the computing platform (from among more than one available platforms) that is the best fit for a particular software application. It is desirable to provide systems and methods to match the processing requirements of applications to the performance results of one or more computing platforms to determine the best platform for an application. It is further desirable to monitor applications during operation and automatically generate application resource usage data for use in further matching each application to a most desirable computing platform. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram overview of a system according to some embodiments of the present invention. 
         FIG. 2  is a flow chart of a method according to some embodiments of the present invention. 
         FIG. 3  is a further flow chart of a method according to some embodiments of the present invention. 
         FIG. 4  is a further flow chart of a method according to some embodiments of the present invention. 
         FIG. 5  is a block diagram of a system according to some embodiments of the present invention. 
         FIG. 6  is an illustration of a user interface according to some embodiments of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Applicants have recognized a need for an ability to match an application with the best fit computing platform from among more than one available computing platforms. Pursuant to some embodiments, a matching platform is provided to manage and administer this matching. 
     Some embodiments described herein are associated with platform matching systems and methods. As used herein, the phrase “software application” or “application” may refer to a software program (or related set of programs) coded (in any of a number of programming languages or techniques) to perform a desired set of services, tasks or operations on behalf of one or more users. The phrase “computing platform” may refer to computing hardware (including one or more processors, memory storage devices, input and output devices, etc.) and operating system software (such as Linux®, Unix, Windows®, or the like) packaged or integrated together so that software applications may be installed and operated on the computing platform. 
     As used herein, the term “services taxonomy” will refer to the description or characterization of each “service” performed by or utilized by an application. Put another way, as used herein, a “taxonomy” defines terminology associated with an application, and provides a coherent description of the components and conceptual structure of the computing platform requirements of the application. Each application has one or more “services”, and each “service” has a service nature or taxonomy. Each service nature or taxonomy describes or categorizes the primary characteristics of a service. For example, a software application may be described as having one or more concrete service types, such as a database service and a data communication service. Each service may have characteristics other than hardware performance that may be considered. For example, characteristics such as HA capabilities, power consumption, failure rates, licensing costs, support costs, operational costs, and the like may also be considered and included in a given services taxonomy. 
     Further details of how these service descriptions are used pursuant to some embodiments will be described below. Those skilled in the art will appreciate that a number of services taxonomies may be followed or used in conjunction with some embodiments, including the “TOGAF Service Categories” available through the OpenGroup at http://wvv-w.opengroup.org. For example, an application taxonomy for a particular application may be shown as follows:
         Database Management Service: 50%
           Memory Intensive: 15%   Disk Intensive: 35%   Network Intensive: 20%   Integer Intensive: 30%   
           Network Service: 25%
           Memory Intensive: 20%   Disk Intensive: 30%   Network Intensive: 20%   Integer Intensive: 30%   
           System and Network Management Service: 25%
           Memory Intensive: 25%   Disk Intensive: 20%   Network Intensive: 40%   Integer Intensive: 15%   
               

     That is, the application taxonomy shown above is used to describe an application that is most heavily dependent upon database management services, and is particularly disk intensive in operation. Pursuant to some embodiments, this type of a description of the services profile of an application is used by the matching platform to analyze different computing platforms to identify the most appropriate platform for use with the application. Those skilled in the art will appreciate that other taxonomies and usage profile breakdowns may be used. 
     In general, pursuant to some embodiments, a platform matching process and system are provided in which an application is matched with a best (or most cost effective, or most desirable) computing platform by first generating a baseline performance dataset by testing the application using a known or benchmark computing platform. A resource usage profile is generated using a desired services taxonomy, and suitable benchmark unit tests are selected to evaluate each of the services in the taxonomy. In some embodiments, generating an accurate resource usage profile may require runtime analysis of an application&#39;s component services with software tools that can capture resource usage metrics from the baseline platform during testing. In some embodiments, for example if resource usage measurement tools are not available, then a resource usage profile can be generated manually based on expert knowledge of application behavior. 
     Each of the benchmark unit tests are then run on one or more target platforms to arrive at a benchmark result dataset. The matching platform then evaluates the benchmark result dataset by comparing the dataset to the resource usage profile to identify the computing platform that is the “best fit” (or most cost effective, or most desirable) computing platform. The result is a systemized, repeatable and efficient process and system for evaluating a plurality of computing platforms to select a computing platform that will produce the most desirable results when used with a particular software application. In this manner, embodiments allow businesses and other organizations to select the best computing platform (from a set of platforms under evaluation) for use with each software application, resulting in better application performance. 
     Features of some embodiments will now be described by first referring to  FIG. 1 , where a block diagram of some components of a platform matching system  100  pursuant to some embodiments is shown. As shown, platform matching system  100  includes a matching platform  102  in communication with a plurality of computing platforms  104   a - n , one or more client devices  106 , application profile and baseline data  108 , unit tests  110 , and result data  112 . In general, matching platform  102  is used to control, administer, and analyze the overall matching process. Matching platform  102  may be configured as a computer server in communication with each of the other devices or systems using a communication network. As used herein, the phrase “communication network” may refer to, by way of example only, a Local Area Network (LAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a proprietary network, a wireless network, an Ethernet network, and/or an Internet Protocol (IP) network such as the Internet, an intranet, a direct (or wired) connection, and/or an extranet. 
     Matching platform  102  might comprise, for example, one or more personal computers, servers or the like. Although a single matching platform  102  is illustrated in  FIG. 1 , a plurality of platforms might be provided. 
     Matching platform  102  is in communication with one or more client devices  106  which may comprise, for example, networked computers in communication with matching platform  102  and used by, for example, end users who use each client device  106  to interact with matching platform  102 . For example, client device  106  may be operated by a technician who wishes to evaluate a number of target platforms  104  for use with a software application. The technician may interact with the matching platform  102  through the client device  106 . For example, the technician may use the client device  106  to specify a services taxonomy for the software application, and then use the client device  106  to control or establish a benchmark test sequence performed on a benchmark platform. The technician may then use the client device  106  to select a set of benchmark unit tests to be run on each of the target platforms  104 , and then use the device to manage or administer the running of each of the benchmark unit tests on each of the target platforms  104 . After all of the benchmark unit tests have been performed, the technician may use the client device  106  to run a “best fit” analysis of each of the target platforms to identify the target platform  104  that is the best or most appropriate platform to use with the software application under evaluation. Further details of this process will be provided below. 
     As shown in  FIG. 1 , matching platform  102  is in communication with a number of data stores, including, for example, unit test data  110 , result data  112 , and application profile/baseline data  108 . Some or all of these data stores may include data stored in a structured database, such as an XQuery capable database (where the data is stored in one or more XML files), although those skilled in the art will appreciate that other storage techniques and structures may also be used. Details of the data stored in some embodiments will be described further below. 
     To illustrate features of some embodiments, an example will now be provided. This illustrative example will be referenced throughout the remainder of this description. Those skilled in the art will appreciate that this example is illustrative but not limiting—other specific applications, platforms, and tests may be used with, and are within the scope of, embodiments of the present invention. In the illustrative example, a technician is tasked with the responsibility of evaluating two potential target platforms for use as the “production” or live computing platform to run or operate a financial services software application. The technician is responsible for selecting which of the two target platforms is best suited for use with the software application. 
     The two target platforms are:
         (1) A System ABC computing platform (referred to in the example as the “ABC” platform); and   (2) A System DEF computing platform (referred to in the example as the “DEF” platform).       

     In the illustrative example, the software application is referred to as the “ABC application”. The ABC application is a financial services software application that is primarily a database application and that will be used by a number of users on a network. The processing that may occur, pursuant to some embodiments, to match an application (such as the ABC application, in the example) to a most desirable, or most suitable (or “best fit”) computing platform will now be described by first referring to  FIG. 2  where a flow diagram of an overview of a platform matching process is provided. The flow charts described herein do not imply a fixed order to the steps, and embodiments of the present invention may be practiced in any order that is practicable. The process of FIG. may be performed, for example, using the matching system  100  of  FIG. 1 . 
     Processing of  FIG. 2  begins at  202  where an application to be matched to a computing platform is analyzed. Pursuant to some embodiments, an application is analyzed to measure its application resource usage profile by measuring, monitoring, and manipulating selected resource metrics. The application resource usage profile data is expressed as a percentage of overall application resource consumption of the application. For example (and as will be described in further detail below), processing at  202  includes determining which resources are used during runtime of an application, and a relative weight of usage of that resource. As a specific illustrative example, memory usage will be expressed as the time that is spent accessing memory during runtime divided by the total application runtime. As another specific illustrative example, integer intensive operations will be expressed as the time that is spent performing integer intensive processing during runtime divided by the total application runtime. The result is an application resource usage profile that identifies the relative weightings of application resource usage for an application (e.g., an application may then be described as being 50% memory intensive, etc.). The resulting application resource usage profile data is stored for each application for later use. 
     Processing continues at  204  where one or more target platforms are analyzed to create data identifying their performance characteristics. Processing at  204  may be performed separately from the processing at  202  so that a separate data store of platform data may be available for matching operations. Platform data may be created by performing a variety of benchmark unit tests on each platform and storing the results in a data store for access by the platform matching system. 
     Processing continues at  206  where a matching process (described further below) is performed to compare the application resource usage profile data with performance data from one or more available target platforms to identify which platform(s) are most suited to the application. In this way, applications may be matched to the best (or most desirable) computing platform, resulting in increased performance, cost savings or efficiency. Pursuant to some embodiments, processing at  206  is performed using matching platform  102  of  FIG. 1 . At  208 , an operator interacting with matching platform  102  may select from the most desirable platforms to pick the target platform to be used with the application. In this manner, an application can be matched to a best or most suitable platform in an efficient and repeatable manner. 
     Further details of the processing to match a target platform to an application will now be described by reference to  FIG. 3 .  FIG. 3  is a flow chart of a platform matching process according to some embodiments of the present invention. 
     According to some embodiments, some or all of the steps of  FIG. 3  may be directed or managed by a user interacting with a matching platform (such as platform  102  of  FIG. 1 ) via a client device (such as device  106  of  FIG. 1 ). Instructions and commands may be input by the user into a Graphical User Interface (GUI) display associated with the client device to interact with a matching application operated under the control of the matching platform. 
     Pursuant to some embodiments, the user begins the process of  FIG. 3  with several pieces of information—knowledge of the software application, and knowledge of the available computing platforms to be evaluated. For example, the user may be a technician or developer who understands the general design and operation of the software application, as well as the set of computing platforms to be evaluated. Continuing the illustrative example introduced above, the technician may begin the process of  FIG. 3  knowing that the ABC application is a financial services software application that is primarily a database application that will be used on a network, and that the ABC and DEF platforms are to be evaluated. 
     Processing begins at  302 , where the user generates a baseline set of data to begin the matching process. In some embodiments, the baseline set of data is obtained by running the software application on a known computing platform, and monitoring the resource usage characteristics of the software application. For example, a test hardware configuration may be used that is in communication with the matching platform. The baseline set of data may, for example, include the capture and storage of resource usage characteristics such as: database usage characteristics, data communication characteristics, system and network management characteristics, and the like. These characteristics may be measured using a set of standard benchmark tests designed to test each of the various characteristics. 
     Processing continues at  304  where a user interacts with the matching system to identify resource usage profile data and select a set of appropriate benchmark unit tests to evaluate the identified resources. In some embodiments, an objective is to measure the application resource usage of various system resources and express the application behavior profile as a breakdown by percentage of resources utilized. Applicants have recognized that one challenge is to translate the recorded resource metrics (which are often expressed as a consumption rate such as a disk write expressed as a bits per second throughput metric) into a form that can be expressed as a percentage of overall application resource consumption which is composed of disparate types of resources each with their own unit of measurement. It&#39;s not enough to know the throughput of a resources; instead, embodiments correlate the throughput to the amount of time the application spends accessing the resource. For example, an application may be doing 50 k bps writes, but if the disk write portion of a 10 minute application runtime is only 10 seconds long, then the disk write operation is only a small percentage of the application resource usage profile. 
     For example, in the illustrative example, the technician may identify that the ABC application has the following baseline resource usage profile, and with the following canonical form weightings:
         Multithreaded/Multiprocess: 5%   Floating Point: 0%;   Memory Intensive: 50%;   Integer Intensive: 25%;   Networking 20%       

     That is, the technician, based on baseline testing of the application on a known platform, has identified that the ABC application is comprised of services that are heavily dependent upon memory operations (with 50% of the canonical form weightings going toward how a platform performs in memory intensive operations), and is less dependent upon multithreaded and multiprocess services and floating point operation services. 
     This baseline resource usage profile will be used by the matching platform to analyze different computing platforms to identify the most appropriate platform for use with the ABC application. Those skilled in the art will appreciate that other taxonomies and usage profile breakdowns may be used. Pursuant to some embodiments, processing at  304  includes storing the baseline resource usage profile in a datastore accessible by the matching platform (e.g., such as in datastore  108  in  FIG. 1 ). 
     Those skilled in the art will appreciate that this baseline resource usage profile may be expressed in a number of ways. In one example embodiment, the baseline resource usage profile is expressed quantitatively so that it can fit into a ranking system (which will be described further below). In the example, based on the baseline resource usage profile established by the technician, the ABC and DEF computing platforms will be tested and evaluated and then ranked with the memory intensive service test results receiving a weight of 50%, the integer intensive service test results receiving a weight of 25%, etc. 
     Processing at  304  further includes the selection of individual benchmark unit tests to test each of the services identified as of importance to analysis of the application. Those skilled in the art will appreciate that a number of benchmark unit test procedures are available to test different resource usage characteristics. In some embodiments, the matching system may access or use data from a datastore (such as datastore  110  of  FIG. 1 ) to access information about available unit tests. For example, datastore  110  may store a library of available benchmark unit tests. 
     In one example embodiment, data may be stored in a structured manner, by using, for example, a relational database or as XML files in an XQuery capable database. In this manner, data from individual unit tests and matching processes may easily be stored, accessed, and manipulated by the matching platform. 
     Continuing the illustrative example introduced above, in the example, processing at  304  may include the technician selecting baseline unit tests that are appropriate for testing a platform&#39;s multithreaded/multiprocess, floating point, memory intensive, integer intensive, and networking services. The technician may select one or more unit test procedures to test each characteristic. As a specific illustrative example, the technician has selected the following unit test procedures to perform on each target computing platform: 
     
       
         
           
               
               
               
             
               
                   
                   
               
               
                   
                 Unit Test Name 
                 Canonical Form 
               
               
                   
                   
               
             
            
               
                   
                 ACE_QBW 
                 Multithreaded/Multiprocess 
               
               
                   
                 ACE_TCP 
                 Networking 
               
               
                   
                 MPB 
                 Multithreaded/Multiprocess 
               
               
                   
                 PB 
                 Multithreaded/Multiprocess 
               
               
                   
                 STREAMS 
                 Memory Intensive 
               
               
                   
                   
               
            
           
         
       
     
     Those skilled in the art will appreciate that a wide range of different unit test procedures are available, and that selection of a desired unit test procedure to test a particular service is within the skill of the person of ordinary skill in the art. The above unit test procedures are identified for illustrative purposes only. 
     Once the technician has selected a set of unit test procedures to test each of the target platforms, processing continues at  306 , where each of the selected unit test procedures are run on each of the target platforms. 
     Processing continues at  308  where the result data for each of the unit test procedures for each of the target platforms is captured and stored. For example, the unit test data may be stored in datastore  110  of  FIG. 1  so that it is readily accessible by matching platform  102  for analysis. In some embodiments, each test may be run multiple times (e.g., there may be multiple sub-tests within each unit test). For example, a unit test may be run with different parameters to identify different performance characteristics of the platform under test. As used herein, the results from each of these sub-tests is referred to as a separate “Y-value” in the unit test results. The test result data from each unit test is stored in a structured manner so that each Y-value result may be identified and compared by the matching platform. Again, in some embodiments, the data is stored in XML format. 
     Processing at  306  and  308  is repeated until all of the selected unit tests have been performed on each of the target platforms and all of the result data has been stored in a data store for analysis by the matching platform. 
     For example, continuing the illustrative example introduced above, processing at  306  and  308  includes performing the ACE_QBW, ACE_TCP, MPB, PB and STREAMS unit test procedures on each of the ABC and DEF platforms. Each of the unit tests are repeated for each Y-value, until a complete dataset representing the full test results for each platform is obtained. In the illustrative example, the unit test results are stored as XML files in an XQuery capable database. The XML may be stored or accessed using the following general format:
         &lt;benchmark test name&gt;|&lt;system configuration tested&gt;|&lt;y-value&gt;       

     As a result, at the completion of the processing at  306  and  308 , in the illustrative example, a data array or table is constructed which has data identifying each unit test for each tested platform, and at each Y-value (or sub test). Those skilled in the art will recognize that other data storage and formatting techniques may be used so long as the data for each test and each platform are readily accessible. 
     Processing continues at  310  where the matching platform (such as matching platform  102  of  FIG. 1 ) accesses the result data and analyzes the data to identify the platform that is the best fit or match for the software application. 
     Further details of the processing at  310  will now be described in conjunction with  FIG. 4 .  FIG. 4  depicts a process  400  for analyzing data to identify the most appropriate platform (or the “best fit”) for a particular software application. Process  400  may be performed by a matching platform such as platform  102  of  FIG. 1 . For example, process  400  may be initiated by a user interacting with a client device in communication with the matching platform. The matching platform may perform process  400  by executing computer program code configured to perform the process steps of process  400 . 
     As a specific illustrative example, the matching platform stores PHP code which, when executed, performs one or more of the steps of process  400 . Process  400  begins at  402  where the matching platform accesses the test result data, for each platform, each unit test, and each subtest. As discussed above, in some embodiments, the data is stored as an XML file in an XSD database. In such an embodiment, the processing at  402  includes retrieving the data by an XQuery. Processing at  402  may include creating an object having an array of the result data. For example, the array for the testing done in the illustrative example introduced above may include an array[0]-array[x] having array data including the unit test name (such as “ACE_QBW”), the system configuration tested (such as “ABC”), and a number of Y-values, representing the unit test results for each Y-value or subtest. 
     In some embodiments, processing continues at  404  where the object (including the XQuery results) is then re-ordered into a tree structure that associates the Y-values to the system configurations tested, grouped within the particular unit benchmark test. This allows the matching system to then numerically sort within the Y-value data to determine a score for each platform within each subtest. In some embodiments, the resulting score within each subtest will be in ascending order, while in other embodiments the score will be in descending order (depending upon the particular benchmark, and whether the benchmark results are to be ranked in ascending, i.e. throughput, or descending order, i.e. latency). For example, again continuing the illustrative example, the result of processing at  404  may be an array that ranks the ABC and DEF platforms by their performance in each subunit test within each unit test. Example result data is shown below in TABLE 1 to facilitate understanding of some embodiments: 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Unit Test: STREAMS 
               
            
           
           
               
               
               
               
            
               
                   
                 Y-Value 
                 Y-Value Result 
                 Platform 
               
               
                   
                   
               
               
                   
                 Y1 
                 1846.1567 
                 ABC 
               
               
                   
                   
                 2250.0021 
                 DEF 
               
               
                   
                 Y2 
                 1846.1567 
                 ABC 
               
               
                   
                   
                 2181.8187 
                 DEF 
               
               
                   
                 Y3 
                 2000.0037 
                 ABC 
               
               
                   
                   
                 2511.8187 
                 DEF 
               
               
                   
                 Y4 
                 2000.0037 
                 ABC 
               
               
                   
                   
                 2400.0061 
                 DEF 
               
               
                   
                   
               
            
           
         
       
     
     As shown, in the STREAMS unit tests, the DEF system performed better than the ABC system in the subunit tests labeled Y1-Y4 (as STREAMS is a throughput test, higher values are better). Those skilled in the art will appreciate that for some benchmark unit tests, a large number of Y-values or subtests may be performed. Processing pursuant to some embodiments involves sorting each of the Y-value results for each unit test so that a ranking may be determined by the matching platform. Similar sorts are performed for each of the unit tests so that a resulting data structure is created that has unit test data with all Y-values and Y-value results for each of the platforms sorted within each Y-value. 
     Processing continues at  406  where the matching platform operates on the data structures created at  404  to generate platform placement scores within each benchmark unit test. In some embodiments these scores are calculated by having the post-sorted array index values (created in  404 ) represent the finishing order of a system within a test. 
     For example, in the STREAMS array illustrated above, the ABC system had the top ranking Y-value results for subtests Y1-Y4, and would have an overall platform placement score of “4” (assuming, for simplicity, that there were no other Y-values). Similarly, the DEF system would have an overall platform placement score of “8” because it finished in second index position in each of the four unit subtests. In some embodiments, Y-values for an identical test between two or more platforms may be considered of equal performance if they do not differ by more than a pre-specified margin of error. This optional margin of error value can be specified by the user at a global level (wherein it would apply to all test comparisons), or on a per test basis. 
     Processing continues at  408  where, in some embodiments, the data structure from  406  is transformed into a results data structure representing the scores for each benchmark unit test, by platform. For example, the results data structure for the illustrative example may generally appear as shown below in TABLE 2: 
     
       
         
           
               
             
               
                 TABLE 2 
               
             
            
               
                   
               
               
                 “ABC Application” Results Data 
               
            
           
           
               
               
               
               
            
               
                   
                 Benchmark Unit 
                   
                   
               
               
                   
                 Test 
                 Platform 
                 Score 
               
               
                   
                   
               
            
           
           
               
               
               
               
            
               
                   
                 ACE QBW 
                 ABC 
                 50 
               
               
                   
                   
                 DEF 
                 40 
               
               
                   
                 ACE TCP 
                 ABC 
                 2 
               
               
                   
                   
                 DEF 
                 4 
               
               
                   
                 MPB 
                 ABC 
                 9 
               
               
                   
                   
                 DEF 
                 9 
               
               
                   
                 PB 
                 ABC 
                 6 
               
               
                   
                   
                 DEF 
                 6 
               
               
                   
                 STREAMS 
                 ABC 
                 4 
               
               
                   
                   
                 DEF 
                 8 
               
               
                   
                   
               
            
           
         
       
     
     The results data structure is generated, for example, by taking the post-sorted subunit test results generated in  406 , and creating a data structure containing all of the results. 
     Processing continues at  410  where the platform placement scores are normalized within each benchmark unit test. For example, this may be performed to assign a familiar (or human-readable) placement score, such as “1” for “first place”, “2” for “second place”, etc. A “tie” may be signified by two equal normalized results. These normalized scores may also be stored in a data structure for later analysis and viewing. An illustrative data structure is shown below in TABLE 3: 
     
       
         
           
               
             
               
                 TABLE 3 
               
             
            
               
                   
               
               
                 “ABC Application” Results Data 
               
            
           
           
               
               
               
               
            
               
                   
                 Benchmark Unit 
                   
                 Normalized 
               
               
                   
                 Test 
                 Platform 
                 Results 
               
               
                   
                   
               
               
                   
                 ACE_QBW 
                 ABC 
                 1 
               
               
                   
                   
                 DEF 
                 2 
               
               
                   
                 ACE_TCP 
                 ABC 
                 1 
               
               
                   
                   
                 DEF 
                 2 
               
               
                   
                 MPB 
                 ABC 
                 1 
               
               
                   
                   
                 DEF 
                 1 
               
               
                   
                 PB 
                 ABC 
                 1 
               
               
                   
                   
                 DEF 
                 1 
               
               
                   
                 STREAMS 
                 ABC 
                 1 
               
               
                   
                   
                 DEF 
                 2 
               
               
                   
                   
               
            
           
         
       
     
     Processing continues at  412  where the matching platform operates on the data generated above to arrive at a “best fit” or best match platform ranking. For example, the matching platform may multiply the value that represents the percentage of relevance (the weighting) of each service in the taxonomy, to the normalized placement scores (generated at  410 ). These may be stored in a further data structure. For example, continuing the illustrative example, at the beginning of the matching process the technician determined that the application profile of the ABC application and their relative weightings was as follows:
         Multithreaded/Multiprocess: 5%   Floating Point: 0%   Memory Intensive: 50%   Integer Intensive: 25%   Networking 20%       

     Since there were three Multithreaded/Multiprocess unit tests selected and performed on each of the platforms, the technician may choose to distribute the 5% weighting of the Multithreaded/Multiprocess service evenly among the three unit tests (although different distributions may be selected). As such, the weightings for each of the unit tests performed in the illustrative example may be:
         ACE_QBW: 1.6 6 %   MPB: 1.6 6 %   PB: 1.6 6 %   STREAMS: 50%   ACE_TCP: 25%       

     In this illustrative example, no Integer Intensive unit tests were performed, so the total weightings allocation for the unit tests does not total 100%. In some embodiments, processing at  412  includes applying the weighting allocations (shown above for the illustrative example) to the normalized rankings (shown in TABLE 3 for the illustrative example) generated at  410 . Pursuant to some embodiments, the resulting rankings may again be normalized to familiar placement rankings (e.g., with “1” for first place, etc.). In the illustrative example, the DEF system is the “best fit” platform for the ABC application, with an overall placement score of “74”. The ABC system is the second place platform, with an overall placement score of “143”. 
     Pursuant to some embodiments, different systems can be readily compared even a variety of different benchmark tests are performed which have different meanings (e.g., a value of a Floating Point test is expressed in MFlops, and a value of a Network test may be expressed in terms of throughput or latency). The result is a system that allows different computing platforms to be compared in a meaningful way to identify the platform (or platforms) that are best suited for use in conjunction with a particular software application. Further, pursuant to some embodiments, the comparison of the platforms can be performed using an automated, or partially automated, matching platform, allowing rapid and accurate comparisons. 
     The following illustrates various additional embodiments of the present invention. These do not constitute a definition of all possible embodiments, and those skilled in the art will understand that the present invention is applicable to many other embodiments. Further, although the following embodiments are briefly described for clarity, those skilled in the art will understand how to make any changes, if necessary, to the above-described apparatus and methods to accommodate these and other embodiments and applications. 
     Pursuant to some embodiments, a matching platform (such as the platform  102  of  FIG. 1 ), may automatically analyze a particular software application to generate accurate canonical form weightings for the application. In some embodiments, the canonical form weightings may be generated manually by a technician operating or interacting with the matching platform. In some embodiments, a matching platform (such as the platform  102 ) is configured to monitor (or receive monitoring data) a variety of applications in their production environments during their normal operation to continually receive and update application resource usage data. In some embodiments, this data may be used to reallocate computing resources, make new platform purchase or allocation decisions, monitor service level agreements, monitor budget and capacity, or the like. 
     For example, referring to  FIG. 5 , a system  500  is shown which receives application resource usage data from applications in a production environment. As shown, matching platform  502  is in communication with an application  506  deployed in a production environment. While only a single production application is shown, a number of applications may be monitored. Application resource usage data is captured by a data collection tool  508  which monitors selected application resource usage data of the application and transmits the data back to platform matching system  502 . Matching platform  502  may use this data to make updated “best fit” recommendations (e.g., by matching the updated application resource usage data to a set of available platforms  504 ). In some embodiments, matching platform  502  may also use the updated application resource data to monitor service level agreement commitments, changes in application resource usage, and other performance data. The data can be used to reallocate resources, deploy new applications and platforms, or the like. 
     Users and administrators may interact with matching platform  102  of  FIG. 1  via one or more client device(s)  106  using a graphical user interface such as the graphical user interface  600  of  FIG. 6 . Pursuant to some embodiments, users may interact with matching platform  102  by pointing a Web browser associated with a client device  106  to an Internet address (such as a secure site) associated with the matching platform  102 . While a wide variety of user interface designs may be used, one suitable design is depicted in  FIG. 6 . As shown, a user screen is presented which includes a top portion in which a user can select an application for analysis. When an application is selected, details of the application resource usage profile associated with that application are shown. A user may also be provided with sufficient permissions to edit the profile of a selected application. 
     A second portion of the illustrative user interface includes a region for selecting one or more target platforms to analyze the suitability of a match between the selected application and the selected target platforms. Details of each of the selected target platforms may be shown, and a user with sufficient permissions may be able to upload an XML file (or other suitable file format) having details of a new or updated target platform. 
     A third portion of the illustrative user interface includes a region for viewing data showing how well each of the selected target platforms fit the selected application. A number of options may be selected or deselected by a user to view various charts or descriptions relating to the match of the selected platforms to the application. In this way, a user operating a client device may efficiently and easily interact with matching platform  102  to view, analyze, update and edit information associated with an application and one or more platforms. The result is an ability to easily select the platform or platforms that are best suited to a given application. 
     The present invention has been described in terms of several embodiments solely for the purpose of illustration. Persons skilled in the art will recognize from this description that the invention is not limited to the embodiments described, but may be practiced with modifications and alterations limited only by the spirit and scope of the appended claims. For example, while some embodiments have been described in which an application&#39;s resource utilization profile is created by a technician who has in-depth knowledge of an application&#39;s behavior, other embodiments create a resource utilization profile using a model derived from measurements obtained via performance analysis tools. Further, while an application&#39;s resource utilization profile has been described in terms of technical characteristics (such as whether/how much an application is disk intensive, memory intensive, network intensive, etc.), those skilled in the art will now appreciate that an application&#39;s profile may be described in terms of other characteristics such as system redundancy features, operational costs, etc. 
     Further, while the use of XML schemas to represent both an application&#39;s resource utilization profile and a platform&#39;s description and test results, those skilled in the art will recognize that other schemas may be used.