Patent Publication Number: US-11645583-B2

Title: Automatic rule learning in shared resource solution design

Description:
BACKGROUND 
     Clients in shared resource environments, such as cloud computing environments, often have requirements of hundreds of virtual machines with complicated configurations. Sales of cloud offering relies on experienced cloud architects to manually design solutions according to customer&#39;s requirements, carefully tailoring the scheme, and adjusting the configurations to guarantee i) customer&#39;s requirements are satisfied and ii) desired properties, such as price, are kept within a target range. Usually one single round of design may take two-four weeks. Due to the case-by-case solution design, when new customers are added, an architect has to create the client&#39;s solution from scratch-up. 
     SUMMARY 
     Embodiments relate to automatically learning shared resource environment solution design rules from a collection of requirement-solution pairs. One embodiment provides automatically learning shared resource environment solution design rules from a collection of requirement-solution pairs including obtaining requirement-solution pairs for a shared resource environment from a data store. A processor iteratively generates a candidate design rule set from each requirement-solution pair. Each generating iteration uses an input including the candidate design rule set output from a previous generating iteration. Evidence scores of each candidate design rule are calculated and candidate design rules having higher evidence score than an evidence score threshold are retained to obtain a learned design rule set. Candidate rules of a next iteration are constructed based on an addition of new attributes to rules of the learned design rule set. 
     These and other features, aspects and advantages of the present invention will become understood with reference to the following description, appended claims and accompanying figures. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    depicts a cloud computing environment, according to an embodiment; 
         FIG.  2    depicts a set of abstraction model layers, according to an embodiment; 
         FIG.  3    is a network architecture for efficient representation, access and modification of variable length data objects, according to an embodiment; 
         FIG.  4    shows a representative hardware environment that may be associated with the servers and/or clients of  FIG.  1   , according to an embodiment; 
         FIG.  5    is a block diagram illustrating system for automatically learning shared resource environment solution design rules from a collection of requirement-solution pairs, according to one embodiment; 
         FIG.  6    illustrates block diagram for a system flow for automatically learning shared resource environment solution design rules from a collection of requirement-solution pairs, according to one embodiment; 
         FIG.  7    illustrates an example diagram for requirement and solution objects being both abstract to a series of attribute-value pairs, according to one embodiment; 
         FIG.  8    illustrates an example table of requirements and solutions, according to one embodiment; 
         FIG.  9    illustrates an example table of evidence and confidence for rules, according to one embodiment; and 
         FIG.  10    illustrates a block diagram for a process for automatically learning shared resource environment solution design rules from a collection of requirement-solution pairs, according to one embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     The descriptions of the various embodiments have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. 
     It is understood in advance that although this disclosure includes a detailed description of cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     One or more embodiments provide for automatically learning shared resource environment (e.g., a cloud computing environment) solution design rules from a collection of requirement-solution pairs. In one embodiment, a method is provided for automatically learning shared resource environment solution design rules from a collection of requirement-solution pairs including obtaining requirement-solution pairs. A processor iteratively generates a candidate design rule set from each requirement-solution pair. Candidate design rules from the candidate rule set are filtered to obtain a learned design rule set. The learned design rule set is optimized based on merging design rules. 
     One or more embodiments provide for reusing existing solutions for new requirements, sharing of design expertise among architects and transferring of knowledge from experienced architects to less experienced architects. One or more embodiments improve the efficiency of a cloud solution design by providing automatic or semi-automatic solution generation via learned solution design rules, helps summarize the solution design rules from experienced architects, and helps train new architects in designing cloud solutions. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines (VMs), and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as Follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed and automatically, without requiring human interaction with the service&#39;s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous, thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
     Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or data center). 
     Rapid elasticity: capabilities can be rapidly and elastically provisioned and, in some cases, automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
     Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active consumer accounts). Resource usage can be monitored, controlled, and reported, thereby providing transparency for both the provider and consumer of the utilized service. 
     Service Models are as Follows: 
     Software as a Service (SaaS): the capability provided to the consumer is the ability to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface, such as a web browser (e.g., web-based email). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited consumer-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is the ability to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application-hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is the ability to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as Follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load balancing between clouds). 
     A cloud computing environment is a service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure comprising a network of interconnected nodes. 
     Referring now to  FIG.  1   , an illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  comprises one or more cloud computing nodes  10  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Nodes  10  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as private, community, public, or hybrid clouds as described hereinabove, or a combination thereof. This allows the cloud computing environment  50  to offer infrastructure, platforms, and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  54 A-N shown in  FIG.  2    are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
     Referring now to  FIG.  2   , a set of functional abstraction layers provided by the cloud computing environment  50  ( FIG.  1   ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG.  2    are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
     Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
     In one example, a management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and pricing  82  provide cost tracking as resources are utilized within the cloud computing environment and billing or invoicing for consumption of these resources. In one example, these resources may comprise application software licenses. Security provides identity verification for cloud consumers and tasks as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  90  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and automatically learning shared resource environment solution design rules from a collection of requirement-solution pairs processing  96 . As mentioned above, all of the foregoing examples described with respect to  FIG.  2    are illustrative only, and the invention is not limited to these examples. 
     It is understood all functions of one or more embodiments as described herein may be typically performed by the processing system  300  ( FIG.  3   ) or the autonomous cloud environment  410  ( FIG.  4   ), which can be tangibly embodied as hardware processors and with modules of program code. However, this need not be the case for non-real-time processing. Rather, for non-real-time processing the functionality recited herein could be carried out/implemented and/or enabled by any of the layers  60 ,  70 ,  80  and  90  shown in  FIG.  2   . 
     It is reiterated that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, the embodiments of the present invention may be implemented with any type of clustered computing environment now known or later developed. 
       FIG.  3    illustrates a network architecture  300 , in accordance with one embodiment. As shown in  FIG.  3   , a plurality of remote networks  302  are provided, including a first remote network  304  and a second remote network  306 . A gateway  301  may be coupled between the remote networks  302  and a proximate network  308 . In the context of the present network architecture  300 , the networks  304 ,  306  may each take any form including, but not limited to, a LAN, a WAN, such as the Internet, public switched telephone network (PSTN), internal telephone network, etc. 
     In use, the gateway  301  serves as an entrance point from the remote networks  302  to the proximate network  308 . As such, the gateway  301  may function as a router, which is capable of directing a given packet of data that arrives at the gateway  301 , and a switch, which furnishes the actual path in and out of the gateway  301  for a given packet. 
     Further included is at least one data server  314  coupled to the proximate network  308 , which is accessible from the remote networks  302  via the gateway  301 . It should be noted that the data server(s)  314  may include any type of computing device/groupware. Coupled to each data server  314  is a plurality of user devices  316 . Such user devices  316  may include a desktop computer, laptop computer, handheld computer, printer, and/or any other type of logic-containing device. It should be noted that a user device  311  may also be directly coupled to any of the networks in some embodiments. 
     A peripheral  320  or series of peripherals  320 , e.g., facsimile machines, printers, scanners, hard disk drives, networked and/or local storage units or systems, etc., may be coupled to one or more of the networks  304 ,  306 ,  308 . It should be noted that databases and/or additional components may be utilized with, or integrated into, any type of network element coupled to the networks  304 ,  306 ,  308 . In the context of the present description, a network element may refer to any component of a network. 
     According to some approaches, methods and systems described herein may be implemented with and/or on virtual systems and/or systems, which emulate one or more other systems, such as a UNIX system that emulates an IBM z/OS environment, a UNIX system that virtually hosts a MICROSOFT WINDOWS environment, a MICROSOFT WINDOWS system that emulates an IBM z/OS environment, etc. This virtualization and/or emulation may be implemented through the use of VMWARE software in some embodiments. 
       FIG.  4    shows a representative hardware system  400  environment associated with a user device  316  and/or server  314  of  FIG.  3   , in accordance with one embodiment. In one example, a hardware configuration includes a workstation having a central processing unit  410 , such as a microprocessor, and a number of other units interconnected via a system bus  412 . The workstation shown in  FIG.  4    may include a Random Access Memory (RAM)  414 , Read Only Memory (ROM)  416 , an I/O adapter  418  for connecting peripheral devices, such as disk storage units  420  to the bus  412 , a user interface adapter  422  for connecting a keyboard  424 , a mouse  426 , a speaker  428 , a microphone  432 , and/or other user interface devices, such as a touch screen, a digital camera (not shown), etc., to the bus  412 , communication adapter  434  for connecting the workstation to a communication network  435  (e.g., a data processing network) and a display adapter  436  for connecting the bus  412  to a display device  438 . 
     In one example, the workstation may have resident thereon an operating system, such as the MICROSOFT WINDOWS Operating System (OS), a MAC OS, a UNIX OS, etc. In one embodiment, the system  400  employs a POSIX® based file system. It will be appreciated that other examples may also be implemented on platforms and operating systems other than those mentioned. Such other examples may include operating systems written using JAVA, XML, C, and/or C++ language, or other programming languages, along with an object oriented programming methodology. Object oriented programming (OOP), which has become increasingly used to develop complex applications, may also be used. 
       FIG.  5    is a block diagram illustrating a system  500  for automatically learning shared resource environment solution design rules from a collection of requirement-solution pairs, according to one embodiment. In one embodiment, the system  500  includes client devices  510  (e.g., mobile devices, smart devices, computing systems, etc.), a cloud or resource sharing environment  520 , and servers  530 . In one embodiment, the client devices are provided with cloud services from the servers  530  through the cloud or resource sharing environment  520 . 
       FIG.  6    illustrates block diagram for a system flow  600  for automatically learning shared resource environment solution design rules from a collection of requirement-solution pairs, according to one embodiment. In one embodiment, the system flow  600  includes obtaining from memory (e.g., a data store, database, etc.) requirement-solution pairs  620  for resource sharing environments (e.g., a cloud computing environment, etc.). The requirement and solution objects are both abstract  630  to a series of attribute-value pairs. Rule learning  640  includes candidate (design) rule generation  650  and candidate (design) rule filtering  660 . Rule optimization  670  optimizes the design rule set based on rule merging as described below. The result (solution) design rules  680  are then provided to a client device for user review  690 . If the solution  616  is acceptable, the design rule  610  (including the requirement  615  and solution  616 ) is stored as a requirement-solution pair  620  for future use.  FIG.  7    illustrates an example diagram  700  for design rule  610  including a requirement  615  and solution  616  objects being both abstract to a series of attribute-value pairs, according to one embodiment. In the example diagram  700 , the requirement  710  includes 1000 system, applications and products (SAPs) and 99.7% availability. The solution  720  includes 2 central processing units (CPUs), 8 gigabytes (GBs) of random-access memory (RAM), and an enhanced service level. 
     Returning back to  FIG.  6   , the process for learning design rules in block  640  is now described. In one embodiment, the inputs are requirement and solution pairs  620 . The output is a learned design rule(s){r j1  . . . r jm →s}, where r represents design rules, s represents a solution, and j and m are positive integers. In one embodiment, learning design rules includes: 
     1. finding/obtaining direct copy-n-paste rule(s), i.e., r 1 →s &amp; r i =s. 
     2. For remaining candidate design rules, set k=1. 
     3. Given k, block  640  generates candidate rules from the last iteration. 
     4. For each candidate rule (r i1  . . . r in →s), where |r i |=k, block  640  calculates the evidence. 
     5. All design rules whose evidence is above an evidence threshold are retained; if there is no design rule whose evidence is above the threshold, then proceed to 7. 
     6. Set k=k+1, construct candidate rules of the next iteration by adding new attributes to rules at iteration k; and proceed to 3. 
     7. For all remaining candidate design rules in iteration k−1, calculate the confidence score and retain those with highest confidence. 
       FIG.  8    illustrates an example table  800  of requirements and solutions, according to one embodiment. Requirement-Solution pairs (R,S) include:
         Requirement attribute-value pair: r 1 , r 2 , . . . , r m      Solution attribute-value pair: s 1 , s 2 , . . . s n .
 
A rule is defined as the mapping from requirement to a solution
 
In example table  800 , the first three of the following requirements and solutions are shown for the following design rules:
       

     Rule 1: SAPs=1000 &amp; Availability=99.7%→Service Level=Enhanced 
     Rule 2: Availability=99.7%→Service Level=Enhanced 
     Rule 3: SAPs=500 &amp; Availability=99.7%→Service Level=Enhanced 
     Rule 4: SAPs=1000→Service Level=Enhanced 
     Rule 5: SAPs=500→Service Level=Enhanced. 
       FIG.  9    illustrates an example table  900  of evidence and confidence for rules, according to one embodiment. In one embodiment, for learning the design rules, two factors need to be determined, evidence and confidence. Evidence measures how accurate the rule is P(s i1 , . . . , s in |r j1 , . . . , r jm ). Confidence measures how common the requirements are observed in existing data P(r j1 , . . . , r jm ). The evidence score of a design rule includes a number of combined requirement and solution attribute-value pairs divided by requirement attribute-value pairs. The confidence score of a design rule comprises a number of requirement attribute-value pairs divided by a total number of requirement-solution pairs. For the above five (rules 1-5) design rules, table  900  shows evidence and confidence. 
     Returning to  FIG.  6   , in one embodiment, in block  660  candidate design rule filtering includes calculating evidence scores of each candidate design rule and retaining candidate design rules having higher evidence score than an evidence score threshold, and for remaining candidate design rules with a same solution attribute, calculating a confidence score and retaining a candidate design rule with a highest confidence score. 
     In one embodiment, in block  650  candidate design rules are generated as follows. In one embodiment, the input includes a learned design rule set of the last iteration. The output includes the candidate design rule set for the next iteration.
         1. If the learned rule set of the last iteration is empty (i.e., k=1), proceed to 2; otherwise proceed to 3.   2. For each requirement and solution pair, generate a candidate design rule as r i →s j  where r i  is a single attribute-value pair in requirement and s j  is a solution.   3. For each learned design rule (r i1 , . . . , r in →s j ) and each r k  that is currently not included, generate a new candidate rule (r i1 , . . . , r in , r k →s j ).       

     In one embodiment, block  670  includes optimizing the learned design rule set output from block  640 . The input includes the learned design rules from block  640 . The output includes an optimized design rule set.
         1. For each design rule in the set, system flow  600  performs the following:   2. Find other design rules that share the same solution attributes and merge the two requirement attributes, denoted as A and B
           1. If A∩B=ϕ, merge as “A or B”   2. Otherwise, C=A∩B, merge as “C and [(A−C) or (B−C)]”   
           3. Find other design rules that share the same requirement attributes, then merge the solution attributes.   4. Repeat 2-3 until there is no change to the design rule set.       

       FIG.  10    illustrates a block diagram for a process  1000  for automatically learning shared resource environment solution design rules from a collection of requirement-solution pairs, according to one embodiment. In block  1010 , process  1000  obtains requirement-solution pairs. In block  1020 , process  1000  iteratively generates (e.g., by a hardware processor) a candidate design rule set from each requirement-solution pair. In block  1030 , process  1000  filters candidate design rules from the candidate rule set to obtain a learned design rule set. In block  1040 , process  1000  optimizes the learned design rule set based on merging design rules. 
     In one embodiment, in process  1000  a requirement is abstracted as a series of attribute-value pairs. A solution is abstracted as a series of attribute-value pairs. In process  1000 , a design rule includes at least one requirement attribute-value pair and at least one solution attribute-value pair. In one embodiment, process  1000  may include generating the candidate design rule set by: for a first iteration, generating candidate design rules with a single requirement attribute and a single solution attribute, and for other iterations, generating candidate design rules from a previous candidate rule by adding one uncovered requirement attribute. 
     In one embodiment, in process  1000  filtering candidate design rules includes: calculating evidence scores of each candidate design rule and retain candidate design rules having higher evidence score than an evidence score threshold, and for remaining candidate design rules with a same solution attribute, calculating a confidence score and retaining a candidate design rule with a highest confidence score. In one embodiment, the evidence score of a design rule includes a number of combined requirement and solution attribute-value pairs divided by requirement attribute-value pairs. The confidence score of a design rule includes a number of requirement attribute-value pairs divided by a total number of requirement-solution pairs. 
     In one embodiment, optimizing learned design rule includes merging design rules with same solution attributes with operators (e.g., logical operators) and/or functions, etc., and merging design rules with same requirement attributes with operators and/or functions. 
     As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon. 
     Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. 
     A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. 
     Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. 
     Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). 
     Aspects of the present invention are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     References in the claims to an element in the singular is not intended to mean “one and only” unless explicitly so stated, but rather “one or more.” All structural and functional equivalents to the elements of the above-described exemplary embodiment that are currently known or later come to be known to those of ordinary skill in the art are intended to be encompassed by the present claims. No claim element herein is to be construed under the provisions of 35 U.S.C. section 112, sixth paragraph, unless the element is expressly recited using the phrase “means for” or “step for.” 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present invention has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.