Patent Publication Number: US-2021182104-A1

Title: Executing computing modules using multi-coring

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a Continuation-in-Part of U.S. application Ser. No. 16/519,190, filed on Jul. 23, 2019; and this application is also a Continuation-in-Part of U.S. application Ser. No. 17/035,031, filed on Sep. 28, 2020, which is a Continuation of U.S. application Ser. No. 16/563,240, filed on Sep. 6, 2019 (now U.S. Pat. No. 10,789,103, and issued on Sep. 29, 2020). The contents of each of these applications are incorporated herein by reference in their entireties. 
    
    
     BACKGROUND 
     Large entities such as financial institutions, retail stores, educational institutions, government agencies, and/or the like may electronically process large amounts of data and execute large amounts of calculations on a daily basis. Events such as natural disasters, updated regulations, power outages, and/or the like can cause a sudden influx in the data (e.g., customer complaints, questions, usage of a mobile application, and/or the like) that needs to be processed and the calculations needed to be executed. These entities may implement computing modules including multiple functions to process the large amounts of data and execute the large amounts of calculations. The functions include code or a set of instructions written in a programming language. The functions may execute a specified set of tasks. Each function may process data, execute calculations, and make function calls. Heavy computations that are not serializable and take large amounts of time can use large amounts of computational resources, and cause bottlenecks and network latency. Certain functions may take hours or days to complete due to millions of records and large amounts of calculations to be executed. Conventionally, entities would have to wait to execute computationally expensive functions when the usage of computer resources and the network is at a minimum. This can be inefficient as functions may need to be executed at any time of the day. 
     As an example, certain compliance applications implemented by large entities may electronically process large amounts of data and execute large amounts of calculations on a daily basis. Additionally, the compliance applications may include functions configured to perform a variety of tasks. 
     The large entities described above are required to comply with regulations, laws, and/or statutes implemented and enforced by government institutions. To ensure that these large entities comply with the regulations, laws, and/or statutes large entities have developed applications including executable code for verifying the large entities are complying with the regulations, laws, and/or statutes. Compliance application may verify an entity&#39;s compliance with financial regulations, cybersecurity laws, privacy laws, and/or the like. Compliance applications may verify an entity&#39;s compliance with compliance data such as with laws, regulations, and/or statutes of various regulatory agencies. The regulatory agencies may update or create new regulations at a rapid pace. Conventionally, users may have to manually browse external data sources to identify updated compliance data and then manually determine which controls of the compliance applications are affected by the updated compliance data. This can be a long and error-prone process, which can use large amounts of computational resources for long periods of time. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       The accompanying drawings, which are incorporated herein and form part of the specification, illustrate the present disclosure and, together with the description, further serve to explain the principles of the disclosure and enable a person skilled in the relevant art to make and use the disclosure. 
         FIG. 1  is a block diagram of an example environment in which systems and/or methods for executing a computing module may be implemented according to an example embodiment. 
         FIG. 2  illustrates example computing cores according to an embodiment. 
         FIG. 3  illustrates example flow of compliance data according to an embodiment. 
         FIGS. 4A-4B  illustrate example data structures according to an embodiment. 
         FIG. 5  is a flowchart illustrating a process for executing a computing module using multi-coring according to an embodiment. 
         FIG. 6  is a flowchart illustrating a process for verifying a computing module is suitable for multi-coring according to an embodiment. 
         FIG. 7  is a flowchart illustrating a process for verifying a computing module is suitable for multi-coring according to an embodiment. 
         FIG. 8  is a flowchart illustrating a process for identifying controls which do not align with updated compliance data according to an embodiment. 
         FIG. 9  is a block diagram of example components of device according to an embodiment. 
     
    
    
     The drawing in which an element first appears is typically indicated by the leftmost digit or digits in the corresponding reference number. In the drawings, like reference numbers may indicate identical or functionally similar elements. 
     DETAILED DESCRIPTION 
     Described herein is a system for executing a computing module. The system may determine whether a function of a computing module is suitable to be executed using multi-coring. That is the system determines whether a function is suitable to be executed by one or more computing cores in a dedicated fashion. The system identifies one or more available computing cores and executes the function on the one or more available computing cores. The one or more available computing cores can be dedicated to execute the function until the execution of the function is complete. For purposes of saving time and efficiency, the one or more available computing cores executes the tasks of the function asynchronously. The system receives output data from the function asynchronously in a list data structure. It can be appreciated that the output data may also be received as an array, stack, queue, and/or the like, but the output data will be discussed as a list throughout for the purposes of example, and not limitation. The system can maintain a desired order of the output data in the list data structure. Once the function has executed, the system converts the list data structure into a data frame data structure by transposing the data from the list data structure into the data frame data structure in the desired order. 
     The system solves the technical problem of network bottlenecks and network latency by dedicating computing cores to execute specific functions. In this configuration, other computing cores are available to execute other functions. Additionally, the system can quickly execute the functions by asynchronously executing the tasks of the function while maintaining the desired order of the output of the function. 
     Furthermore, described herein is a system for identifying controls not aligned with updated compliance data. The system may scrub external data sources for updated compliance data. The system may detect and extract the updated compliance data from the external data sources. The system may identify and correlate controls of compliance applications currently using compliance data which has now been updated. The system determines whether a control exists to cover the updated compliance data. In the event a control does not exist for the updated compliance data, a requirement may be generated for generating a new control for the updated compliance data. In the event a control for the updated compliance data exists, the system may determine whether the control covers the updated compliance data. In the event the control data does not cover the updated compliance data, the system may generate a requirement for modifying the existing control to cover the updated compliance data. The requirements may be output into a database. 
     The system solves a technical problem of manually having to search external data sources one by one, extract compliance data from the external data sources and correlating the controls of the compliance application with the extracted compliance data, which can be time-consuming and error-prone. Conventionally, this would require numerous queries and computational resources utilized over a long time period. The system described herein solves these problems by automatically extracting updated compliance data in a single execution of a scraping application, and correlating the compliance application with the extracted compliance data. 
       FIG. 1  is a block diagram of an example environment  100  in which systems and/or methods described herein may be implemented. The environment  100  may include a deployment system  100 . The deployment system  100  may include a first computing module  102  and a second computing module  108 . The first computing module  102  may include function  1   104  and function  2   106 . The second computing module  108  may include function  1   110  and function  2   112 . Environment  100  may further include computing cores  114 . Computing cores  114  may be a pool of computing cores which includes several individual computing cores such as computing core  116 , computing core  118 , computing core  120 , computing core  122 , and computing core  124 . The devices of the environment  300  may be connected through wired connections, wireless connections, or a combination of wired and wireless connections. 
     In an example embodiment, one or more portions of the network  130  may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless wide area network (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a cellular telephone network, a wireless network, a WiFi network, a WiMax network, any other type of network, or a combination of two or more such networks. 
     The backend platform  125  may include a server or a group of servers. In an embodiment, the backend platform  125  may be hosted in a cloud computing environment  140 . It may be appreciated that the backend platform  125  may not be cloud-based, or may be partially cloud-based. 
     The cloud computing environment  140  includes an environment that delivers computing as a service, whereby shared resources, services, etc.. The cloud computing environment  140  may provide computation, software, data access, storage, and/or other services that do not require end-user knowledge of a physical location and configuration of a system and/or a device that delivers the services. The cloud computing system  140  may include computer resources  126   a - d.    
     Each computing resource  126   a - d  includes one or more personal computers, workstations, computers, server devices, or other types of computation and/or communication devices. The computing resource(s)  126   a - d  may host the backend platform  125 . The cloud resources may include compute instances executing in the cloud computing resources  126   a - d . The cloud computing resources  126   a - d  may communicate with other cloud computing resources  126   a - d  via wired connections, wireless connections, or a combination of wired or wireless connections. 
     Computing resources  126   a - d  may include a group of cloud resources, such as one or more applications (“APPs”)  126 - 1 , one or more virtual machines (“VMs”)  126 - 2 , virtualized storage (“VS”)  126 - 3 , and one or more hypervisors (“HYPs”)  126 - 4 . 
     Application  125 - 1  may include one or more software applications that may be provided to or accessed by the user device  140 . In an embodiment, the application  204  may execute locally on the user device  140 . Alternatively, the application  126 - 1  may eliminate a need to install and execute software applications on the user device  140 . The application  126 - 1  may include software associated with backend platform  125  and/or any other software configured to be provided across the cloud computing environment  140 . The application  126 - 1  may send/receive information from one or more other applications  126 - 1 , via the virtual machine  126 - 2 . 
     Virtual machine  126 - 2  may include a software implementation of a machine (e.g., a computer) that executes programs like a physical machine. Virtual machine  126 - 2  may be either a system virtual machine or a process virtual machine, depending upon the use and degree of correspondence to any real machine by virtual machine  126 - 2 . A system virtual machine may provide a complete system platform that supports execution of a complete operating system (OS). A process virtual machine may execute a single program and may support a single process. The virtual machine  126 - 2  may execute on behalf of a user (e.g., user device  140 ) and/or on behalf of one or more other backend platforms  125 , and may manage infrastructure of cloud computing environment  140 , such as data management, synchronization, or long duration data transfers. 
     Virtualized storage  126 - 3  may include one or more storage systems and/or one or more devices that use virtualization techniques within the storage systems or devices of computing resources  126   a - d . With respect to a storage system, types of virtualizations may include block virtualization and file virtualization. Block virtualization may refer to abstraction (or separation) of logical storage from physical storage so that the storage system may be accessed without regard to physical storage or heterogeneous structure. The separation may permit administrators of the storage system flexibility in how administrators manage storage for end users. File virtualization may eliminate dependencies between data accessed at a file level and location where files are physically store. This may enable optimization of storage use, server consolidation, and/or performance of non-disruptive file migrations. 
     Hypervisor  126 - 4  may provide hardware virtualization techniques that allow multiple operations systems (e.g., “guest operating systems”) to execute concurrently on a host computer, such as computing resources  126   a - d . Hypervisor  126 - 4  may present a virtual operating platform to the guest operating systems, and may manage the execution of the guest operating systems multiple instances of a variety of operating systems and may share virtualized hardware resource. 
     In an embodiment, first computing module  102  includes function  1   104  and function  2   106 . Second computing module  108  includes function  1   110  and function  2   112 . Function  1104 , function  2   106 , function  1   110 , and function  2   112 , may be code programmed in a programming language such as Python, Java, C++, C, C #, and/or the like. The code may be instructions to complete a set of tasks. Function  1104 , function  2   106 , function  1   110 , and function  2   112  may process data, execute calculations and return data when executed. Each of the first and second computing module  102 ,  108  may be programmed to execute various tasks. Function  1   104  and function  2   106  may execute the tasks to be completed by first computing module  102 . Function  1   110 , and function  2   112  may execute the various tasks to be executed by second computing module  108 . 
     As a non-limiting example, first computing module  102  may be programmed to detect words or phrases from audio, video, and/or text files. Second computing module  108  may be programmed to generate reports based on the detected words or phrases from the audio, video, and/or text files. Accordingly, function  1   104  and function  2   106  of first computing system  102  may individually or together execute the tasks necessary for detecting words or phrases from audio, video, and/or text files. Function  1   110  and function  2   112  of the second computing module  108  may individually or together execute the tasks necessary to generate a report based on the detected words or phrases from the audio, video, and/or text files. Each function may require different arguments. 
     Continuing with the earlier example, function  2   106  may be responsible for generating or retrieving the audio, video, and/or text files. Function  2   106  may call function  1   104  and provide the audio, video, and/or text files as arguments. Function  1   104  may be responsible for detecting the words or phrases from the audio, video, and/or text files received as arguments from function  2   106 . 
     Computing cores  114  may be a pool of computing cores  116 - 124 . Computing cores  116 - 124  may be separate processing units configured to execute functions of the first and second computing modules  102 ,  108 . Computing cores  116 - 124  may execute on one or more processors. Computing cores  116 - 124  may execute independently or in combination with one another. Computing cores  114  may be part of the cloud computing system  140 . Alternatively, computing cores  114  may be separate from the cloud computing system  140 . 
     Deployment system  100  may be configured to determine whether a function of the first and second computing modules  102 ,  108  is suitable executing using multi-coring or multiprocessing. Multi-coring is the concept using dedicated cores to execute a single function. For the purposes of speed and efficiency, multi-coring may be executed asynchronously. In this regard, using multi-coring, the tasks of a function may be executed in an asynchronous order. Multiprocessing includes the running of two or more programs or sequences of instructions simultaneously by a computer with more than one central processor. Using multiprocessing, deployment system  100  may execute the functions of the first and second computing modules  102 ,  108  using any one of the computing cores  116 - 124 . In multi-coring one or more cores may be dedicated to only execute a single function. In multiprocessing any one of the computing cores may execute multiple functions in parallel or serially. 
     Deployment system  100  may determine whether a function is suitable for multi-coring or multiprocessing based on a series of steps. Initially, deployment system  100  may determine whether the code included in the function to be executed is computationally expensive. Deployment system  100  may determine the code is computationally expensive to execute based on an expected amount data to be processed by the code multiplied by an expected amount of calculations to be executed by the code. In response to determining the expected amount data to be processed by the code multiplied by the expected amount of calculations to be executed by the code is more than a threshold amount, the deployment system  100  may determine the code is computationally expensive. In response to determining code is not computationally expensive, deployment system may determine if the function is not suitable for multi-coring, as it may not be desirable to dedicate a set of resources to a function that is not computationally expensive to execute. 
     Next, deployment system  100  may determine whether the code of the function includes calculations that are interdependent of each other. As described above, using multi-coring, the tasks of a function may be executed asynchronously. Accordingly, in the event a function includes calculations which are dependent on other calculations, multi-coring may not be suitable for this function as the calculations may be executed out of the desired order. Likewise, deployment system  100  also determines whether the function has interdependences with other functions within the computing module. Multi-coring may not be suitable for a function in situations where the function is relying on other function calls, as the tasks of the function are executed asynchronously. 
     Next, deployment system  100  determines whether a computing module (i.e., first and second computing module  102 ,  108 ) has more than one function which is computationally expensive. Multi-coring may not be suitable for computing modules in which more than one function is computationally expensive as it may not be desirable to dedicate a large amount of computing cores to execute each computationally expensive function. 
     In the event deployment system  100  determines the code of the function is computationally expensive, does not include interdependent calculations, does not have interdependencies with other functions, and the computing module does not include more than one computationally expensive function, the deployment system  100  may determine the function may be suitable for multi-coring. Otherwise the deployment system  100  may determine the function is not suitable for multi-coring but rather is suitable for multiprocessing. 
     In the event a function is suitable for multi-coring, execution engine  150  may determine an amount of available computing cores. Execution engine  150  may determine the amount of computing cores necessary to execute the function. Execution engine  150  may assign the amount of computing cores from the available computing cores to execute the function. The assigned computing cores may execute the function and may not execute any other function until the function has completely executed. Execution engine  150  may execute the function on the assigned computing cores. In a non-limiting example, multi-coring may be implemented using Python which may include a global interpreter lock. The global interpreter lock is a mutex or a lock that allows only one computing core to be dedicated to execute the function. 
     As the assigned computing cores execute the function asynchronously, the function may return data asynchronously. Execution engine  150  may receive the data from the function and store the data in a list data structure rather than a data frame data structure. In this regard, execution engine  150  can ensure a desired order of the data is maintained even though the data may be received out of order. As an example, in the event a function is configured to execute task  1 , task  2 , and task  3 . The assigned computing cores may execute the tasks in the following order: task  2 , task  3 , and task  1 , leading to return data from each of these tasks out of order. It may be desirable to maintain the order of returned data from task  1 , task  2 , and task  3 . Accordingly, execution engine  150  may maintain the order of the returned data in the list data structure as follows: [returned value from task  1 , returned value from task  2 , and returned value from task  3 ]. Execution engine  150  may transpose the list data structure into a data frame data structure, once the function has completely executed. 
     Once the assigned computing cores have completed the execution of the function using multi-coring, the assigned computing cores may be deemed available for selection again. 
     In the event deployment system  100  determines a function is suitable for multiprocessing, the execution engine may assign the function to a process and execute the process. The process may be executed by any one of the available computing cores. 
     In some embodiments, the deployment system  100  may include a scraping engine  152  and an analyzing engine  104 . The example environment may further include external data sources  111 , a compliance application  142 , a database  144 , and a user device  146 . Compliance application  142  may be an executable application which verifies an entity&#39;s compliance with specified laws, regulations, and/or statutes. Different compliance applications  142  may verify entity&#39;s compliance with different types of laws, regulations, and/or statutes. For example, one compliance application  142  may verify an entity&#39;s compliance with financial laws, regulations, and/or statutes of a geographic region, while another compliance application  142  may verify an entity&#39;s compliance a cybersecurity laws, regulations, and/or statutes of a geographic region. Alternatively, a single compliance application  142  may verify an entity&#39;s compliance of all relevant laws, regulations, and/or statutes of a geographic region. The entity may be a financial institution, social media company, retail store, ecommerce website, government institution, educational institution, and/or the like. 
     Compliance application  142  includes controls which control the operation of compliance application  142  based on the current compliance data. Compliance data may be relevant laws and/or statutes. As an example, a given law may require two-step authentication for logging onto an entity&#39;s mobile application. Compliance application  142  may include a control to interrogate the entity&#39;s mobile application source code to confirm the entity&#39;s mobile application requires two-step authentication for logging onto the mobile application. In the event the mobile application does not require two-step authentication, the control of compliance application  142  may generate an error or alert. 
     To effectively and accurately execute compliance application  142 , it is necessary to provide the most current compliance data to the compliance application  142 , so that the controls can confirm the correct information. In this regard, deployment system  100  may execute scraping engine  152  to scrub external data sources  111  for updated compliance data. External data sources  111  may include databases, data repositories, websites, web services, RSS feeds, and/or the like. Scraping engine  152  may be a SCRAPY application developed in Python. The SCRAPY application is a web-crawler frame work that is configured to extract data from websites. Scraping engine  152  may extract data using Application Program Interfaces (APIs) or can be configured to be a general web-crawler. 
     Scraping engine  152  may include a set of instructions to search for and extract compliance data from various websites. Scraping engine  152  may include instructions to search for alphanumeric strings such as “new law”, “update in regulation”, “new legislation”, and/or the like. Scraping engine  152  may include instructions to extract any alphanumeric text relevant to updated compliance data. As an example, scraping engine  152  determines a date and time a “new law”, “update in regulation”, or “new legislation” has been posted on a website. If the new date and time within a specified time period (e.g., within the last week; last month; or last 6 months), then scraping engine  152  extracts the “new law”, “update in regulation”, or “new legislation” from the website. 
     Scraping engine  152  may return the updated compliance data to analyze engine  154 . The updated compliance data may include multiple different updated laws, regulations, and/or statutes, and their relevant regulation ID. The regulation ID may be an identification number of the law, regulation, and/or statute. For example, the regulation ID may be a statute number, U.S. Title and Section number, and/or the like. 
     Analyze engine  154  may query database  144  to retrieve the current compliance data stored in the database  144 . Analyze engine  154  may compare the current compliance data to the updated compliance data to determine the difference between the current compliance data and the updated compliance data. Analyze engine  154  may query database  144  to retrieve any controls relevant to the updated compliance data. Analyze engine  154  may correlate all of the controls to relevant to the updated compliance data. As described above, scraping engine  152  may return multiple different updated laws, regulations, and/or statutes and their relevant regulation ID. Analyze engine  154  may correlate the relevant control with each updated compliance data by matching a regulation ID of compliance data currently used by compliance application  142  with the regulation ID of the updated compliance data. 
     Analyze engine  154  may determine whether a control exists for the updated compliance data. In the event a control does not exist for the updated compliance data, analyze engine may generate a requirement for generating a new control for the updated compliance data. For example, the updated compliance data may be a new law, regulation, and/or statute. In the event a control for the updated compliance data exists, the analyze engine  154  may determine whether the control covers the updated compliance data. In the event the control data does not cover the updated compliance data, analyze engine  154  may generate a requirement for modifying the existing control to cover the updated compliance data. Analyze engine  154  may return the generated requirements. Deployment system  100  may output requirements to user device  146 . Deployment system  100  may store the requirements in the database  144 . 
     As a non-limiting example, scraping engine  152  may detect an updated code of advertising with the better business bureau (BBB). As an example input, the updated code on the (BBB) website may read, “2.1 Advertisers may offer a price reduction or savings by comparing their selling price with: 2.1.1 Their own former selling price”. Scraping engine  152  may extract the updated code from the BBB website and return the text of the updated code. 
     Analyze engine  154  may retrieve controls relevant to the updated code. The controls relevant to the updated code may be controls verifying the compliance of pricing and advertising. Analyze engine  154  may correlate the relevant controls with the updated code. Analyze engine  154  may correlate controls with the updated code by comparing the regulation ID of the updated code (i.e., 2.1 and 2.11) with the regulation ID of the compliance data currently used by the control. 
     Analyze engine  154  may determine whether a control exists to cover the updated code. In the event a control does not exist, analyze engine  154  may generate a requirement. Analyze engine  154  may determine whether a control exists for the updated compliance data. In the event a new control is needed for the updated compliance data, analyze engine  154  may generate a requirement for generating a new control for the updated compliance data. For example, the updated compliance data may be a new law, regulation, and/or statute. In the event a control for the updated compliance data exists, the analyze engine  154  may determine whether the control covers the updated compliance data. In the event the control data does not cover the updated compliance data, analyze engine  154  may generate a requirement for modifying the existing control to cover the updated compliance data. 
     Deployment system  100  may be configured to determine whether scraping engine  152  and/or analyze engine  154  are suitable for executing using multi-coring or multiprocessing. Multi-coring is the concept using dedicated cores to execute a single function. For the purposes of speed and efficiency, multi-coring may be executed asynchronously. In this regard, using multi-coring, the tasks of a function may be executed in an asynchronous order. Multiprocessing the running of two or more programs or sequences of instructions simultaneously by a computer with more than one central processor. Using multiprocessing deployment system  100  may execute the functions using anyone of the computing cores  116 - 124 . In multi-coring one or more cores may be dedicated to only execute a single function. In multiprocessing any one of the computing cores may execute multiple functions in parallel or serially. Computing cores  114  may be a pool of computing cores  116 - 124 . Computing cores  116 - 124  may be separate processing units configured to execute any function. Computing cores  116 - 124  may execute on one or more processors. Computing cores  116 - 124  independently or in combination with one another. Computing cores  114  may be part of the cloud computing system  140 . Alternatively, computing cores  114  may be separate from the cloud computing system  140 . 
     Deployment system  100  may determine whether a function is suitable for multi-coring or multiprocessing based on a series of steps. Initially, deployment system  100  may determine whether the code included in the function to be executed is computationally expensive. Deployment system  100  may determine the code is computationally expensive to execute based on an expected amount data to be processed by the code multiplied by an expected amount of calculations to be executed by the code. In response to determining the expected amount data to be processed by the code multiplied by the expected amount of calculations to be executed by the code is more than a threshold amount, the deployment system  100  may determine the code is computationally expensive. In response to determining code is not computationally expensive, deployment system may determine is the function is not suitable for multi-coring, as it may not be desirable to dedicate a set of resources to a function that is not computationally expensive to execute. 
     Next, deployment system  100  may determine whether the code of the function include calculations that are interdependent of each other. As described above, using multi-coring, the tasks of a function may be executed asynchronously. Accordingly, in the event a function includes calculations which are dependent on other calculations, multi-coring may not be suitable for this function as the calculations may be executed out of the desired order. Likewise, deployment system  100  also determines whether the function has interdependences with other functions. Multi-coring may not be suitable for a function in situations where the function is relying on other function calls, as the tasks of the function are executed asynchronously. 
     Next, deployment system  100  determines whether more than one function is computationally expensive. Multi-coring may not be suitable for when more than one function is computationally expensive as it may not be desirable to dedicate a large amount of computing cores to execute each computationally expensive function. 
     In the event deployment system  100  determines the code of the function is computationally expensive, does not include interdependent calculations, does not have interdependencies with other functions, and the more than one function are not computationally expensive, the deployment system  100  may determine the function may be suitable for multi-coring. Otherwise the deployment system  100  may determine the function is not suitable for multi-coring but rather is suitable for multiprocessing. 
     In the event a function is suitable for multi-coring, execution engine  150  may determine an amount of available computing cores. Execution engine  150  may determine the amount of computing cores necessary to execute the function. Execution engine  150  may assign the amount of computing cores from the available computing cores to execute the function. The assigned computing cores may execute the function and may not execute any other function until the function has completely executed. Execution engine  150  may execute the function on the assigned computing cores. 
     As the assigned computing cores execute the function asynchronously, the function may return data asynchronously. Execution engine  150  may receive the data from the function and store the data in a list data structure rather than a data frame data structure. In this regard, execution engine  150  can ensure a desired order of the data is maintained even though the data may be received out of order. As an example, in the event a function is configured to execute task  1 , task  2 , and task  3 . The assigned computing cores may execute the tasks in the following order: task  2 , task  3 , and task  1 , leading to return data from each of these tasks out of order. It may be desirable to maintain the order of returned data from task  1 , task  2 , and task  3 . Accordingly, execution engine  150  may maintain the order of the returned data in the list data structure as follows: [returned value from task  1 , returned value from task  2 , and returned value from task  3 ]. Execution engine  150  may transpose the list data structure into a data frame data structure, once the function has completely executed. 
     Once the assigned computing cores have completed the execution of the function using multi-coring, the assigned computing cores may be deemed available for selection again. 
     In the event deployment system  100  determines a function is suitable for multiprocessing, the execution engine may assign the function to a process and execute the process. The process may be executed by any one of the available computing cores. Deployment system  100  may execute multiple functions at once using multiprocessing. The functions may be assigned to processes and executed. The processes may be structured as follows P 1 =Process(target=function  1 , args( )); P 2 =Process(target=function  2 , args( )); P 3 =Process(target=function  3 , args( )). The args ( ) represent arguments required by each of the functions. The processes may be executed in parallel using any one of the available computing cores other than the computing cores dedicated to execute a function using multi-coring. The processes may be executed in parallel. 
     With reference to  FIG. 2 , example computing cores according to an embodiment are illustrated.  FIG. 1  and  FIG. 2  will be described concurrently. As described above, computing cores  114  is a pool of computing cores  116 - 124 . Each of the computing cores  116 - 124  may be configured to either execute multiple functions or may be instructed to be dedicated to execute a single function. 
     As a non-limiting example, deployment system  100  may be implemented in a financial institution or retail environment. First computing module  102  may be configured to detect customer complaints received via email, telephone, short messaging service (SMS), websites, web based applications, and/or the like. In the event of a natural disaster or power outage customer complaints may sharply increase and the influx of data to be processed may also increase. 
     Function  1   104  of the first computing module  102  may be tasked to detect and collect specified words or phrases in the customer complaints which may increase drastically based on the influx in incoming data. Function  1   104  may return the detected specified words or phrases in a data frame data structure. Due to the influx of incoming data, deployment system  100  may determine whether Function  1   104  is suitable for using multi-coring. 
     Deployment system  100  may determine that due to the influx of large amounts of data to be processed by function  1   104 , the amount of data to be processed multiplied by the calculations to be performed by function  104  will be greater than a threshold amount making it computationally expensive. Deployment system  100  may determine the calculations executed by function  1   104  are not interdependent on each other and function  1   104  is not interdependent of other functions in the first or second computing modules  102 ,  108 . Deployment system  100  may also determine that function  2   106  of the first computing module is not computationally expensive to execute based on an expected amount of data to be processed multiplied an expected number of calculations to be executed. Accordingly, deployment system  100  may determine function  1   104  is suitable for multi-coring. 
     Deployment system  100  may determine computing cores  116 - 124  are available. Deployment system  100  may determine that two computing cores are necessary to execute function  1   104 . Execution engine  150  may assign computing core  116  and  118  to execute function  1   104 , as described above. As a non-limiting example, when executing multi-coring in Python, a collect_df function can be programmed using a df.values.tolist( ) function so that function  1   104  does not directly return a data frame data structure rather the values returned from function  1   104  are collected in a list data structure. The df.values.tolist( ) function converts a data frame data structure into a list data structure. In this regard, the collect_df function receives the output data as the data frame data structure function  1   104  is configured to output and converts the data frame data structure into a list data structure. 
     Execution engine  150  may execute the function on computing cores  116  and  118  by instructing the assigned computing cores  116  and  118  to execute function  1   104  using the arguments required to execute function  1   104 . Additionally, execution engine  150  may call a function (i.e., collect_df) to receive the output data of function  1   104  as a list data structure. As an example, while executing multi-coring using Python, execution engine  150  can execute function on computing cores  116  and  118  by executing the following call: pool.apply_async(funct 1 , args=(x,y,z), callback=collect_df). Pool represents the assigned computing cores  116 - 118  dedicated to execute function  1   104 . Apply_async instructs computing cores  116 - 118  to execute the tasks of function  1   104  asynchronously. Funct 1  may represent function  1   104 . Args=(x,y,z) represent the arguments required to execute function  1   104 . Callback represents a list data structure configured to receive data from function  1   104  using the collect_df function. 
     Execution engine  150  may collect the data frame data structure returned by function  1   104  in a list data structure using the collect_df function. Execution engine  150  may convert the callback list data structure into a data frame data structure at the completion of the execution of function  1   104  by transposing the data in the callback list data structure into a data frame data structure. Once the execution of function  1   104  is completed, computing cores  116 - 118  can be deemed available again and eligible for executing different functions. 
     Deployment system  100  may determine function  2   106  of first computing module  102  and function  1   110  and function  2   112  of second computing module  108  may be executed using multi-processing. Accordingly, execution engine  150  may assign each of the function  1   106  and function  1  and  2   110 ,  112  to a process. The processes may be structured as follows P 1 =Process(target=function  2   106 , args( )); P 2 =Process(target=function  1   110 , args( )); P 3 =Process(target=function  2   112 , args( )). The args ( ) represent arguments required by each of the functions. The processes may be executed in parallel using any one of the available computing cores  120 - 124  other than the computing cores  116 - 118  dedicated to execute function  1   104 . Each of the processes may be initiated in parallel. 
       FIG. 3  illustrates example flow of compliance data according to an embodiment. A crawler  300  such as a scraping engine (e.g., scraping engine  152  as shown in  FIG. 1 ) may detect and extract updated compliance data from external data sources  111 . External data sources may include web sites of the Consumer Financial Protection Bureau (CFPB), Better Business Bureau (BBB), Office of the Comptroller of the Currency (OCC), and/or the like. 
     An analyzer  302  such as an analyze engine (e.g., analyze engine  154  as shown in  FIG. 1 ) may query database  142  to retrieve controls and compliance data currently used by the controls. Analyzer  302  may correlate the relevant controls with the updated compliance data using the regulation ID of the updated compliance data and the compliance data currently used by the controls. 
     In operation  304  analyzer  302  may determine whether a control exists to cover the updated compliance data. In the event a control does not exist, the analyzer  302  may generate a new requirement for generating a new control to cover the updated compliance data and store the new requirement in the database  142 . In the event a control does exist, in operation  306 , analyzer  302  may determine whether the control covers the updated compliance data. In the event the control does not cover the updated compliance data, analyzer  302  may generate a requirement for modifying the existing control and may store the requirement in database  142 . 
       FIG. 4A  illustrates example data structures according to an embodiment. As described above, while executing a function using multi-coring, the function completes the tasks asynchronously. In this regard, the function returns and/or outputs data asynchronously. The function may return and/or output values in a data frame data structure. A data frame data structure is a two dimensional data structure, where data is aligned in a tabular fashion in rows and columns. The data may be associated to a key value pair. The execution engine (e.g., execution engine  150  as shown in  FIG. 1 ) may receive and store the data from the function in a list data structure. A list data structure is a one dimensional changeable ordered sequence of elements. As the execution engine may receive the data asynchronously, the execution engine maintains a desired order so that the data in the list data structure can be accurately transposed to a data frame data structure. 
     As a non-limiting example, the deployment system (e.g., deployment system  100  as shown in  FIG. 1 ) may deploy a function using multi-coring which preforms the tasks of retrieving account holder IDs, names, and ages. Each respective account holder ID, name, and age can be tied to a single account. As the function is executed, the execution engine starts receiving output data from the function as the function completes the respective tasks asynchronously. The execution engine stores the output data in a list data structure  300 . List data structure  300  may include the account holder name of “Jon Doe.” However, it may be missing the respective account holder ID number and age. List data structure  300  may further include account holder ID number of “245” and account holder name of “Jane Smith”, however, the data structure may be missing the age of “Jane Smith”. List data structure  300  may further include account holder ID number of 567 and account holder age of 45, however, it may be missing the account holder name for account holder ID number of 567. As shown by list data structure  300 , the execution engine may store the data in a particular order such that the account holder ID, name, and age that are tied to the same account are adjacent to one another. However, it can be appreciated that the execution engine may store the data in any specified order such that the data from the list data structure may be transposed to a data frame data structure. 
     List data structure  402  may store more data as the function completes more tasks. The execution engine may receive the account holder ID number for “Jon Doe” and the age for “Jane Smith”. Accordingly, list data structure  402  may store the account holder ID number for “Jon Doe” and the age for “Jane Smith” in their designated positions in list data structure  402 . 
     As the function completes its final tasks, the execution engine may receive the age for “Jon Doe” and the name for account holder ID “567”. Accordingly, list data structure  404  may store the age for “Jon Doe” and the name for account holder “567” in their designated position in list data structure  404 . 
     Once the function has completed all of its tasks, the execution engine may determine list data structure  404  is complete. The execution engine may then transpose the values of list data structure  404  into a data frame data structure  406 . As a non-limiting example, data frame data structure  406  may be set up to include three rows and three columns. The first column may store account holder ID numbers, the second column can store account holder names, and the third column can store account holder ages. The account holder ID number may be the key value pair. The execution engine transposes the value in the order maintained by list data structure  304 . For example, account holder ID number “123”, account holder name “Jon Doe”, and account holder age “26” are transposed into the first row; holder ID number “245”, account holder name “Jane Smith”, and account holder age “35” are transposed into the second row; and holder ID number “567”, account holder name “Bob Smith”, and account holder age “45” are transposed into the third row. Data frame data structure  406  may store the data in the desired order, such that information for each separate account is stored in a single row. 
       FIG. 4B  illustrates example data structures according to an embodiment. As described above, while executing a function using multi-coring, the function completes the tasks asynchronously. A data frame data structure is a two-dimensional data structure, where data is aligned in a tabular fashion in rows and columns. The data may be associated to a key value pair. A list data structure is a one-dimensional changeable ordered sequence of elements. The function returns the data asynchronously to the function (i.e., collect_df) to receive the output data of scraping engine  152  as a list data structure. As the list data structure may receive the data asynchronously, the list data structure maintains a desired order so that the data in the list data structure can be accurately transposed to a data frame data structure. 
     As a non-limiting example, the deployment system (e.g., deployment system  100  as shown in  FIG. 1 ) may deploy a function, such as the scraping engine, using multi-coring which preforms the task scraping external data sources for compliance data different than the compliance data currently used by compliance applications. The compliance data may include laws or regulations that determine compliance of an entity. The compliance data may be alphanumeric text. As the scraping engine is executed, the execution engine starts receiving output from the scraping engine as the function completes the respective tasks asynchronously. The output data can include an updated law or regulation and regulation ID identifying the law or regulation. The regulation ID can be a statute number, US Title and Section number, and/or the like. The regulation ID can be the key value pair. The execution engine stores the output data in a list data structure  450 . List data structure  450  may include regulation ID “35 U.S.C. 456” and may be missing the updated regulation. List data structure  400  may further include regulation ID § 1200.1 and the updated regulation. As shown by list data structure  450 , the execution engine may store the data in a particular order such that the regulation ID and the updated regulation are adjacent to one another. However, it can be appreciated that the execution engine may store the data in any specified order such that the data from the list data structure may be transposed to a data frame data structure. 
     List data structure  452  may store more data as the function completes more tasks. The execution engine may receive the updated regulation for regulation ID “35 U.S.C. 456”. Accordingly, list data structure  452  may store the updated regulation for code number “35 U.S.C. 456” in its designated positions in list data structure  452 . The execution engine may also receive regulation ID “§ 347.106k” and the updated regulation. Accordingly, list data structure  452  may store regulation ID “§ 347.106k” and the updated regulation in its respective position in the list data structure  452 . 
     As the function completes its final tasks, the execution engine may receive regulation ID “§ 347.101” and the updated regulation. Accordingly, list data structure  454  may store receive regulation ID “§ 347.101” and the updated regulation in their designated positions in list data structure  454 . 
     Once the function has completed all of its tasks, the execution engine may determine list data structure  454  is complete. The execution engine may then transpose the values of list data structure  454  into a data frame data structure  456 . As a non-limiting example, data frame data structure  456  may be set up to include two rows and four columns. The first column may store regulation ID numbers, the second column stores the alphanumeric text of the updated regulation. The regulation ID number may be the key value pair. The execution engine transposes the value in the order maintained by list data structure  454 . For example, “35 U.S.C. 456” and the updated regulation are transposed into the first row; “§ 347.101” and the updated regulation are transposed into the second row; “§ 1200.1” and the updated regulation are transposed into the third row; “§ 347.101” and the updated regulation are transposed to the fourth row. Data frame data structure  456  may store the data in the desired order, such that information for each updated regulation is stored in a single row. 
       FIG. 5  is a flowchart  500  illustrating a method for executing a computing module according to an embodiment. Searching an external data source for updated compliance data different than compliance data used by a compliance application may be executed by a function (i.e., scraping engine  152  as shown in  FIG. 1 ). 
     Flowchart  500  starts at operation  502 . In operation  502 , a deployment system may determine execution of a function of a first computing module requires more than a threshold amount of computing resources. Computing resources may include memory, CPU power, storage space, and/or the like. The function of the first computing module may be code to be executed. The deployment system may determine execution of the function is computationally expensive based on an expected amount data to be processed by the function multiplied by an expected amount of calculations to be executed by the function. 
     In operation  504 , an execution engine may determine available computing cores. The execution engine may identify the available computing cores from a pool of computing cores. Each computing core can be a separate processing unit. 
     In operation  506 , the execution engine may assign the one or more computing cores to execute the function of the first computing module. 
     In operation  508 , the execution engine may execute the function of the first computing module using the assigned one or more computing cores. The assigned one or more computing cores are dedicated to executing the function of the first computing module. The execution engine may transmit a call to the assigned one or more computing cores. The call may include instructions to the assigned one or more computing cores to execute the function asynchronously. The call may further include arguments required by the function to perform the tasks of the function. The call may further include a different call to a function for converting a data frame data structure to be output by the function to a list data structure. 
     In operation  510 , the execution engine may receive output data from the function of the first computing module asynchronously while the function of the first computing module is being executed. Each of the tasks of the function may be executed asynchronously. For example, the function may include task  1 ; task  2 ; and task  3 , and task  1 , task  2 , and task  3  may be executed concurrently by the assigned computing cores. The assigned computing cores may execute different tasks of the function irrespective of their order within the function. The function may output data in response to completing a task irrespective of the order of the task in the function. In the event task  3  is completed before task  1 , the function will output the result of task  3  before task  1 . 
     In operation  512 , the execution engine may store the output data as the output data is received in a list data structure as described with respect to operation  408 . As the data is being received asynchronously, the list data structure maintains a desired order of the output data. 
     In operation  514 , the execution engine may convert the list data structure into a data frame data structure based on the desired order and priority of the output data. The list may be a one-dimensional data structure and the data frame data structure may be a two dimensional data structure. The execution engine may transpose the output data from the list data structure to the data frame data structure. The execution engine may ensure the data is transposed from the list to the data frame in the desired order. 
     In operation  516 , the deployment system may output the data frame data structure. The data frame data structure may be output to a user device. Alternatively, the data frame data structure may be output to a different sub-computing system within a distributed and/or cloud computing environment, for further processing. 
       FIG. 6  is a flowchart  600  illustrating a process for verifying a computing module is suitable for multi-coring according to an embodiment. 
     Flowchart  600  starts at operation  602 . In operation  602 , a deployment system may determine execution of a function of a first computing module requires more than a threshold amount of computing resources. Computing resources may include memory, CPU power, storage space, and/or the like. 
     In operation  604 , the deployment system may determine available computing cores. 
     In operation  606 , the deployment system may verify whether the function of the first computing module is suitable to be executed by one or more computing cores of the available computing cores. In determining whether the function of the first computing module is suitable by the one or more computing cores of the available computing cores the deployment system determines whether the function is suitable to be executed using multi-coring. As described above, multi-coring is dedicating one or more computing cores to execute the function. The deployment system may determine a function is suitable for multi-coring based on the amount of data to be processed multiplied by the calculations to be executed by the function being below a threshold amount, the function not including any interdependent calculations, and the function not having any interdependencies with other functions. 
     In operation  608 , in response to verifying the function of the first computing module is suitable to be executed by the one or more computing cores of the available computing cores, the deployment system may assign the one or more computing cores to execute the function of the first computing module. 
     In operation  610 , the deployment system may execute the function of the first computing module using the assigned one or more computing cores. The assigned one or more computing cores are dedicated to executing the function of the first computing module. 
     In operation  612 , the deployment system may receive output data from the function of the first computing module asynchronously while the first computing module is being executed. 
     In operation  614 , the deployment system may store the output data as the output data is received in a list data structure, wherein the list data structure maintains a desired order of the output data. 
     In operation  616 , the deployment system may convert the list data structure into a data frame data structure based on the desired order and priority of the output data. 
     In operation  618 , the deployment system may output the data frame data structure. 
       FIG. 7  is a flowchart  700  illustrating a process for verifying a computing module is suitable for multi-coring according to an embodiment. 
     Flowchart  700  starts with operation  702 . In operation  702 , a deployment system may determine whether the function of the first computing module includes interdependencies between calculations executed in the function of the first computing module. As the tasks of the function are completed asynchronously, calculations of the function are executed out of the intended order. In this regard, there cannot be interdependencies between calculations when executing the function using multi-coring, as the assigned computing cores may attempt to execute a second calculation without waiting for the result of the first calculation. If the second calculation includes a variable or value to be calculated by the first calculation, executing the second calculation before the completion of the first calculation may cause an error. 
     In operation  704 , the deployment system may determine whether the function of the first computing module has interdependencies with any other function of the first computing module. As stated above, in multi-coring tasks of the function are executed asynchronously. Therefore, while using multi-coring the function may not rely on different function calls as the tasks are not completed in the intended order. 
     In operation  706 , in response to determining the function of the first computing module is void of interdependencies between calculations executed in the function of the first computing module or other functions of the first computing module, the deployment system may determine that the first computing module is suitable for execution on the assigned one or more computing cores. In the event the deployment system may determine execution of more than one function requires more than the threshold amount of computing resources or the function interdependent calculations or have interdependencies with another function, the deployment system may execute the first and second functions of the second computing module, in parallel, using any one of the available computing cores. 
       FIG. 8  is a flowchart  800  illustrating a process for identifying controls which do not align with updated compliance data according to an embodiment. 
     Flowchart  800  starts at operation  802 . In operation  802 , a scraping engine searches an external data source for updated compliance data different than compliance data currently used by a compliance application. Scraping engine may be a SCRAPY application developed in python. SCRAPY is an open-source web crawling framework written in Python. SCRAPY is built using self-contained crawlers that may be given a set of instructions. External data sources may include websites, databases, data repositories, RSS feeds, web services, and/or the like. 
     In operation  804 , the scraping engine extracts the updated compliance data from the external data source. The scraping engine may extract the alphanumeric string of the updated compliance data from the external data source. 
     In operation  806 , an analyze engine correlates the updated compliance data to the data utilized by the compliance application stored in a database. The analyze engine may correlate the updated compliance data with the compliance data by matching a regulation ID number of the updated compliance data with a regulation ID of the compliance data. 
     In operation  808 , the analyze engine identifies a control that fails to adhere to the updated compliance data based on a difference between the updated compliance data and the compliance data currently used by the compliance application. The control may control the operation of the compliance application based on the compliance data. 
     In operation  810 , the analyze engine outputs the identified controls and a requirement to align the identified control with the updated compliance data. The analyze engine may store the requirement in the database. 
       FIG. 9  is a block diagram of example components of computer system  900 . One or more computer systems  900  may be used, for example, to implement any of the embodiments discussed herein, as well as combinations and sub-combinations thereof. Computer system  900  may include one or more processors (also called central processing units, or CPUs), such as a processor  904 . Processor  904  may be connected to a communication infrastructure or bus  907 . 
     Computer system  900  may also include user input/output interface(s)  902 , such as monitors, keyboards, pointing devices, etc., which may communicate with communication infrastructure  907  through user input/output interface(s)  902 . 
     One or more of processors  904  may be a graphics processing unit (GPU). In an embodiment, a GPU may be a processor that is a specialized electronic circuit designed to process mathematically intensive applications. The GPU may have a parallel structure that is efficient for parallel processing of large blocks of data, such as mathematically intensive data common to computer graphics applications, images, videos, etc. 
     Computer system  900  may also include a main or primary memory  908 , such as random access memory (RAM). Main memory  908  may include one or more levels of cache. Main memory  908  may have stored therein control logic (i.e., computer software) and/or data. 
     Computer system  900  may also include one or more secondary storage devices or memory  910 . Secondary memory  910  may include, for example, a hard disk drive  912  and/or a removable storage drive  914 . 
     Removable storage drive  914  may interact with a removable storage unit  918 . Removable storage unit  918  may include a computer usable or readable storage device having stored thereon computer software (control logic) and/or data. Removable storage unit  918  may be a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and/or any other removable storage unit and associated interface. Removable storage drive  914  may read from and/or write to removable storage unit  918 . 
     Secondary memory  910  may include other means, devices, components, instrumentalities or other approaches for allowing computer programs and/or other instructions and/or data to be accessed by computer system  900 . Such means, devices, components, instrumentalities or other approaches may include, for example, a removable storage unit  922  and an interface  920 . Examples of the removable storage unit  922  and the interface  920  may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and/or any other removable storage unit and associated interface. 
     Computer system  900  may further include a communication or network interface  924 . Communication interface  924  may enable computer system  900  to communicate and interact with any combination of external devices, external networks, external entities, etc. (individually and collectively referenced by reference number  928 ). For example, communication interface  924  may allow computer system  900  to communicate with external or remote devices  928  over communications path  926 , which may be wired and/or wireless (or a combination thereof), and which may include any combination of LANs, WANs, the Internet, etc. Control logic and/or data may be transmitted to and from computer system  900  via communication path  926 . 
     Computer system  900  may also be any of a personal digital assistant (PDA), desktop workstation, laptop or notebook computer, netbook, tablet, smart phone, smart watch or other wearable, appliance, part of the Internet-of-Things, and/or embedded system, to name a few non-limiting examples, or any combination thereof. 
     Computer system  900  may be a client or server, accessing or hosting any applications and/or data through any delivery paradigm, including but not limited to remote or distributed cloud computing solutions; local or on-premises software (“on-premise” cloud-based solutions); “as a service” models (e.g., content as a service (CaaS), digital content as a service (DCaaS), software as a service (SaaS), managed software as a service (MSaaS), platform as a service (PaaS), desktop as a service (DaaS), framework as a service (FaaS), backend as a service (BaaS), mobile backend as a service (MBaaS), infrastructure as a service (IaaS), etc.); and/or a hybrid model including any combination of the foregoing examples or other services or delivery paradigms. 
     Any applicable data structures, file formats, and schemas in computer system  900  may be derived from standards including but not limited to JavaScript Object Notation (JSON), Extensible Markup Language (XML), Yet Another Markup Language (YAML), Extensible Hypertext Markup Language (XHTML), Wireless Markup Language (WML), MessagePack, XML User Interface Language (XUL), or any other functionally similar representations alone or in combination. Alternatively, proprietary data structures, formats or schemas may be used, either exclusively or in combination with known or open standards. 
     In some embodiments, a tangible, non-transitory apparatus or article of manufacture comprising a tangible, non-transitory computer useable or readable medium having control logic (software) stored thereon may also be referred to herein as a computer program product or program storage device. This includes, but is not limited to, computer system  900 , main memory  908 , secondary memory  910 , and removable storage units  918  and  922 , as well as tangible articles of manufacture embodying any combination of the foregoing. Such control logic, when executed by one or more data processing devices (such as computer system  900 ), may cause such data processing devices to operate as described herein. 
     It is to be appreciated that the Detailed Description section, and not the Summary and Abstract sections, is intended to be used to interpret the claims. The Summary and Abstract sections may set forth one or more but not all exemplary embodiments of the present disclosure as contemplated by the inventor(s), and thus, are not intended to limit the present disclosure and the appended claims in any way. 
     The present disclosure has been described above with the aid of functional building blocks illustrating the implementation of specified functions and relationships thereof. The boundaries of these functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternate boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. 
     The foregoing description of the specific embodiments will so fully reveal the general nature of the disclosure that others can, by applying knowledge within the skill of the art, readily modify and/or adapt for various applications such specific embodiments, without undue experimentation, without departing from the general concept of the present disclosure. Therefore, such adaptations and modifications are intended to be within the meaning and range of equivalents of the disclosed embodiments, based on the teaching and guidance presented herein. It is to be understood that the phraseology or terminology herein is for the purpose of description and not of limitation, such that the terminology or phraseology of the present specification is to be interpreted by the skilled artisan in light of the teachings and guidance. 
     The breadth and scope of the present disclosure should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.