Systems and methods to convert information technology infrastructure to a software-defined system

Disclosed herein are system, method, and computer program product embodiments for a method of cloud infrastructure optimization. The method identifies an existing infrastructure configuration deployed in a cloud environment and generates a plurality of proposal configurations, each of the plurality of proposal configurations having executable code configured to adjust the existing infrastructure configuration for at least one variable. The method selects a proposal configuration from the plurality of proposal configurations based on the at least one variable adjusted for in the existing infrastructure configuration, and the selected proposal configuration is deployed in the cloud environment. The method then analyzes the selected proposal configuration for a level of adjustment for the at least one variable. The method trains a model engine with existing and new training data.

FIELD

The present disclosure relates to a method of optimizing information technology (“IT”) system infrastructures deployed into cloud environments. In particular, the infrastructure of the IT system is optimized for at least one specific variable while maintaining compliance with rules of the cloud environment.

BACKGROUND

Information technology (“IT”) systems are often deployed into cloud environments on one or more servers for access by one or more client devices. When deploying such IT systems into cloud environments, the infrastructure of the IT system must be compliant with rules of the cloud environment and be able to handle various workloads depending on system complexity and/or client demand. To build and manage infrastructure of the deployed IT system, infrastructure-as-code (“IAC”), or a collection of code written to represent machine-readable definition files of the IT system, may be run through a compiler and execution engine. Any modifications to the system infrastructure must first be updated in the IAC before being re-run through the compiler and execution engine to result in corresponding changes in the underlying infrastructure of the deployed system in the cloud environment.

BRIEF SUMMARY

Disclosed herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof for optimizing IT system infrastructures deployed into cloud environments. The infrastructure of the IT system is optimized for a specific variable, such as cost, security, performance, resiliency, latency, scalability, etc., and rules of the cloud environment are maintained.

In some embodiments, a method of cloud infrastructure optimization includes using a processor to identify an existing infrastructure configuration deployed in a cloud environment. Based on the identified existing infrastructure configuration, the processor can generate a plurality of proposal configurations, each of the plurality of proposal configurations having executable code configured to adjust the existing infrastructure configuration for at least one variable. The processor can select a proposal configuration from the plurality of proposal configurations based on the at least one variable adjusted for in the existing infrastructure configuration. The selected proposal configuration is deployed in the cloud environment, and the processor analyzes the selected proposal configuration for a level of adjustment for the at least one variable. The processor can train a model engine with existing and new training data.

In some examples, the processor can train the model engine by adjusting executable code of the selected proposal configuration based on the analysis of the selected proposal configuration for the level of adjustment.

In some examples, the processor can replace the existing infrastructure configuration with the adjusted executable code of the selected proposal configuration to generate the plurality of proposal configurations. The processor can then proceed to repeat the method steps of generating the plurality of proposal configurations, selecting a proposal configuration from the plurality of proposal configurations, deploying the selected proposal configuration in the cloud environment, analyzing the selected proposal configuration for the level of adjustment, and training the model engine with existing and new training data. In some examples, when training the model engine, the processor can further verify whether the selected proposal configuration is compliant with a set of rules of the cloud environment. The processor can periodically analyze the selected proposal configuration for the level of adjustment for the at least one variable and for compliance with the set of rules of the cloud environment.

In another embodiment, a system includes a memory for storing instructions and one or more processors, communicatively coupled to the memory, configured to execute the instructions. The instructions causes the one or more processors to identify an existing infrastructure configuration deployed in a cloud environment. Based on the identified existing infrastructure configuration, a plurality of proposal configurations are generated, each of the plurality of proposal configurations having executable code configured to adjust the existing infrastructure configuration for at least one variable. A proposal configuration is selected from the plurality of proposal configurations based on the at least one variable adjusted for in the existing infrastructure configuration, and the selected proposal configuration is deployed in the cloud environment. The selected proposal configuration is analyzed for a level of adjustment for the at least one variable. The instructions can cause the one or more processors to train a model engine with existing and new training data.

In yet another embodiment, a non-transitory, tangible computer-readable device has instructions stored thereon that, when executed by at least one computing devices, causes the at least one computing device to perform operations. The at least one computing device identifies an existing infrastructure configuration deployed in a cloud environment. Based on the identified existing infrastructure configuration, the at least one computing device can generate a plurality of proposal configurations, each of the plurality of proposal configurations having executable code configured to adjust the existing infrastructure configuration for at least one variable. The at least one computing device can select a proposal configuration from the plurality of proposal configurations based on the at least one variable adjusted for in the existing infrastructure configuration. The selected proposal configuration is deployed in the cloud environment, and the at least one computing device analyzes the selected proposal configuration for a level of adjustment for the at least one variable. The at least one computing device can train a model engine with existing and new training data.

Descriptions provided in the summary section represent only examples of the embodiments. Other embodiments in the disclosure may provide varying scopes different from the description in the summary.

DETAILED DESCRIPTION

Currently, the creation, modification, and optimization of the IAC is performed as a single-path process, where the collection of code is written to represent the infrastructure “as-is.” A user manually applies modifications to the infrastructure and checks for compliance with rules of the cloud environment. Thereafter, the modified IAC is re-run through the compiler and execution engine. This process is time-consuming, prone to human error and oversight, and ineffective in optimizing the infrastructure of the deployed IT system for specific variables. Furthermore, this process does not allow for easy migration of existing infrastructure to other cloud platforms offering a specific application or service to client devices because the existing infrastructure must be manually checked for compliance with the new cloud platform's rules. Therefore, a new method of modifying the IAC is needed to better manage and optimize the infrastructure of the deployed IT system in the cloud environment and to maintain compliance with the cloud environment rules.

Embodiments described herein are directed to a new method of optimizing, analyzing, and managing infrastructure configurations of deployed IT systems in a cloud environment. The method may model existing IT system infrastructures deployed in the cloud environment and, based on the “as-is” model of the existing infrastructure, generate proposal infrastructure configurations that are optimized for a specific variable. Depending on the specific variable that a user wishes to optimize, the user may be presented with and then select a proposal infrastructure configuration to be deployed in the cloud environment. The method then analyzes the performance of the deployed infrastructure configuration in optimizing the system for the specific variable through a system validation process. Based on this analysis, the method may adjust the infrastructure configuration or generate improved proposal configurations to ensure continued optimization of the deployed IT system infrastructure in the cloud environment. Specifically, the method may execute feedback loops to generate next generations of proposal infrastructure configurations that further optimize for the specific variable.

FIG.1shows a cloud environment100according to an embodiment of the present disclosure In some embodiments, cloud environment100may be the Internet and/or other public or private networks or combinations thereof. One or more resources105and one or more client devices106may connect to cloud environment100. Resources105may provide IT infrastructure for cloud-based applications and/or other software available to client devices106through cloud environment100. For example, resources105may include cloud-based hosting and/or computing devices. Those of ordinary skill in the art will recognize that the number of resources105and the number of client devices106connected to cloud environment100may vary in different embodiments of the present disclosure and are not exhaustively described herein. Furthermore, resources105may have any configuration available in the art and may be capable of providing any deployment services available in the art and/or subsets thereof.

A server110may communicate with the cloud environment100and control optimization and compliance of the IT infrastructures deployed in cloud environment100. Server110may communicate with and store information to a memory115. In some embodiments, information stored in memory115may include IT infrastructure configuration models, evaluation results of optimization analysis conducted on infrastructure configuration models, compliance rules of the cloud environment100, etc. Server110is depicted inFIG.1as a single device for ease of illustration, but those of ordinary skill in the art will appreciate that server110may be embodied in different forms for different implementations. For example, server110may include a plurality of servers that work together to manage optimization and compliance of IT infrastructures deployed in cloud environment100. Components of an exemplary server110will be described in further detail below with reference to the following figures.

FIG.2shows an exemplary server110according to an embodiment of the present disclosure Server110may be implemented on any electronic device that runs software applications derived from compiled instructions, including, without limitation, personal computers, servers, smart phones, media players, electronic tablets, game consoles, email devices, etc. In some embodiments, server110may include one or more processors205, one or more input devices210, one or more network interfaces215, one or more display devices220, and one or more computer readable mediums225. Each of these components may be coupled by bus200, which enables communication between various components of server110.

Bus200may be any known internal or external bus technology, including but not limited to ISA, EISA, PCI, PCI Express, NuBus, USB, Serial ATA or FireWire. Processors205may use any known processor technology, including but not limited to graphics processors and multi-core processors. Input devices210may be any known input device technology, including but not limited to a keyboard (including a virtual keyboard), mouse, track ball, and touch-sensitive pad or display, which allows a user to manually provide an input to server110. Display devices220may be any known display technology, including but not limited to display devices using Liquid Crystal Display (LCD) or Light Emitting Diode (LED) technology, which allows server110to output information to the user. Computer-readable medium225may be any medium that participates in providing instructions to processors205for execution, including but not limited to non-volatile storage media (e.g., optical disks, magnetic disks, flash drives, etc.), or volatile media (e.g., SDRAM, ROM, etc.).

In some embodiments, computer-readable medium225may include various instructions230-234. In one example, computer-readable medium225may include various instructions230for implementing an operating system (e.g., Mac OS®, Windows®, Linux). The operating system may be multi-user, multiprocessing, multitasking, multithreading, real-time, and the like. The operating system may perform basic tasks, including but not limited to: recognizing input from input devices210; sending output to display devices220; keeping track of files and directories on computer-readable medium225; controlling peripheral devices (e.g., disk drives, printers, etc.) which can be controlled directly or through an I/O controller; and managing traffic on bus200. In another example, computer-readable medium225may also include various instructions232for establishing and maintaining network connections (e.g., software for implementing communication protocols, such as TCP/IP, HTTP, Ethernet, telephony, etc.). In another example, computer-readable medium225may further include various instructions234to perform optimization processing of IT infrastructure configurations deployed in cloud environment100, as described in further detail with respect toFIGS.3and4below. The exemplary instructions described herein are for illustrative purposes only and are not intended to be exhaustive. Those of ordinary skill in the art will recognize that various other types of instructions achieving different purposes may be included in computer-readable medium225in other embodiments of the present disclosure.

An exemplary method for optimizing IT system infrastructure configurations according to some aspects of the present disclosure will now be described with reference toFIGS.3and4.FIG.3shows a flowchart illustrating a method300for optimizing IT system infrastructure configurations according to an embodiment of the present disclosure. Some operations of method300may be performed in a different order and/or vary, and method300may include more operations that are not described herein for simplicity.FIG.4shows a block diagram of an exemplary server configured to implement method300shown inFIG.3.

Referring toFIG.3, at step305, method300identifies an existing infrastructure configuration previously deployed in cloud environment100. As shown in the block diagram ofFIG.4, server110includes a scanner405configured to scan existing infrastructure in cloud environment100as provided by resources105. For example, existing infrastructure may include existing firewalls or application databases providing services to client devices106. In some embodiments, scanner405may collect the scanned existing infrastructure configurations and store the collected information in memory115.

At step310, method300generates a plurality of proposal configurations based on the existing infrastructure configuration identified in step305. Various resources may be used to guide the generation of the plurality of proposal configurations, including but not limited to organizational standards or best practice regulations, industry standards or best practice regulations, and machine learning algorithms. These resources may be available to method300as programmable computer code, and the selection of which resource to use may be triggered via preset thresholds. For example, if the existing infrastructure identified in step305has a maximum utilization ratio of only 20%, then a best practice regulations resource may be triggered to guide the generation of the plurality of proposal configurations such that the generated plurality of proposal configurations focus on downsizing the existing infrastructure and optimizing its cost.

As shown in the block diagram ofFIG.4, server110includes a generator410that receives the existing infrastructure identified by scanner405and generates a plurality of proposal configurations415. In some embodiments, each proposal configuration415includes at least three parts. First, each proposal configuration415includes a collection of code, or infrastructure-as-code (i.e., IAC), written to represent machine-readable definition files of one possible infrastructure configuration of the IT system deployable in cloud environment100. The number of proposal configurations415generated by generator410may differ in various embodiments of the present disclosure and are not exhaustively illustrated inFIG.4or described herein.

Second, each proposal configuration415includes a summary of proposed configuration model outputs, including which specific variable(s) is/are optimized for in each proposed configuration415and optimization thresholds across common specific variables optimized for in the plurality of proposal configurations415. Specifically, in some embodiments, generator410first generates an “as-is” IAC model of the existing infrastructure configuration already deployed in cloud environment100and displays the “as-is” IAC model as “Proposal A.” Based on the “as-is” IAC model of Proposal A, generator410may generate a number of additional proposal configurations B-X, each proposal configuration including IAC representing a possible IT system infrastructure configuration that optimizes the “as-is” IAC model of the existing infrastructure in Proposal A for a specific variable. For example, “Proposal B” may include IAC representing an infrastructure configuration that optimizes the “as-is” IAC model in Proposal A for implementation cost. On the other hand, “Proposal C” may include IAC representing an infrastructure configuration that optimizes the “as-is” IAC model in Proposal A for system security, and so on. In some embodiments, each proposal configuration415optimizes the existing infrastructure configuration for one specific variable. In other embodiments, each proposal configuration415may optimize the existing infrastructure configuration for multiple variables. In embodiments where proposal configurations415optimize the existing infrastructure configuration for multiple variables, an optimization weight may be assigned to each of the specific variables, as explained in further detail below.

Examples of the specific variable may include cost, security, performance, resiliency (i.e., the system's ability to handle failures and recover system data), latency (i.e., the amount of time required for a data packet to travel from one point to another point within the system, in other words, the speed of data transmission), scalability (i.e., the system's ability to handle a growing amount of work caused by adding resources to the system), etc. It should be understood that the specific variables enumerated in the present disclosure are for illustrative purposes only and are not intended to be exhaustive. Those of ordinary skill in the art will recognize that proposal configurations415may optimize the existing infrastructure in Proposal A for various other types of specific variables in other embodiments of the present disclosure.

Third, each proposal configuration415includes a history or lineage of proposed configuration model decisions and data, including how each proposal configuration415was created by generator410. This history or lineage may include decisions made by generator410in generating each of the proposal configurations415, thereby providing transparency of system operations of server110.

Referring toFIG.3, at step315, method300implements an input-based validation process to select a proposal configuration from the plurality of proposal configurations415generated by generator410in step310to deploy in cloud environment100. As shown in the block diagram ofFIG.4, server110includes an input-based validation engine420that receives an input425. In some embodiments, input425may be a manual input provided by a user. For example, generator410may display proposal configurations415on display devices220such that the user may provide input425by manually selecting a proposal configuration415via input devices210. In this example, the user would select a proposal configuration415based on which specific variable the user wished to optimize in the existing infrastructure configuration. In other embodiments, input425may be an automatic input provided by a computer. For example, processor205of server110may automatically determine which proposal configuration415to deploy in cloud environment100based on a predetermined algorithm, such as instructions234for performing optimization processing stored in computer readable medium225(seeFIG.2). In this example, instructions234may include a predetermined specific variable that needs to be optimized in the existing infrastructure configuration.

In some embodiments, input-based validation engine420may further generate a confidence score for each proposal configuration415as part of the input-based validation process. For example, the confidence score may be a percentage between 0% and 100% representing how often each of the proposal configurations415are selected for deployment in cloud environment100. In this example, a proposal configuration415with a confidence score of 100% is extremely certain to deploy in cloud environment100, whereas a proposal configuration415with a confidence score of 0% will essentially never be chosen to deploy in cloud environment100. In some embodiments, the confidence score may be determined by system validation engine430using an artificial intelligence algorithm or artificial neural network (ANN) having a collection of connected units/nodes (i.e., artificial neurons) that work together to make decisions. In determining the confidence score, system validation engine430may also take into consideration variables including user feedback, effectiveness of proposal configuration performance, and threshold requirement/rules of the cloud environment.

Initially, a minimum confidence score may be defined by an administrator or user such that a proposal configuration415must meet the minimum confidence score before being selected to deploy in cloud environment100. Over time, method300may automatically adjust the minimum confidence score needed to deploy a selected proposal configuration415based on a calculated success rate of past deployments of the selected proposal configuration415in cloud environment100. The process of determining the success rate of past deployments of the selected proposal configuration415is described in further detail below.

Referring toFIG.3, at step320, method300deploys the selected proposal configuration415, as determined by the input-based validation process in step315, in cloud environment100.

Referring toFIG.3, at step325, method300implements a system validation process to automatically analyze the selected proposal configuration415deployed in cloud environment100for a level of adjustment of the specific variable optimized for by the deployed proposal configuration415. The level of adjustment illustrates how well the deployed proposal configuration415optimizes the specific variable. As shown in the block diagram ofFIG.4, server110includes a system validation engine430that connects to cloud environment100and analyzes the deployed proposal configuration415for the level of adjustment. For example, a low level of adjustment signifies that the deployed proposal configuration415failed in making improvements to the IAC to result in sufficient optimization of the existing infrastructure. On the other hand, a high level of adjustment signifies that the deployed proposal configuration415succeeded in making improvements to the IAC to result in sufficient optimization of the existing infrastructure. In this context, sufficient optimization of the existing infrastructure may be defined by a number of factors, including but not limited to cost, transactions per second, application performance metrics, etc. Sufficient optimization may be defined from the organization standard at the time that method300performs the system validation process. Furthermore, in some embodiments, a combination of weights, ranging from 0 to 1, may be applied to each factor defining the sufficiency of optimization. In some embodiments, the level of adjustment may be represented through a numerical score. The success rate of a proposal configuration415is defined by achieving a predefined level of adjustment for a predefined percentage of deployments in cloud environment100. In some embodiments, method300may use the success rate of a proposal configuration415to adjust the minimum confidence score needed to deploy the proposal configuration415in cloud environment100, as explained above.

In some embodiments, method300may periodically execute step325to analyze the deployed proposal configuration415for the level of adjustment of the specific variable. By periodically executing step325, method300ensures that the deployed proposal configuration415continues to optimize the IT system infrastructure for the specific variable over time. Specifically, method300ensures that a proposal configuration415that sufficiently optimized for the specific variable when initially deployed in cloud environment100continues to sufficiently optimize for the specific variable after operating in cloud environment100for a predetermined period of time. Those of ordinary skill in the art will recognize that method300may execute step325at various periodic intervals, which are not exhaustively listed herein for simplicity.

In some embodiments, method300may utilize machine learning to train a model engine with training data, as explained in further detail below with reference to steps330-340. Method300may further generate next generations of proposal configurations415by executing life cycle iterations via a feedback loop, as explained in further detail below with reference to loop345. Next generations of proposal configurations415may further optimize proposal configurations received from a trained model engine for various specific variables to ensure continued optimization of the deployed IT system infrastructure in cloud environment100.

Referring toFIG.3, at step330, method300may train a model engine with existing and new training data by executing step335and step340. At step335, method300may adjust IAC of the deployed proposal configuration to further optimize the deployed proposal configuration415for the specific variable in future iterations of method300. To accomplish further optimization of the deployed proposal configuration415, system validation engine430provides the deployed proposal configuration415and the analysis results from step325of the deployed proposal configuration's level of adjustment to a model engine435(seeFIG.4). As shown in the block diagram ofFIG.4, model engine435receives input from a cloud governance algorithm440. In some embodiments, cloud governance algorithm440includes at least optimization weights442and compliance rules444. In other embodiments, cloud governance algorithm440may include more or less parameters not exhaustively described herein. Model engine435uses the input from cloud governance algorithm440to generate a next generation proposal configuration that adjusts and updates IAC of the deployed proposal configuration415to further optimize for the specific variable. This life cycle iteration of the deployed proposal configuration415to generate next generations of proposal configurations415is described in further detail below with respect to feedback loop345.

In embodiments where a deployed proposal configuration415optimizes the existing infrastructure by at least two specific variables, model engine435may apply optimization weights442to each of the specific variables optimized for in the deployed proposal configuration415(seeFIG.4). Optimization weights442may be a scaled weight ranging from 0 to 1, or from 0% to 100%. By applying an optimization weight442to a specific variable optimized for in the deployed proposal configuration415, model engine435may adjust IAC of the deployed proposal configuration415to place an emphasis on optimizing one specific variable over the other. In some embodiments, an administrator or user may specify optimization weights442for a plurality of specific variables. In other embodiments, optimization weights442may be predetermined and stored in instructions234for performing optimization processing stored in computer readable medium225(seeFIG.2).

Referring toFIG.3, after applying any optimization weights442to specific variables, model engine435may execute step340to verify that the deployed proposal configuration415meets all required thresholds for compliance, as specified by compliance rules444(seeFIG.4). Compliance rules444may be a binary weight of 0 or 1 that is applied to IAC of the proposal configuration415based on whether the proposal configuration415complies with all current rules of cloud environment100. In some embodiments, rules of cloud environment100may include abiding by global/environmental regulations and firewalls of the cloud environment. Model engine435will apply compliance rules444to override any user preferences defined in optimization weights442. For example, a user may wish to optimize a proposal configuration415at 100% for cost and 0% for security. However, such a proposal configuration would be in violation of a regulation of the cloud environment requiring all proposal configurations to be optimized at least 20% for security. In this scenario, model engine435will apply a binary weight of 0 to that proposal configuration415to signify that the proposal configuration415failed to comply with rules of cloud environment100. As a result, the non-compliant proposal configuration415cannot be deployed into cloud environment100. It should be understood by those skilled in the art that compliance rules of cloud environment100may change over time such that the deployed proposal configuration415may be compliant with compliance rules444at one time but no longer compliant with compliance rules444at a later time. This process of continually checking for compliance of the proposal configurations415with rules of cloud environment100is completed automatically and without user intervention, thereby eliminating human error, oversight, and inefficiency. Furthermore, an automatic check for compliance of the proposal configurations415with rules of cloud environment100allows for easy migration of existing infrastructure to other cloud platforms offering a specific application or service to client devices106.

Referring toFIG.3, at loop345, method300repeats steps310-340after executing step340. Specifically, after model engine435adjusts IAC of the deployed proposal configuration to further optimize for the specific variable in step335and verifies that the deployed proposal configuration meets all required thresholds for compliance in step340, method300feedbacks the IAC of the adjusted and compliant proposal configuration to generator410. In this embodiment, generator410generates the plurality of proposal configurations415in step310using the adjusted and compliant proposal configuration as the existing infrastructure configuration rather than using the scanned existing infrastructure configuration as identified by scanner405in step305. Method300then proceeds with steps315-340, as described above. In other words, with reference toFIG.4, the adjusted proposal configuration received from model engine435becomes the “as-is” IAC model of “Proposal A” in subsequent iterations of method300via loop345. Accordingly, all other proposal configurations B-X are generated based on the “as-is” IAC model of the adjusted proposal configuration to optimize for various specific variables, as already described above. By generating the plurality of proposal configurations415based on the adjusted and compliant proposal configuration received from model engine435, method300ensures further optimization and compliance of the IT system infrastructure in cloud environment100through multiple life cycle iterations.

FIG.5illustrates an exemplary computer system capable of implementing the method for optimizing IT system infrastructure configurations according to one embodiment of the present disclosure.

Various embodiments may be implemented, for example, using one or more well-known computer systems, such as a computer system500, as shown inFIG.5. One or more computer systems500may be used, for example, to implement any of the embodiments discussed herein, as well as combinations and sub-combinations thereof. The computer system500may be used to implement method300, server110, resources105, client devices106, and cloud environment100, as described above with reference toFIGS.1-4.

The computer system500may include one or more processors (also called central processing units, or CPUs), such as a processor504. The processor504may be connected to a communication infrastructure or bus506.

The computer system500may also include user input/output device(s)503, such as monitors, keyboards, pointing devices, etc., which may communicate with communication infrastructure506through user input/output interface(s)502.

The computer system500may also include a main or primary memory508, such as random access memory (RAM). Main memory508may include one or more levels of cache. Main memory508may have stored therein control logic (i.e., computer software) and/or data.

The computer system500may also include one or more secondary storage devices or memory510. The secondary memory510may include, for example, a hard disk drive512and/or a removable storage device or drive514. The removable storage drive514may be a floppy disk drive, a magnetic tape drive, a compact disk drive, an optical storage device, tape backup device, and/or any other storage device/drive.

The removable storage drive514may interact with a removable storage unit518. The removable storage unit518may include a computer usable or readable storage device having stored thereon computer software (control logic) and/or data. The removable storage unit518may be a floppy disk, magnetic tape, compact disk, DVD, optical storage disk, and/any other computer data storage device. The removable storage drive514may read from and/or write to the removable storage unit518.

The computer system500may further include a communication or network interface524. The communication interface524may enable the computer system500to communicate and interact with any combination of external devices, external networks, external entities, etc. (individually and collectively referenced by reference number528). For example, the communication interface524may allow the computer system500to communicate with the external or remote devices528over communications path526, 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 the computer system500via the communication path526.

In accordance with 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, the computer system500, the main memory508, the secondary memory510, and the removable storage units518and522, 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 the computer system500), may cause such data processing devices to operate as described herein.

The claims in the instant application are different than those of the parent application or other related applications. The Applicant, therefore, rescinds any disclaimer of claim scope made in the parent application or any predecessor application in relation to the instant application. The Examiner is therefore advised that any such previous disclaimer and the cited references that it was made to avoid, may need to be revisited. Further, the Examiner is also reminded that any disclaimer made in the instant application should not be read into or against the parent application.