Progressively implementing declarative models in distributed systems

A system for automatically implementing high-level instructions in a distributed application program, where the high-level instructions reflect the behavior of the distributed application program, includes at least a tools component. The tools component is used to write high-level instructions in the form of declarative models, and place them in a repository. An executive component then receives the declarative models from the repository and refines them (e.g., via progressive elaboration) until there are no ambiguities. A platform-specific driver then translates the commands from the executive component, effectively turning the declarative model instructions into a set of imperative actions to be implemented in one or more application containers. The platform-specific driver also relays one or more event streams to an analytics means, which can result in modifications to the declarative models and corresponding new sets of instructions coming through the platform-specific driver at a later point.

CROSS-REFERENCE TO RELATED APPLICATIONS

BACKGROUND

Background and Relevant Art

As computerized systems have increased in popularity, so have the complexity of the software and hardware employed within such systems. In general, the need for seemingly more complex software continues to grow, which further tends to be one of the forces that push greater development of hardware. For example, if application programs require too much of a given hardware system, the hardware system can operate inefficiently, or otherwise be unable to process the application program at all. Recent trends in application program development, however, have removed many of these types of hardware constraints at least in part using distributed application programs. In general, distributed application programs comprise components that are executed over several different hardware components, often on different computer systems in a tiered environment.

With distributed application programs, the different computer systems may communicate various processing results to each other over a network. Along these lines, an organization will employ a distributed application server to manage several different distributed application programs over many different computer systems. For example, a user might employ one distributed application server to manage the operations of an ecommerce application program that is executed on one set of different computer systems. The user might also use the distributed application server to manage execution of customer management application programs on the same or even a different set of computer systems.

Of course, each corresponding distributed application managed through the distributed application server can, in turn, have several different modules and components that are executed on still other different computer systems. One can appreciate, therefore, that while this ability to combine processing power through several different computer systems can be an advantage, there are other disadvantages to such a wide distribution of application program modules. For example, organizations typically expect a distributed application server to run distributed applications optimally on the available resources, and take into account changing demand patterns and resource availability.

Unfortunately, conventional distributed application servers are typically ill-equipped (or not equipped at all) to automatically handle and manage all of the different problems that can occur for each given module of a distributed application program. For example, a user may have an online store application program that is routinely swamped with orders whenever there is a promotion, or during the same holidays each year. In some cases, the user might expect the distributed application server to analyze and anticipate these fluctuating demands on various components or modules of the given distributed application program.

In particular, the organization might expect the distributed application server to swap around various resources so that high-demand processes can be handled by software and hardware components on other systems that may be less busy. Such accommodations, however, can be difficult if not impossible to do with conventional distributed application server platforms. Specifically, most conventional distributed application server platforms are ill-equipped or otherwise unable to identify and properly manage different demand patterns between components of a distributed application program. This may be due at least partly to the complexity in managing application programs that can have many distributed components and subsystems, many of which are long-running workflows, and/or otherwise legacy or external systems.

In addition, conventional distributed application program servers are generally not configured for efficient scalability. For example, most distributed application servers are configured to manage precise instructions of the given distributed application program, such as precise reference and/or component addressing schemes. That is, there is often little or no “loose coupling” between components of an application program. Thus, when an administrator of the server desires to redeploy certain modules or components onto another server or set of computer systems, there is an enhanced potential of errors particularly where a large number of different computer systems and/or modules may be involved. This potential for errors can be realized when some of the new module or component references are not passed onward everywhere they are needed, or if they are passed onward incorrectly.

One aspect of distributed application programs that can further enhance this potential for error is the notion that the distributed application server may be managing several different distributed application programs, each of which executes on a different platform. That is, the distributed application server may need to translate different instructions for each different platform before the corresponding distributed application program may be able to accept and implement the change. Due to these and other complications, distributed application programs tend to be fairly sensitive to demand spikes.

This sensitivity to demand spikes can mean that various distributed application program modules may continue to operate at a sub-optimum level for a long period of time before the error can be detected. In some cases, the administrator for the distributed application server may not even take corrective action since attempting to do so could result in an even greater number of errors. As a result, a distributed application program module could potentially become stuck in a pattern of inefficient operation, such as continually rebooting itself, without ever getting corrected during the lifetime of the distributed application program. Accordingly, there are a number of difficulties with management of current distributed application programs and distributed application program servers that can be addressed.

BRIEF SUMMARY

Implementations of the present invention provide systems, methods, and computer program products configured to automatically implement operations of distributed application programs through a distributed application program server. In at least one implementation, for example, a distributed application program server comprises a set of implementation means and a set of analytics means. Through a platform-specific driver for each given module of a distributed application program, the implementation means deploy sets of high-level instructions, or declarative models, to create a given distributed application program module on the respective platform, while the analytics means automatically monitor and adjust the declarative models, as needed. This loose coupling through the declarative models of server components to the distributed application program and automatic monitoring and adjustment can allow the server to better manage demand, resource, or usage spikes, and/or other forms of distributed application program behavior fluctuations.

Accordingly, a method of automatically implementing one or more sets of high-level instructions in a distributed application program during execution using declarative models can involve identifying one or more modifications to corresponding one or more declarative models in a repository. The one or more declarative models include high-level instructions regarding one or more operations of a distributed application program. The method can also involve refining the one or more declarative models to include contextual information regarding operations of the distributed application program. In addition, the method can involve translating the one or more refined declarative models into one or more commands to be implemented by the container of the distributed application program. Furthermore, the method can involve sending the translated commands to one or more application containers. The translated commands are then received by the container and used to determine and configure behavior of the distributed application program in that container.

In addition, an additional or alternative method of automatically implementing one or more sets of high-level instructions in a distributed application program during execution using declarative models can involve receiving a set of new one or more declarative models from a repository. The new one or more declarative models include high-level instructions regarding operations of a distributed application program. The method can also involve implementing the new one or more new declarative models through an implementation means and one or more application containers. As a result, a first set of low-level commands are prepared and sent to the one or more application containers to be executed.

In addition, the method can involve identifying a change in the new one or more declarative models via one or more analytics means. The change reflects performance information for the distributed application program that is received from the one or more application containers. Furthermore, the method can involve implementing an updated version of the one or more declarative models through the implementation means and the one or more application containers. As such, a second set of low-level commands are prepared and sent to the one or more application containers to be executed based on the changes to the one or more new declarative models.

DETAILED DESCRIPTION

Implementations of the present invention extend to systems, methods, and computer program products configured to automatically implement operations of distributed application programs through a distributed application program server. In at least one implementation, for example, a distributed application program server comprises a set of implementation means and a set of analytics means. Through a platform-specific driver for each given module of a distributed application program, the implementation means deploy sets of high-level instructions, or declarative models, to create a given distributed application program module on the respective platform, while the analytics means automatically monitor and adjust the declarative models, as needed. This loose coupling through the declarative models of server components to the distributed application program and automatic monitoring and adjustment can allow the server to better manage demand, resource, or usage spikes, and/or other forms of distributed application program behavior fluctuations.

Accordingly, one will appreciate from the following specification and claims that implementations of the present invention can provide a number of different advantages to managing distributed application programs. This is at least partly due to the ease of implementing high-level instructions, such as those created by a program developer, as low-level instructions (e.g., executable commands) that can be executed by distributed application containers that configure and manage distributed application modules on a platform-specific basis. For example, implementations of the present invention provide mechanisms for writing a declarative model, detecting changes to a declarative model, and scheduling an appropriate model refinement process so that refined declarative model instructions can be translated.

Further implementations provide mechanisms for translating the refined model into instructions/commands that are ultimately executed. Accordingly, one will appreciate that these and other features can significantly ease and normalize management of a distributed application program server managing one or multiple different distributed application programs, potentially on several different platforms. In particular, the server administrator can easily configure a wide range of distributed application operations without necessarily needing to understand all the configuration particulars of the given run-time environments, and/or the specific implementation platforms of the given distributed application program.

Referring now to the Figures,FIG. 1Aillustrates an overview schematic diagram of at least one implementation of the present invention in which a distributed application server in a distributed computerized environment/system100is used to implement high-level instructions in one or more different distributed application programs107on an ongoing, automatic basis. In particular,FIG. 1Ashows a system distributed system100comprising an implementation means105and an analytics means110. In general, implementation means105and analytics means110comprise one or more sets of generalized computer-executable components that can be used within one or more distributed application program servers. These generalized computer-executable components, in turn, are configured to manage one or more different distributed application programs107in one or more application containers135.

For example,FIG. 1Ashows that, in at least one implementation, implementation means105can comprise a tools component125. In general, tools component125comprises one or more sets of computer-executable programs that can be used by a program developer or a server administrator to create one or more declarative models153. For example, a user (e.g., distributed application program developer) can use one or more developer's tools (e.g.,125), to create a declarative model153. As a preliminary matter, one will appreciate that any reference herein to any platform (or otherwise operating system)-specific component or module/program is made purely by way of convenience in explanation. Specifically, any reference herein to any component, module or application-specific feature will be understood as capable of being applied in a wide range of operating environments, systems, and/or platforms.

In any event, and as previously mentioned, declarative models153include one or more sets of high-level instructions regarding operations of a particular distributed application program107. These high-level instructions generally describe a particular intent for operation/behavior of one or more modules in the distributed application program, but do not necessarily describe steps required to implement the particular operations/behaviors. For example, a declarative model153can include such information as on what computer systems a particular module should run, as well as the characteristics of a computer system that should be allowed to run the particular module (e.g., processing speed, storage capacity, etc.).

Although the declarative model153could ultimately include such specific information as the Uniform Resource Identifier (URI) address of a particular endpoint, the initial creation of any declarative model (e.g.,153) will usually result in a document which will more likely include generalized information. Such generalized information might include a domain name where a module can be executed, different permissions sets that can be associated with execution of the module, whether or not certain components should connect at all, etc. For example, a declarative model153may describe the intent of having one web service connect to another web service.

When ultimately interpreted and/or translated, these generalized intent instructions can result in very specific instructions/commands, depending on the platform or operating environment. For example, the declarative model153could include instructions so that, when interpreted, a web service deployed into one datacenter may be configured to use a TCP transport if one other web service is nearby. The instructions could also include instructions that tell the deployed web service to alternatively use an Internet relay connection if the other web service is outside of the firewall (i.e., not nearby).

Although indicating a preference for connection of some sort, the declarative model (e.g., a “declarative application model”) (153) will typically leave the choice of connection protocol to a model interpreter. In particular, a declarative model creator (e.g., tools component125) might indicate a preference for connections in the declarative model153generally, while the declarative model interpreter (e.g., executive component115and/or platform-specific driver130) can be configured to select different communication transports depending on where specific modules are deployed. For example, the model interpreter (e.g., executive component115and/or platform-specific driver130) may prepare more specific instructions to differentiate the connection between modules when on the same machine, in a cluster, or connected over the Internet.

Similarly, another declarative model (e.g., a “declarative policy model”) (153) may describe operational features based more on end use policies. For example, a declarative policy model used with a distributed financial application program may dictate that no more than 100 trade requests in a second may be sent over a connection to a brokerage firm. A policy model interpreter (e.g., executive component115and/or platform-specific driver130), however, can be configured to choose an appropriate strategy, such as queuing excessive requests to implement the described intent.

In any case,FIG. 1Ashows that, upon creating a particular declarative model153, the tools component125then passes (e.g., writes) the declarative model153into repository120. In at least one implementation, anytime repository120receives any kind of modification to a declarative model153(e.g., new writes, or modifications from analytics means110), executive component115will detect this. For example, the repository120can send one or more updates or alerts to the executive component115. In additional or alternative implementations, however, executive component115may be configured to identify any such modifications, creations, or changes by synchronizing with repository120on some particular schedule, or even a continuous basis.

In either case, executive component115ultimately identifies, receives and refines the declarative models153(and/or changes thereto) in repository120so that they can be translated by the platform-specific driver130. In general, “refining” a declarative model153includes adding or modifying any of the information contained in a declarative model so that the declarative model instructions are sufficiently complete for translation by platform-specific driver130. Since the declarative models153can be written relatively loosely by a human user (i.e., containing generalized intent instructions or requests), there may be different degrees or extents to which an executive component will need to modify or supplement a declarative model.

Along these lines,FIG. 1Billustrates additional details regarding the refinement and translation process as performed via implementation means105. In particular,FIG. 1Billustrates a number of additional processes that can occur pursuant to implementing the various declarative models153ultimately as low-level instructions. To this end,FIG. 1Bshows that executive component115comprises one or more different components that can be used to refine declarative model153using a progressive elaboration techniques.

For example,FIG. 1Ashows that executive component115comprises a schedule processing component117and a refining component119. In general, the schedule processing component117is that which enables the executive component115to identify changes in the repository to any declarative models. For example, the schedule processing component117can comprise one or more interfaces for receiving communication from a corresponding interface built into repository120. Additionally, or alternatively, schedule processing component117comprises one or more sets of executable instructions for synchronizing declarative model153data within the repository.

Upon detecting any changes (whether new declarative models or updates thereto), executive component115then begins the process of progressive elaboration on any such identified declarative model (or modification). In general, progressive elaboration involves refining a particular declarative model153(i.e., adding or modifying data) until there are no ambiguities, and until details are sufficient for the platform-specific drivers130to consume/translate them. The executive component115performs progressive elaboration at least in part using refining component119, which “refines” the declarative model153data.

In at least one implementation, executive component115implements this progressive elaboration or “refining” process as a workflow that uses a set of activities from a particular library (not shown). In one implementation, the executive component115also provides the library in advance, and specifically for the purposes of working on declarative models. Some example activities that might be used in this particular workflow can include “read model data,” “write model data,” “find driver,” “call driver,” or the like. The actions associated with these or other types of calls are described more fully below as implemented by the refining component119portion of executive component115.

Specifically, in at least one implementation, the refining component119refines a declarative model153(or update thereto). The refining component119typically refines a declarative model153by adding information based on knowledge of dependencies (and corresponding semantics) between elements in the declarative model153(e.g. one web service connected to another). The refining component119can also refine the declarative model153by adding some forms of contextual awareness, such as by adding information about the available inventory of application containers135for deploying a distributed application program107. In addition, the refining component119can be configured to fill-in missing data regarding computer system assignments.

For example, refining component119might identify a number of different modules that will be used to implement a declarative model153, where the two modules have no requirement for specific computer system addresses or operating requirements. The refining component119might thus assign the distributed application program107modules to available computer systems arranged by appropriate distributed application program containers135, and correspondingly record that machine information in the refined declarative model153a(or segment thereof). Along these lines, the refining component119can reason about the best way to fill-in data in a refined declarative model153. For example, as previously described, refining component119of executive component115may determine and decide which transport to use for an endpoint based on proximity of connection, or determine and decide how to allocate distributed application program modules based on factors appropriate for handling expected spikes in demand.

In additional or alternative implementations, the refining component119can compute dependent data in the declarative model153. For example, the refining component119may compute dependent data based on an assignment of distributed application program modules to machines. Along these lines, the refining component119may also calculate URI addresses on the endpoints, and propagate the corresponding URI addresses from provider endpoints to consumer endpoints. In addition, the refining component119may evaluate constraints in the declarative model153. For example, the refining component119can be configured to check to see if two distributed application program modules can actually be assigned to the same machine, and if not, the refining component119can refine the declarative model153ato correct it.

After adding all appropriate data to (or otherwise modifying/refining) the given declarative model153(to create model153a), the refining component119can finalize the refined declarative model153aso that it can be translated by platform-specific drivers130. To finalize or complete the refined declarative model153a,refining component119might, for example, partition declarative model153into segments that can be targeted by any one or more platform-specific drivers130. To this end, the refining component119might tag each declarative model153a(or segment thereof) with its target driver (e.g., the address of platform-specific driver130). Furthermore, the refining component119can verify that the declarative model153acan actually be translated by the platform-specific drivers130, and, if so, pass the refined declarative model153a(or segment thereof) to the particular platform-specific driver130for translation.

In any case,FIG. 1Bshows that the platform-specific driver130translates these instructions through translation component131. In general, translation component translates the refined declarative models153a(and/or segment thereof) into sets of one or more platform-specific instructions/commands133. For example,FIG. 1Bshows that the platform-specific driver130might create a set of imperative instructions/commands133that can be executed in a particular operating system or operating environment, and/or will be understood by a specific application container135. In one implementation, translation of a refined declarative model153acan result in the creation of files, adding virtual directories, writing settings into configuration files, or the like.

Whatever actions performed by the translation component131will be tailored for the specific platform or operating environment. In particular, the platform-specific driver (e.g., via translation component131) can translate the refined declarative models according to in-depth, platform-specific configuration knowledge of a given platform/operating environment corresponding to the one or more application containers135(e.g., version of the operating system they run under) and container implementation technologies. With respect to a MICROSOFT WINDOWS operating environment, for example, some container implementation technologies might include “IIS”—Internet Information Service, or a WINDOWS ACTIVATION SERVICE used to host a “WCF”—WINDOWS Communication Foundation—service module). (As previously mentioned, however, any specific reference to any WINDOWS or MICROSOFT components, modules, platforms, or programs is by way only of example.)

As a result, the generalized or supplemented instructions placed into the declarative models by the tools component125and/or refining component119ultimately direct operational reality of one or more distributed application programs107in one or more application containers135. In particular, the one or more distributed application containers135execute the declarative models153by executing the instructions/commands133received from the platform-specific driver130. To this end, the distributed application containers135might replace or update any prior modules have been replaced or revised with a new declarative model153. In addition, the distributed application containers135execute the most recent version of modules and/or components, such as normally done, including those described in the new instructions/commands133, and on any number of different computer systems.

In addition to the foregoing, the distributed application programs107can provide various operational information about execution and performance back through the implementation means105. For example, implementations of the present invention provide for the distributed application program107to send back one or more event streams137regarding various execution or performance indicators back through platform-specific driver130. In one implementation, the distributed application program107may send out the event streams137on a continuous, ongoing basis, while, in other implementations, the distributed application program107sends the event streams on a scheduled basis (e.g., based on a scheduled request from driver130). The platform-specific drivers130, in turn, pass the one or more event streams137to analytics means110for analysis, tuning, and/or other appropriate modifications.

In particular, and as will be understood more fully herein, the analytics means110aggregate, correlate, and otherwise filter the relevant data to identify interesting trends and behaviors of the various distributed application programs107. The analytics means110can also modify corresponding declarative models153as appropriate for the identified trends. For example, the analytics means110may modify declarative models153to create a new or otherwise modified declarative model153bthat reflects a change in intent, such as to overcome a problem identified in event streams137. In particular, the modified declarative model153bmight be configured so that a given module of a distributed application program can be redeployed on another machine if the currently assigned machine is rebooting too frequently.

The modified declarative model153bis then passed back into repository120. As previously mentioned, executive component115will identify the new declarative model153b(or modification to a prior declarative model153) and begin the corresponding refining process. Specifically, executive component will use refining component119to add any necessary data to modified declarative model153bto create refined, modified declarative model, such as previously described. The newly refined, albeit modified declarative model153bis then passed to platform-specific driver130, where it is translated and passed to the appropriate application containers135for processing.

Accordingly,FIGS. 1A-1B(and the corresponding text) provide a number of different schematics, components, and mechanisms for automatically implementing high-level instructions within distributed application programs. As previously described, this can all be done without necessarily requiring intimate knowledge by a server administrator of the distributed application programs and their containers.

In addition to the foregoing, implementations of the present invention can also be described in terms of one or more flow charts of methods having a series of acts and/or steps for accomplishing a particular result. For example,FIGS. 2 and 3illustrate additional or alternative methods from the perspective of a server for automatically implementing one or more sets of high-level instructions in a distributed application program. The acts and/or steps ofFIGS. 2 and 3are described more fully below with respect to the components, schematics, and corresponding text ofFIGS. 1A and 1B.

For example,FIG. 2shows that a method from the perspective of a server of automatically implementing one or more sets of high-level instructions in a distributed application program during execution can comprise an act200of identifying one or more declarative models. Act200includes identifying one or more modifications to corresponding one or more declarative models into a repository, the one or more declarative models including high-level instructions regarding one or more operations of a distributed application program. For example, as shown inFIGS. 1A and 1B, tools component125can be used to create and/or pass declarative models153into repository120. Executive component115(e.g., via schedule processing component117) receives the declarative models153(or corresponding updates thereto) and begins processing.

FIG. 2also shows a method from the perspective of the server can comprise an act210of refining the declarative models with contextual information. Act210includes, refining the identified one or more declarative models to include contextual information regarding operations of the distributed application program. For example, executive component115can perform any number of actions, such as filling in missing data in a declarative model, deciding which transport to use in connection between modules, computing dependent data in a declarative model, evaluating constraints in a declarative model, and so forth.

In addition,FIG. 2shows that the method from the perspective of the server can comprise an act220of translating the refined declarative models. Act220includes translating the one or more refined declarative models into one or more commands to be implemented by the distributed application program. For example, implementation means105inFIGS. 1A and 1Bincludes a platform-specific driver component130that receives instructions corresponding to refined declarative model153aand translates that refined declarative model153athrough translation component131to send a set of instructions/commands133to one or more application containers135.

Furthermore,FIG. 2shows that the method from the perspective of the server can comprise an act230of sending the translated commands to an application container. Act230includes, sending the translated commands to one or more application containers, wherein the translated commands are received and implemented. For example, as show inFIGS. 1A and 1B, upon translating the instructions to create platform-specific instructions/commands133, platform-specific driver130prepares (e.g., via translation component131) and sends these commands to the one or more application containers135, whereupon they are executed in order to configure and manage distributed application programs.

In addition to the foregoing,FIG. 3shows that an additional or alternative method from the perspective of the server of automatically implementing one or more sets of high-level instructions can comprise an act300of receiving a set of new declarative models. Act300includes receiving a set of new one or more declarative models from a repository, the new one or more declarative models including high-level instructions regarding operations of a distributed application program. For example, executive component115receives declarative models113through repository120. These declarative models113can come from tools component125if they are new or may alternatively come via analytics means110, such as if they are modified in response to information in event streams137.

FIG. 3also shows the method from the perspective of the server can comprise an act310of implementing the new declarative models through an application container. Act310includes implementing the new one or more declarative models through an implementation means in one or more application containers, wherein a first set of low-level commands are prepared and sent to one or more application containers to be executed. For example,FIGS. 1A and 1Bshow that the executive component115, such as via scheduling processing component117, and refining component119, prepares a set of refined declarative model153ainformation. A platform-specific driver130then translates the information into specific sets of instructions/commands133. These specific sets of instructions/commands133then configure and control behavior of the distributed application program(s)107through the execution in the respective application containers135.

In addition,FIG. 3shows a method from the perspective of the server can comprise an act320of identifying a change in the declarative models. Act320includes identifying a change in the new one or more declarative models via one or more analytics means, the change reflecting performance information for the distributed application program that is received from the one or more application containers. For example,FIG. 1Bshows that application container135sends performance information140back through platform-specific driver130of the implementation means105. This information is then passed on to the analytics means110, which, if appropriate, can change or update the declarative models113to accommodate any performance issues. As previously discussed, the event streams might identify that a server (or module on a server) is rebooting too frequently, and so analytics means might create a modification to the declarative model (or153b) that identifies an intent to redeploy the module onto another server.

Furthermore,FIG. 3shows that a method from the perspective of the server can comprise an act330of implementing an updated version of the declarative models through the application container. Act330includes implementing an updated version of the one or more declarative models through the implementation means and the one or more application containers, wherein a second set of low-level commands are prepared and sent to the one or more application containers to be executed based on the changes to the one or more new declarative models. For example,FIG. 1Bshows that implementation means105can receive a modified declarative model153b.As with model153, declarative model153bcan then be detected and refined through executive component115, which then passes the refined, modified declarative model153binstructions to platform-specific driver130for translation. As before, the distributed application container(s)135then execute the new corresponding instructions/commands corresponding to refined, modified declarative model153bto reconfigure the distributed application programs in their respective application containers.

Accordingly,FIGS. 1A through 3provide a number of schematics, components, and mechanisms for automatically implementing high-level instructions at the server level that are ultimately implemented as low-level instructions through an application container. As described herein, these and other advantages can enable a server administrator to continually and automatically adjust distributed application program operations without necessarily requiring intimate knowledge of the platform requirements and rules of a particular distributed application program. As such, implementations of the present invention are highly scalable across distributed systems, and relatively simple to manage.

The embodiments of the present invention may comprise a special purpose or general-purpose computer including various computer hardware, as discussed in greater detail below. Embodiments within the scope of the present invention also include computer-readable media for carrying or having computer-executable instructions or data structures stored thereon. Such computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer.

By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a computer, the computer properly views the connection as a computer-readable medium. Thus, any such connection is properly termed a computer-readable medium. Combinations of the above should also be included within the scope of computer-readable media.