Forming microservices from monolithic applications

A method, system, and computer program product for decomposing monolithic applications to form microservices are provided. The method identifies a set of classes within a monolithic application. A set of horizontal clusters are generated by performing horizontal clustering to the set of classes to decompose the classes based on a first functionality type. The method generates a set of vertical clusters by performing vertical clustering to the set of classes to decompose the classes based on a second functionality type. A subset of classes occurring in a common horizontal cluster and vertical cluster are identified as a functional unit. The method merges one or more functional units to form a microservice.

BACKGROUND

Microservices are an architectural style of structuring an application as a collection of services. These collections of services may be maintainable and testable, loosely coupled, and independently deployable. Collections of services may be organized around common capabilities. A monolithic application is an architectural style for single-tiered software applications. Monolithic applications are often self-contained and independent from other computing applications. Monolithic applications often lack modularity. Monolithic applications are a single unified unit, while microservices break down similar functionality into smaller units which may operate independently. Each process of an application using a microservice architecture acts as a separate service, while monolithic applications are often deployed on identical servers and use load balancing to offer simplified deployment and horizontal scaling. Microservices enable individual development of services, reduce barriers to adopting or adapting new technologies, and allow for independent scaling of services.

SUMMARY

According to an embodiment described herein, a computer-implemented method for decomposing monolithic applications to form microservices is provided. The method identifies a set of classes within a monolithic application. A set of horizontal clusters are generated by performing horizontal clustering to the set of classes to decompose the classes based on a first functionality type. The method generates a set of vertical clusters by performing vertical clustering to the set of classes to decompose the classes based on a second functionality type. A subset of classes occurring in a common horizontal cluster and vertical cluster are identified as a functional unit. The method merges one or more functional units to form a microservice.

According to an embodiment described herein, a system for decomposing monolithic applications to form microservices is provided. The system includes one or more processors and a computer-readable storage medium, coupled to the one or more processors, storing program instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations identify a set of classes within a monolithic application. A set of horizontal clusters are generated by performing horizontal clustering to the set of classes to decompose the classes based on a first functionality type. The operations generate a set of vertical clusters by performing vertical clustering to the set of classes to decompose the classes based on a second functionality type. A subset of classes occurring in a common horizontal cluster and vertical cluster are identified as a functional unit. The operations merge one or more functional units to form a microservice.

According to an embodiment described herein, a computer program product for decomposing monolithic applications to form microservices is provided. The computer program product includes a computer-readable storage medium having program instructions embodied therewith, the program instructions being executable by one or more processors to cause the one or more processors to identify a set of classes within a monolithic application. A set of horizontal clusters are generated by performing horizontal clustering to the set of classes to decompose the classes based on a first functionality type. The computer program product generates a set of vertical clusters by performing vertical clustering to the set of classes to decompose the classes based on a second functionality type. A subset of classes occurring in a common horizontal cluster and vertical cluster are identified as a functional unit. The computer program product merges one or more functional units to form a microservice.

DETAILED DESCRIPTION

The present disclosure relates generally to methods for identifying and forming microservices. More particularly, but not exclusively, embodiments of the present disclosure relate to a computer-implemented method for decomposing monolithic applications to form microservices. The present disclosure relates further to a related system for identifying and forming microservices, and a computer program product for operating such a system.

Applications may be used by organizations for relatively long periods of time, even as other applications are adopted, changed, or retired. As applications are used over time, modernization may be considered to maintain usefulness of the application. Identifying microservices within an application may be part of the modernization and maintenance of the application. Some systems identify microservices without considering an architectural pattern of the application. Further, some systems do not consider relationships of classes in identifying microservices. Some systems consider relations between classes with respect to context windows, without consideration of logical functions of classes.

Embodiments of the present disclosure decompose monolithic applications into microservices. The monolithic applications may be decomposed using orthogonal clustering. Some embodiments of the present disclosure use horizontal clustering to decompose monolithic applications. Horizontal clustering may decompose classes based on functionalities, such as business functionalities. Some embodiments of the present disclosure use vertical clustering to decompose monolithic applications. Vertical clustering may decompose classes based on functionalities, such as logical functionalities. Some embodiments of the present disclosure use a combination of horizontal clustering and vertical clustering to decompose monolithic applications. Hierarchical clustering may be used to decompose application classes into different partitions. Clustering methods used in the present disclosure may use static code or runtime traces as input. Embodiments of the present disclosure identify classes into functional units and merge functional units into microservices. The functional units may be merged according to a set of rules, for example rules considering call invocations. Embodiments of the present disclosure enable identification of microservice candidates with suitable accuracy and maintainability including complexity, cohesion, and coupling. Further, embodiments of the present disclosure may enable groupings of classes to inform application modernization decisions.

Some embodiments of the concepts described herein may take the form of a system or a computer program product. For example, a computer program product may store program instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations described above with respect to the computer-implemented method. By way of further example, the system may comprise components, such as processors and computer-readable storage media. The computer-readable storage media may interact with other components of the system to cause the system to execute program instructions comprising operations of the computer-implemented method, described herein. For the purpose of this description, a computer-usable or computer-readable medium may be any apparatus that may contain means for storing, communicating, propagating, or transporting the program for use, by, or in connection with, the instruction execution system, apparatus, or device.

Referring now toFIG. 1, a block diagram of an example computing environment100is shown. The present disclosure may be implemented within the example computing environment100. In some embodiments, the computing environment100may be included within or embodied by a computer system, described below. The computing environment100may include a class-based microservice system102. The class-based microservice system102may comprise a class component110, a horizontal cluster component120, a vertical cluster component130, a unit component140, and a microservice component150. The class component110identifies classes within monolithic applications. The horizontal cluster component120generates horizontal clusters from sets of classes identified within monolithic applications. The vertical cluster component130generates vertical clusters from sets of classes identified within monolithic applications. The unit component140identifies classes occurring in common horizontal clusters and vertical clusters to identify functional units. The microservice component150merges functional units or classes within functional units into microservices. Although described with distinct components, it should be understood that, in at least some embodiments, components may be combined or divided, and/or additional components may be added without departing from the scope of the present disclosure.

Referring now toFIG. 2, a flow diagram of a computer-implemented method200is shown. The computer-implemented method200is a method for decomposing monolithic applications to form microservices. In some embodiments, the computer-implemented method200may be performed by one or more components of the computing environment100, as described in more detail below.

At operation210, the class component110identifies a set of classes within a monolithic application. In some embodiments, the class component110identifies the set of classes by examining source code of the monolithic application. The class component110may identify class names, attributes, characteristics, method names, method arguments, return types, class inputs, class outputs, class connections, combinations thereof, and any other suitable information from source code or associated application programming interface. In some embodiments, the class component110includes a python-based tool to insert code into the monolithic application. The inserted code may enable runtime trace generation. The class component110may also include a Java front-end tool. The front-end tool may enable generation of a trace based on a context of the class, such as a business context. In some embodiments, the class component110includes an extraction application. The extraction application may extract inheritance relationships, data dependency, attributes, method arguments, return types, combinations thereof, and other relationships among classes within the monolithic application.

In some embodiments, the class component110includes an architecture tool. The architecture tool analyzes an architecture pattern of the monolithic application. For example, the architecture tool may identify an n-tier architecture pattern within the monolithic application. The n-tier architecture pattern may include presentation, business logic, and data access layers. Each layer may define or represent a specific role that satisfies a particular business request. In some instances, the architecture tool may discern among classes, tables, and Java Server Pages (JSP) distributed among the layers of the architecture pattern within the monolithic application. The architecture tool may also determine complete paths of data, classes, or operations across layers of the architecture pattern. For example, the architecture tool may enable capture or identification of complete paths from the interface layer to the data access layer. These complete paths may be identified as causal paths. In some embodiments, the class component110the architecture tool identifies and considers direct calls and indirect calls to classes or methods to identify degrees of association between classes.

At operation220, the horizontal cluster component120generates a set of horizontal clusters of the set of classes. The horizontal clustering may decompose the classes based on a first functionality type. The horizontal cluster component120may receive runtime traces with business context labels or symbol tables from static code as input. Runtime traces may be reduced to generate a unique call path and passed to the horizontal cluster component120as input to compute a similarity matrix based on direct call relations and indirect call relations. From the direct call relations and the indirect call relations, the horizontal cluster component120may generate the set of horizontal clusters by performing horizontal clustering on the set of classes.

At operation230, the vertical cluster component130generates a set of vertical clusters of the set of classes. The vertical clustering may decompose the classes based on a second functionality type. The vertical cluster component130may receive runtime traces with business context labels or symbol tables from static code as input. Runtime traces may be reduced to generate a unique call path and passed to the vertical cluster component130as input to compute in-degree and out-degree of nodes. The in-degree and out-degree may be determined based on direct call relations and indirect call relations. The vertical cluster component130may compute a similarity matrix based on in-degree/out-degree ratio. The set of vertical clusters may then be generated by performing vertical clustering on the set of classes based on the similarity matrix.

At operation240, the unit component140identifies a subset of classes occurring in a common horizontal cluster and vertical cluster. The subset of classes may be identified as a functional unit. Classes which have been clustered into the same horizontal cluster and vertical cluster are identified as occurring in a common horizontal cluster and vertical cluster and are identified as a functional unit. For example, classes occurring in a first horizontal cluster and a first vertical cluster may be considered as occurring in a same or common horizontal cluster and vertical cluster. By way of further example, the first horizontal cluster and the first vertical cluster may be understood as intersecting clusters.

In some embodiments, the unit component140merges classes into functional units based on relations of the classes determined by the horizontal clustering and vertical clustering. For example, where a class is called by other classes belonging to a same functional unit, the class may be merged with the functional unit and the other classes. The decomposition may decompose legacy monolithic applications into functional units. Classes belonging to the same functional units serve similar business use cases and share similar logical responsibilities.

In some instances, the set of classes of the monolithic application may be divided according to orthogonal views, such as a grid providing horizontal and vertical layers with the classes of the monolithic application distributed among the horizontal and vertical layers. Horizontal clusters may be identified as horizontal layers within the orthogonal views. Vertical clusters may be identified as vertical layers within the orthogonal views. Where a horizontal cluster and a vertical cluster intersect, classes within the intersection may be considered to be classes occurring in a common or same horizontal cluster and vertical cluster. In some instances, for each horizontal cluster, vertical layers may be merged where there is a backward function call.

At operation250, the microservice component150merges one or more functional units to form a microservice. The microservice component150may merge functional units which are related above a relationship threshold. The microservice component150may merge functional units which have dependencies existing between classes contained in two or more different functional units. In some instances, the microservice component150analyzes functional units called by multiple other units. The microservice component150may determine relationships between a first functional unit which is called by a second functional unit and a third functional unit. The microservice component150may determine whether the relationship extends between the second functional unit and the third functional unit.

In some embodiments, the one or more functional units are merged to form a microservice based on one or more call invocations. In some embodiments, operations230,240, and250act as a set of rules to split or merge functional units according to patterns of invocation. The patterns of invocations may be identified from the one or more call invocations of the classes, the horizontal clusters, and the vertical clusters. The set of rules may ensure the merged functional units or microservices are maintainable based on complexity, cohesion, and coupling.

In some embodiments, the set of rules include merging functional units within each horizontal cluster to merge vertical layers if a reverse function call is detected by rules of an n-tier application. The set of rules may also include merging functional units if a class is called by other classes that belong to the same functional unit. Where such an instance occurs, a class is merged into the functional unit of the other classes to reduce coupling between functional units. The rules may also include merging functional units that are bi-directionally dependent on each other. Such merging may reduce coupling.

In some embodiments, the microservice component150presents the microservices (e.g., the orthogonal clustering results) in a graphical user interface. The graphical user interface may present the microservices, functional units within microservices, and classes clustered and merged into functional units. The graphical user interface may present the microservices, functional units, and classes within an expandable presentation, such as a graph. In some embodiments, the graphical user interface may present information in a nested or cascading format, such that selection of a microservice may expand into a presentation of one or more functional units within the selected microservice. Similarly, selection of a functional unit may expand into a presentation of a set of classes clustered and merged into a selected functional unit. In some instances, the graphical user interface may present the microservices, functional units, and classes in a searchable format, enabling exploration of the above-referenced information with varying levels of granularity.

In some instances, the graphical user interface presents the microservices in a selectable manner, such that a user may explore the microservices and the functional units and classes making up those microservices. The graphical user interface may present both vertical and horizontal clustering results. In such instances, users may interact with the graphical user interface to select between views of horizontal clusters (e.g., horizontal views) and vertical clusters (e.g., vertical views).

FIG. 3shows a flow diagram of an embodiment of a computer-implemented method300for decomposing monolithic applications to form microservices. The method300may be performed by or within the computing environment100. In some embodiments, the method300comprises or incorporates one or more operations of the method200. In some instances, operations of the method300may be incorporated as part of or sub-operations of the method200, such as operation220.

In operation310, the horizontal cluster component120determines an input mode for the horizontal clustering. The input mode may represent an input available for the horizontal cluster component120to generate clusters of classes. In some instances, input modes include static code and runtime traces. Static code may be source code of the monolithic application. Runtime traces capture execution events within the monolithic application, when the monolithic application is executed with specified or available input. The horizontal cluster component120may determine the input mode based on selection of a user within a graphical user interface. In some embodiments, the horizontal cluster component120determines the input mode based on accessibility of inputs for each input mode. For example, the horizontal cluster component120may determine if source code is available for the monolithic application, and select the static code input where the source code is available. In some instances, the horizontal cluster component120may determine the input mode based on a type of application or characteristics for the monolithic application.

In operation320, where the horizontal cluster component120determines an input mode of a static code, the horizontal cluster component120identifies static code within the monolithic application (e.g., source code). In some instances, the horizontal cluster component120identifies source code relating to classes within the monolithic application. In some instances the horizontal cluster component120determines classes based on an API associated with the monolithic application.

In operation330, the horizontal cluster component120assigns a first functionality type label to an application class. The horizontal cluster component120may assign the first functionality type label based on an information retrieval technique. The horizontal cluster component120may assign the first functionality type label based on a static analysis. The horizontal cluster component120may assign the first functionality type label based on the information retrieval technique and the static analysis. The information retrieval technique and the static analysis may be associated with the application class. The functionality type label may be a business context label, in some instances.

In operation340, the horizontal cluster component120simulates a set of call paths based on a static call graph. The static call graph may be a control flow graph representing calling relationships between classes. Each call path may be associated with a set of business use case labels. The horizontal cluster component120may simulate the set of call paths by static analysis.

In operation350, where the horizontal cluster component120determines an input mode of a runtime trace, the horizontal cluster component120collects a set of runtime traces for an application class. The set of runtime traces may be produced by running one or more test cases of the first functionality type. In some instances, the runtime traces are produced by running business functional test cases. The runtime traces may include a business context label indicating the business functional test case of the runtime trace. The runtime traces are collected as they run.

In operation360, the horizontal cluster component120generates a symbol table based on the set of runtime traces for the application class. The symbol table may be a data structure having identifies associated with information relating to a declaration or appearance of classes within the monolithic application. In some embodiments, the symbol table is generated from the collected runtime traces based on a static code base of the monolithic application. The symbol table may be generated on demand for the clustering process by the horizontal cluster component120or another component of the class-based microservice system102.

In operation370, the horizontal cluster component120generates a reduced call path from one or more original runtime traces. The reduced call path may be a reduced runtime path generated from original runtime traces. In some embodiments, the horizontal cluster component120generates the set of horizontal clusters by performing horizontal clustering on the reduced runtime path. The horizontal clustering may result in formation of groups of classes with coherent business functionalities. In some instances, the reduced call path is generated from the original runtime traces and the symbol table. The reduced call path may be reduced to generate a unique call path from the runtime trace.

The reduced call path may be a runtime path generation reduced by removing loops, removing returns, and separating forks. For example, where runtime paths are encountered including “t1, bcl, Root call A,” “t2, bcl, A call B,” “t3, bcl, B call C,” “t4, bcl, C returns B,” “t5, bcl, B returns A,” and “t6, bcl, A returns Root,” a runtime path may be reduced to a path of “Root →A→B→C.” By way of further example, where runtime paths are encountered including “t1, bcl, Root call A,” “t2, bcl, A call B,” “t3, bcl B call C,” “t4, bcl, C returns B,” “t5, bcl B call E,” “t6, bcl, E returns B,” “t7, bcl, B returns A,” and “t8, bcl, A returns Root,” a runtime path may be reduced to two paths of: Path1: “Root→A→B→C” and Path2: “Root→A→B→E.” The horizontal cluster component120may then call a stack push on one of the reduced paths and return a stack pop for the reduced path.

In operation380, the horizontal cluster component120generates the set of horizontal clusters. The horizontal cluster component120may generate the set of horizontal clusters from the set of classes based on the input of static code and operations320-340or based on the input of the runtime trace and operations350-370. In some embodiments, the horizontal cluster component120generates the set of horizontal clusters by calculating a similarity matrix of pairs of classes. The pairs of classes used for the similarity matrix may be pairs of classes based on direct call relations and/or indirect call relations. The horizontal cluster component120may apply a hierarchical clustering algorithm to generate one or more partitions for the horizontal clusters. The hierarchical clustering algorithm may group similar objects into clusters based on the similarity matrix. The hierarchical clustering algorithm may merge classes belonging to a same business use case. The merged classes may be initial clusters. In some instances, the horizontal cluster component120forms the horizontal clusters by repeatedly or iteratively calculating the similarity between pairs of initial clusters and merging pairs of clusters which are most similar.

To calculate similarity scores for a pair of initial clusters, the horizontal cluster component may employ Equation 1.

In Equation 1, S(cim, cjn) is a similarity score of two classes cimand cjn. “i” and “j” may represent clusters. The classes cimand cjnmay belong to cluster i and cluster j, respectively. |Ci| and |Cj| may represent a size of cluster i and cluster j, respectively.

In some embodiments, the similarity score of a pair of classes can be calculated based on relations between the classes. The relations may include direct call relations, indirect call relations, direct call relations patterns, and indirect call relations patterns. Direct call relations (DCR) may represent whether a call relation exists between two classes. Indirect call relations (ICR) may represent whether two classes appear in a same reduced runtime path. Direct call relations patterns (DCRP) represents whether classes have similar call relations with other classes. Indirect call relations patterns (ICRP) represents whether classes have similar co-occur relations with other classes. The similarity score between classes ciand cjmay be calculated based on the four relations using Equation 2.
S(ci,cj)=DCRij+ICRij+DCRPij+ICRPijEquation 2

In some embodiments, a direct call relation is measured as a ratio of a number of business use cases where there is a call relation between classes ciand cjto a number of business use cases associated with two classes. The DCR may be measured using Equation 3.

|Bci↔cj| represents a number of business use cases where cicalls a function which belongs to cjor cjcalls a function belonging to ci. The denominator of Equation 3 represents a total number of business use cases with which classes ciand cjare associated. In some instances, if ciparticipates in two business use cases {B1, B2}, cjparticipates in three business use cases {B1, B2, B3}, and there is a function call from cito cjassociated with B1, Equation 3 may determine the direct call relation for ciand cjas

In some embodiments, ICR co-occur relations are calculated as a ratio of a number of business use cases associated with a reduced runtime path where classes ciand cjco-occur to a number of business use cases associated with the two classes ciand cj. The ICR may be measured using Equation 4.

In Equation 4, |Bci↔cj| represents a number of business context uses associated with the reduced runtime paths where classes ciand cjco-occur.

In some embodiments, DCRP represents how similarly two classes interact with other classes. The similarity may be based on function calls. The DCRP may be calculated using Equation 5.

For example, tables 1 and 2 may depict an interaction pattern of classes c1and c2with other classes.

Tables 1 and 2 depict whether classes c1and c2have a call relation with other classes c3, c4, and c5. Classes c1and c2may have a direct edge with c3and c5in a business use case BC1. In such an instances, Equation 5 may provide values such that

CRPij=2(3×3).
In some embodiments, ICRP may be calculated in a manner similar to or the same as the manner for calculating DCRP.

FIG. 4shows a flow diagram of an embodiment of a computer-implemented method400for decomposing monolithic applications to form microservices. The method400may be performed by or within the computing environment100. In some embodiments, the method400comprises or incorporates one or more operations of the method200. In some instances, operations of the method400may be incorporated as part of or sub-operations of the method200, such as operation230.

In operation410, the vertical cluster component130determines an input mode for the vertical clustering. The input mode may be in the form of direct call relations and indirect call relations. In some embodiments, the vertical cluster component130may determine the input mode in a manner similar to or the same as described above with respect to operation310. After determining an input mode of static code or runtime traces, the vertical cluster component130may determine the relations of the classes. The vertical cluster component130may determine direct call relations and indirect call relations for each class within the monolithic application.

In operation420, where the vertical cluster component130determines an input mode of static code, the vertical cluster component130identifies static code within the monolithic application. The vertical cluster component130may identify the static code in a manner similar to or the same as described above with respect to operation320. The vertical cluster component130may also determine the direct and indirect call relations for the classes from the static code based on the source code of the monolithic application or an API associated with the monolithic application.

In operation430, the vertical cluster component130partitions the set of classes based on one or more class characteristics. The class characteristics may include one or more of a class name, a package structure, a source directory structure, and a development team allocation. The class characteristics may be determined based on the source code or the API associated with the monolithic application. In some instances, the vertical cluster component130partitions the set of classes using a vertical clustering algorithm.

In operation440, where the vertical cluster component130determines an input mode of runtime traces, the vertical cluster component130partitions the set of classes. The set of classes may be partitioned based on one or more of an in-degree and an out-degree relative to a set of runtime traces. In some embodiments, the vertical cluster component130calculates in-degree and out-degree values of each class based on direct call relations and indirect call relations for those classes. The vertical cluster component130may calculate an in-degree/out-degree ratio for each class. Based on the in-degree/out-degree ratio, the vertical cluster component130generates a similarity matrix based on a difference in the in-degree/out-degree ratio for each class. The vertical cluster component130may then apply a hierarchical clustering algorithm to generate partitions among the classes.

The vertical cluster component130may calculate the similarity matrix between pairs of classes. In some embodiments, the vertical cluster component130generates an in-degree and an out-degree value for each class from the reduced runtime path. The vertical cluster component130may consider relations between the classes. In a manner similar to that described above with respect to the horizontal cluster component120, the vertical cluster component130may consider two types of relation, a call relation and a co-occur relation. The vertical cluster component130may increment in-degree and out-degree by one where there is a call relation between a source class and a target class (e.g., two classes of a pair). The vertical cluster component130may increment the in-degree and out-degree by

1disij
when there is a co-occur relation. In some embodiments, the vertical cluster component130then calculates an in-degree/out-degree ratio as

in⁢-⁢degreein⁢-⁢degree+out⁢-⁢degree.
In such instances, the similarity score between the two classes ciand cjmay be calculated as

In operation450, the vertical cluster component140generates the set of vertical clusters. The vertical cluster component130may generate the set of vertical clusters from the set of classes based on the input of static code and operations420-430or based on the input of the runtime trace and operation440. The vertical clusters may be generated in a single set of operations or iteratively. Where the vertical clusters are generated iteratively, the vertical cluster component140may generate an initial set of vertical clusters and subsequent sets of vertical clusters based on the initial set of vertical clusters and one or more subsequent operations to refine the initial set of vertical clusters.

Embodiments of the present disclosure may be implemented together with virtually any type of computer, regardless of the platform is suitable for storing and/or executing program code.FIG. 5shows, as an example, a computing system500(e.g., cloud computing system) suitable for executing program code related to the methods disclosed herein and for decomposing monolithic applications to form microservices.

As shown in the figure, computer system/server500is shown in the form of a general-purpose computing device. The components of computer system/server500may include, but are not limited to, one or more processors502(e.g., processing units), a system memory504(e.g., a computer-readable storage medium coupled to the one or more processors), and a bus506that couple various system components including system memory504to the processor502. Bus506represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limiting, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnects (PCI) bus. Computer system/server500typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server500, and it includes both, volatile and non-volatile media, removable and non-removable media.

The program/utility, having a set (at least one) of program modules516, may be stored in the system memory504by way of example, and not limiting, as well as an operating system, one or more application programs, other program modules, and program data. Program modules may include one or more of the class component110, the horizontal cluster component120, the vertical cluster component130, the unit component140, and the microservice component150, which are illustrated inFIG. 1. Each of the operating systems, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules516generally carry out the functions and/or methodologies of embodiments of the present disclosure, as described herein.

The computer system/server500may also communicate with one or more external devices518such as a keyboard, a pointing device, a display520, etc.; one or more devices that enable a user to interact with computer system/server500; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server500to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces514. Still yet, computer system/server500may communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter522. As depicted, network adapter522may communicate with the other components of computer system/server500via bus506. It should be understood that, although not shown, other hardware and/or software components could be used in conjunction with computer system/server500. Examples include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

Deployment models may include private cloud, community cloud, public cloud, and hybrid cloud. In private cloud, the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. In community cloud, the cloud infrastructure is shared by several organizations and supports specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party that may exist on-premises or off-premises. In public cloud, the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. In hybrid cloud, the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).

Cloud models may include characteristics including on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. In on-demand self-service a cloud consumer may unilaterally provision computing capabilities such as server time and network storage, as needed automatically without requiring human interaction with the service's provider. In broad network access, capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). In resource pooling, the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). In rapid elasticity, capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. In measured service, cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.

The corresponding structures, materials, acts, and equivalents of all means or steps plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements, as specifically claimed. The description of the present disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the present disclosure in the form disclosed. Many modifications and variations will be apparent to those of ordinary skills in the art without departing from the scope of the present disclosure. The embodiments are chosen and described in order to explain the principles of the present disclosure and the practical application, and to enable others of ordinary skills in the art to understand the present disclosure for various embodiments with various modifications, as are suited to the particular use contemplated.