Patent Publication Number: US-11650809-B2

Title: Autonomous and optimized cloning, reinstating, and archiving of an application in a containerized platform

Description:
BACKGROUND 
     The present invention relates to backing up and restoring software applications, and more particularly to optimizing cloning, reinstating, and archiving of containerized applications. 
     In traditional information technology (IT) environments, different applications and database teams maintain their own infrastructures, which include servers, storage, cooling, data center space, software-based virtualization technology, etc. These infrastructures are often under-utilized and require separate teams to design and maintain. Containers provide a lightweight alternative to virtual machines that include an operating system, applications, and application dependencies. By using containers, an application can be efficiently packaged along with its dependencies, thereby streamlining workload scheduling. Containerized platforms use a microservices architecture for cost efficiency, flexibility, and scale, where servers are pooled together and a software-defined scheduler handles workload requests for resources and schedules the workload in cases in which capacity is available. Containers and container platforms (also referred to herein as containerized platforms) were originally used for stateless applications, where no data is stored, but are increasingly used for stateful applications that have a persistence layer. The container platforms are designed to operate on commodity hardware and use application replicas (i.e., multiple copies of an application or service running on different servers or nodes in a cluster) to provide high availability by which an application that fails on a certain server due to hardware failure or any other reason is automatically routed to another instance of the application on a different server. 
     Known backup and restore approaches are dependent on underlying infrastructure support for a particular storage mechanism. Furthermore, known backup and restore approaches lack integration with governance artifacts and autonomous compliance to an established governance framework. 
     Known virtual machine cloning approaches require the cloning of an entire virtual machine that includes an operating system, runtime environment, and application components, thereby preventing the cloning from being able to respond to regulatory questions related to data handling. Since the known virtual cloning approaches virtualize at the hardware level, each virtual machine requires a separate operating system, thereby requiring a significant amount of additional memory and storage resources and limiting portability and flexibility across different cloud environments. 
     Accordingly, there is a need for a lightweight, portable, autonomous, and policy-driven cloning and reinstating approach that consistently clones and reinstates related components of cloud-native containerized applications, application metadata, application state, and underlying application data, where the approach is infrastructure-independent and ensures compliance with a governance framework established by an enterprise. 
     SUMMARY 
     In one embodiment, the present invention provides a computer system. The computer system includes a central processing unit (CPU); a memory coupled to the CPU; and one or more computer readable storage media coupled to the CPU. The one or more computer readable storage media collectively contain instructions that are executed by the CPU via the memory to implement a method of cloning, reinstating, and archiving an application. The method includes the computer system identifying a triggering of a cloning of an application in a cloud-native containerized platform. The method further includes in response to the identifying the triggering of the cloning of the application, the computer system obtaining application metadata from a catalog. The application metadata defines components of the application. The method further includes based on the application metadata, the computer system identifying and obtaining rules specifying the cloning of the application. The method further includes based on the rules, the computer system selecting multiple application components included in a plurality of components of the application. The method further includes the computer system determining that a cloning of the multiple application components is compliant with the rules. The method further includes in response to the selecting the multiple application components and the determining that the cloning of the multiple application components is compliant with the rules, the computer system cloning the multiple application components. The method further includes the computer system saving data in the cloned multiple application components to a clone repository. The method further includes the computer system updating the catalog with specifications of the cloned multiple application components. 
     The aforementioned embodiment provides a lightweight, portable, autonomous, and policy-driven cloning and reinstating approach that consistently clones related components of cloud-native containerized applications, application metadata, application state, and underlying application data, where the cloning approach is infrastructure-independent and ensures compliance with a governance framework established by an enterprise. The aforementioned embodiment further provides an ability to clone stateful applications regardless of the underlying storage mechanism. The aforementioned embodiment provides an ability to save application clones in a desired clone repository that can be mounted as a persistence volume in the containerized platform. The aforementioned embodiment enables disaster recovery of applications in the containerized platform and enables blue/green testing. 
     In one optional aspect of the present invention, the method further includes the computer system quiescing the application. The method further includes the computer system scheduling cloning jobs in parallel on the cloud-native containerized platform. The cloning of the multiple application components includes exporting data from the multiple application components to the clone repository by employing the cloning jobs in parallel subsequent to the quiescing of the application. The aforementioned aspect of the present invention advantageously provides an optimized and time-efficient cloning approach by using parallelization in the cloning of application components. 
     In another optional aspect of the present invention, the method further includes the computer system identifying a triggering of a reinstatement of a clone of the application. The method further includes in response to the identifying the triggering of the reinstatement, the computer system obtaining the application metadata from the catalog. The method further includes based on the application metadata, the computer system identifying and obtaining reinstate rules specifying the reinstatement of the clone of the application. The method further includes the computer system determining that the reinstatement of the clone of the application is compliant with the reinstate rules. The method further includes in response to the determining that the reinstatement of the clone of the application is compliant with the reinstate rules, the computer system reinstating the clone of the application. The method further includes the computer system updating the catalog with specifications of the reinstated clone of the application. The aforementioned aspect of the present invention advantageously provides a flexible and efficient reinstatement approach for containerized applications that is infrastructure-independent. 
     In another optional aspect of the present invention, the method further includes the computer system scheduling reinstating jobs in parallel on the cloud-native containerized platform. The reinstating of the clone of the application includes importing data from the clone repository for the reinstatement of the clone of the application by employing the reinstating jobs in parallel. The aforementioned aspect of the present invention advantageously provides an optimized and time-efficient reinstatement approach by using parallelization in the reinstating of application components. 
     In another embodiment, the present invention provides a computer system. The computer system includes a central processing unit (CPU); a memory coupled to the CPU; and one or more computer readable storage media coupled to the CPU. The one or more computer readable storage media collectively contain instructions that are executed by the CPU via the memory to implement a method of cloning, reinstating, and archiving an application. The method includes the computer system collecting information from a plurality of applications in a cloud-native containerized platform. The plurality of applications includes microservices. The information is collected from a data dictionary, metadata of the microservices, metadata of namespaces associated with the microservices, and data about interactions among the microservices. The method further includes based on the collected information, the computer system training a machine learning-based cognitive classifier by employing a classification algorithm. The method further includes subsequent to the training the machine learning-based cognitive classifier, the computer system extracting application metadata defining components of an application and dependencies among the components in the cloud-native containerized platform. The method further includes the computer system determining that the extracted application metadata does not exist in a governance catalog. The method further includes in response to the determining that the extracted application metadata does not exist in the governance catalog, the computer system inferring one or more associations between the application and one or more other applications included in the plurality of applications by employing the trained machine learning-based cognitive classifier on the extracted application metadata. The method further includes the computer system receiving from a human subject matter expert a validation of an association between the application and another application included in the plurality of applications. The association is included in the inferred one or more associations. The method further includes based on the validation of the association, the computer system updating the governance catalog with the extracted application metadata. The method further includes subsequent to the updating the governance catalog, the computer system extracting second application metadata defining components of a second application and dependencies among the components of the second application in the cloud-native containerized platform. The method further includes the computer system determining that the extracted second application metadata exists in the governance catalog. The method further includes in response to the determining that the second extracted application metadata exists in the governance catalog, the computer system identifying policies and rules associated with the second application by employing the trained machine learning-based cognitive classifier. The method further includes the computer system updating the governance catalog with the identified policies and rules as being associated with the second application. The method further includes based on the updated governance catalog, the computer system cloning the components of the second application or reinstating a clone of the second application. 
     The aforementioned embodiment provides an autonomous approach to cloning, reinstating, and archiving of applications that uses a governance-infused framework and satisfies retention policies of enterprises and regulatory requirements for sensitive data that is cloned, where the approach controls who is permitted to trigger the cloning and reinstatement of the application and ensures required parties are notified about the cloning and reinstatement. Further, the aforementioned embodiment employs machine learning-based classification of application components to business subject areas and logical areas, thereby allowing cloning and reinstating approaches described herein to be based on policies and rules specific to the business subject and logical areas. 
     Respective computer program products and methods corresponding to the above-summarized computer systems are also described herein. The advantages discussed above relative to the computer systems also apply to the respective computer program products and methods. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram of a system for cloning, reinstating, and archiving an application in a containerized platform, in accordance with embodiments of the present invention. 
         FIG.  2    is a flowchart of a process of collecting metadata for an application in a containerized platform, where the process is implemented in the system of  FIG.  1   , in accordance with embodiments of the present invention. 
         FIG.  3    is a flowchart of a process of cloning an application in a containerized platform, where the process is implemented in the system of  FIG.  1   , in accordance with embodiments of the present invention. 
         FIG.  4    is a flowchart of a process of reinstating a clone of an application in a containerized platform, where the process is implemented in the system of  FIG.  1   , in accordance with embodiments of the present invention. 
         FIG.  5    is a block diagram of a machine learning-based cognitive classifier and components used to train the machine learning-based cognitive classifier, where the classifier is included in the system of  FIG.  1   , in accordance with embodiments of the present invention. 
         FIG.  6    is an example of cloning and reinstating an application in a containerized platform using the processes of  FIG.  3    and  FIG.  4    and the system of  FIG.  1   , in accordance with embodiments of the present invention. 
         FIG.  7    is a block diagram of a computer that is included in the system of  FIG.  1    and that implements the processes of  FIG.  2   ,  FIG.  3   , and  FIG.  4   , in accordance with embodiments of the present invention. 
         FIG.  8    depicts a cloud computing environment, in accordance with embodiments of the present invention. 
         FIG.  9    depicts abstraction model layers, in accordance with embodiments of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Overview 
     Most enterprise applications host customer and/or product sensitive data. Enterprises employ known cloning and reinstating approaches that fail to establish a governance framework to manage information assets and fail to provide an automated system that clones and archives applications in accordance with corporate retention policies and regulatory requirements. 
     Microservices based applications and services that run on containerized platforms have holistically multiple dependencies on other microservices. Containerized platforms also support a wide range of underlying storage infrastructure technologies used, as the storage layer is abstracted from the microservices. Known cloning and reinstating approaches fail to consider microservices dependencies and fail to provide a storage layer agnostic solution. Furthermore, known cloning and reinstating approaches fail to accurately capture the application state given the aforementioned dependencies, thereby failing to provide application integrity. 
     Traditional backup and restore methods fail to provide a cloning approach that employs an autonomous and governance-infused mechanism to create clones. Known approaches to cloning lack an ability to consistently clone the related components of an application, application metadata, application state, and the underlying application data in a cloud-native architecture, where the application is broken into multiple microservices with an underlying distributed storage architecture for persistent data services. 
     Organizations are taking a multi-cloud approach in which application workloads are shifted from on-premises to the public cloud and even from one cloud service provider to another. Known clone and reinstate approaches lack the flexibility and infrastructure independence required by the multi-cloud approach. 
     Existing backup and restore techniques that use storage level snapshot capabilities depend on support for a particular infrastructure (e.g., a particular storage platform or an underlying storage technology plugins), thereby making these techniques inflexible. Furthermore, existing backup and restore techniques are limited because they lack integration with governance artifacts and autonomous compliance to an established governance framework. One or more existing backup and restore techniques have a dependency on Container Storage Interface (CSI) drivers and specifications. 
     Embodiments of the present invention address the aforementioned unique challenges of existing backup and restore solutions by providing an infrastructure-independent clone, reinstate, and archiving process, which is enabled with governance-driven controls that ensure only authorized individual(s) and/or authorized group(s) are permitted to request a clone to be reinstated. The governance-driven controls manage reinstatement with no data loss and provides an efficient reinstatement of clones to an appropriate target infrastructure. 
     Embodiments of the present invention provide a governance-infused, autonomous clone and reinstate capability that supports multi-cloud and hybrid cloud environments. Embodiments of the present invention provide a governance-infused framework for application retention through cloning and for a reinstatement of clones. Embodiments of the present invention provide a storage-independent and a CSI-independent clone and reinstate technique. 
     As used herein, “cognitive” is defined as pertaining to a system or process that provides artificial intelligence capabilities that perform machine learning tasks. As used herein, “cognitive” and its variants are not to be construed as being or pertaining to mental processes or concepts performed in the human mind. 
     System for Autonomous and Optimized Cloning, Reinstating, and Archiving of an Application in a Containerized Platform 
       FIG.  1    is a block diagram of a system  100  for cloning, reinstating, and archiving an application in a containerized platform, in accordance with embodiments of the present invention. System  100  includes a computer  102  that includes a software-based autonomous and optimal clone and reinstate system  104  which includes a governance module  106 , a clone &amp; reinstate module  108  and a management module  110 . 
     Governance module  106  sources application metadata and organizes application metadata in a governance taxonomy. Governance module  106  includes a governance catalog  112  (i.e., a catalog of metadata) that includes business metadata and technical metadata. The business metadata is organized in a taxonomy of subject areas and logical areas. The technical metadata specifies all aspects of metadata of an application including, but not limited to (i) an identifier of the application, (ii) name of the application, (iii) application label, (iv) replication factor, (v) storage class, (vi) retention information (e.g., details pertaining to retention period, timeframe to move to cold storage, etc.), (vii) identifier(s) of related application(s), and (vii) date and time of most recent clone. 
     Governance module  106  provides functions that include, but are not limited to (i) onboarding application metadata to governance catalog  112 , (ii) associating applications to subject areas and logical areas, (iii) adding metadata of related application(s), (iv) establishing governance rules and policies, and (v) checking for compliance with governance rules and policies. 
     Clone &amp; reinstate module  108  performs tasks related to scheduling and functioning of clone and reinstate operations. Clone &amp; reinstate module  108  extracts a current state (i.e., number of replicas of individual microservices) of an application being cloned, extracts object specifications for different containerized platform objects (e.g., deployments, statefulsets, secrets, roles, role bindings, etc.), schedules and triggers parallelized application data export jobs on the containerized platform for cloning, and schedules and triggers parallelized application data import jobs on the containerized platform for reinstatement of a cloned application. For the scheduling of the parallelized application data export and import jobs, clone &amp; reinstate module  108  employs one work stream for each of the persistence volumes used by the application. Clone &amp; reinstate module  108  also ensures proper sequencing of sub-tasks, so all application object dependencies are created prior to creating the application objects. Other functions performed by clone &amp; reinstate module  108  include quiescing the application and scaling up of individual application components. 
     Clone &amp; reinstate module  108  includes a machine learning-based cognitive classifier  114 , which generates a classification model based on information about assets of an enterprise, including inter-relationships among applications and clones of the enterprise, and information about the cloud-native containerized platform. The cloud-native containerized platform is also referred to herein simply as the containerized platform. 
     Given a clone that is not included in the governance catalog  112 , clone &amp; reinstate module  108  employs the classification model to infer associations between the given clone and the application that was cloned and between the given clone and other applications. Further, clone &amp; reinstate module  108  employs the classification model to generate confidence scores indicating how likely the inferred associations are actual associations between the given clone and the aforementioned applications. Moreover, given a clone that is included in governance catalog  112 , clone &amp; reinstate module  108  employs the classification model to recommend enterprise-level rules and policies that are associated with the clone. 
     Clone &amp; reinstate module  108  clones an application from a source cluster  116  (i.e., any cluster in the containerized platform). Clone &amp; reinstate module  108  reinstates a previously cloned application to a target cluster  118 . Clone &amp; reinstate module  108  performs cloning and reinstating to a clone repository  120 , which is not limited to a particular storage mechanism (i.e., the cloning and reinstating are storage independent). 
     In one embodiment, source cluster  116  employs container images in a centralized image registry for cloning components of an application, while clone repository  120  can reinstate clones to multiple container images in multiple local cluster registries in multiple target clusters. 
     Management module  110  generates reports about clone and reinstatement activity and scheduling of regular clone jobs for governance-driven archiving. Functions of management module  110  include, but are not limited to (i) access management, (ii) provisioning of the clone repository  120 , (iii) continuous monitoring of a governance framework provided by governance module  106  to determine scheduling of cloning and/or reinstatement, (iv) continuous monitoring for completion of scheduled cloning and reinstate jobs, and (v) compliance reporting for regulatory requirements. 
     The functionality of the components shown in  FIG.  1    is described in more detail in the discussion of  FIG.  2   ,  FIG.  3   ,  FIG.  4   ,  FIG.  5   ,  FIG.  6   , and  FIG.  7    presented below. 
     Processes for Autonomous and Optimized Cloning, Reinstating, and Archiving of an Application in a Containerized Platform 
       FIG.  2    is a flowchart of a process of collecting metadata for an application in a containerized platform, where the process is implemented in the system of  FIG.  1   , in accordance with embodiments of the present invention. The process of  FIG.  2    begins at a start node  200 . In step  202 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) extracts project or namespace metadata of a cloud-native containerized application. As used herein, a cloud-native containerized application is an application running on a cloud-native containerized platform. Hereinafter in the discussion of  FIG.  2   , the cloud-native containerized application is also referred to simply as “the application.” 
     In step  204 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) extracts service dependencies, which are inter-dependencies among components (i.e., microservices) of the application. 
     In step  206 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) determines whether the application exists in governance catalog  112  (see  FIG.  1   ) by searching governance catalog  112  (see  FIG.  1   ) for the metadata and dependencies extracted in steps  202  and  204 . If the search in step  206  does not find the aforementioned metadata and dependencies, then autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) determines that the application is not in governance catalog  112  (see  FIG.  1   ), the No branch of step  206  is followed, and step  208  is performed. 
     In step  208  and in response to the determination in step  206  that the application does not exist in governance catalog  112  (see  FIG.  1   ), machine learning-based cognitive classifier  114  (see  FIG.  1   ) infers a classification of the application using the classification model together with the metadata and dependencies extracted in steps  202  and  204 . The classification inferred in step  208  includes association(s) between the application and one or more other applications. In step  208 , machine learning-based cognitive classifier  114  (see  FIG.  1   ) also generates confidence score(s) indicating how likely the inferred classification indicates actual association(s) between the application and the one or more other applications. In one embodiment, machine learning-based cognitive classifier  114  (see  FIG.  1   ) performs step  208  after being trained by using components included in  FIG.  5   , as described below. 
     In step  210 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) sends the classification inferred in step  208  to a subject matter expert (SME). The SME validates the association(s) between the application and the one or more other applications. 
     In step  212  and based on the validation of the association(s) by the SME, autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) updates governance catalog  112  (see  FIG.  1   ) with the metadata and dependencies extracted in steps  202  and  204 , along with the association(s) validated by the SME. 
     Following step  212 , the process of  FIG.  2    ends at an end node  214 . 
     Returning to step  206 , if the search finds the metadata and dependencies extracted in steps  202  and  204 , then autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) determines that the application is in governance catalog  112  (see  FIG.  1   ), the Yes branch of step  206  is followed, and step  216  is performed. 
     In step  216 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) checks governance catalog  112  (see  FIG.  1   ) for an existence of governance policies and rules associated with the application, where the policies and rules are specified in a governance framework for an enterprise. In one embodiment, the policies and rules are based on regulatory requirements. 
     In step  218 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) determines whether the governance policies and rules exist in governance catalog  112  (see  FIG.  1   ) for the application as a result of the check in step  216 . If autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) determines in step  218  that the governance policies and rules exist for the application, then the Yes branch of step  218  is followed and step  220  is performed. 
     In step  220 , machine learning-based cognitive classifier  114  (see  FIG.  1   ) recommends rules and policies that are associated with the application, where the recommendation is generated by employing the classification model. In one embodiment, machine learning-based cognitive classifier  114  (see  FIG.  1   ) performs step  220  after being trained by using components included in  FIG.  5   , as described below. 
     In step  222 , machine learning-based cognitive classifier  114  (see  FIG.  1   ) sends the recommended rules and policies to the SME, who accepts one or more of the recommended rules and policies or defines one or more other rules and policies for the application. 
     In step  212  which follows step  222 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) updates governance catalog  112  (see  FIG.  1   ) with an association between the application and the rules and policies accepted or defined by the SME. Again, following step  212 , the process of  FIG.  2    ends at the end node  214 . 
     In one embodiment, after the update of governance catalog  112  (see  FIG.  1   ) in step  212 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) clones components of another application in the cloud-native containerized platform using the process of  FIG.  3   , as described below, or reinstates a clone of another application in the cloud-native containerized platform using the process of  FIG.  4   , as described below. 
     Returning to step  218 , if autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) determines that governance policies and rules do not exist in governance catalog  112  (see  FIG.  1   ) for the application, then the No branch of step  218  is followed and step  224  is performed. 
     In step  224 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) sends information to the SME about no policies and rules existing in governance catalog  112  (see  FIG.  1   ). The SME can accept that there are no policies and rules existing in governance catalog  112  (see  FIG.  1   ) or the SME can work with the enterprise to define policies and rules. Following step  224 , the process of  FIG.  2    ends at the end node  214 . 
       FIG.  3    is a flowchart of a process of cloning an application in a containerized platform, where the process is implemented in the system of  FIG.  1   , in accordance with embodiments of the present invention. The process of  FIG.  3    begins at a start node  300 . In step  302 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) triggers an initiation of cloning a cloud-native containerized application. Hereinafter in the discussion of  FIG.  3   , the cloud-native containerized application is also referred to simply as “the application.” The triggering of the initiation of cloning in step  302  is in response to a scheduled cloning of the application (e.g., initiate cloning every Tuesday) or the autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) detecting an occurrence of an event designated as a trigger for cloning the application (e.g., initiate cloning on a particular Monday because detected sales over the weekend exceeded a threshold level). 
     In step  304 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) obtains metadata about the application from governance catalog  112  (see  FIG.  1   ). The metadata obtained in step  304  includes a definition of components (i.e., microservices) of the application. In one embodiment, step  304  is performed subsequent to one or more performances of the process of  FIG.  2   , which obtains metadata for one or more containerized applications to update governance catalog  112  (see  FIG.  1   ). 
     In step  306 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) determines whether clone rules for the application exist in governance catalog  112  (see  FIG.  1   ) by searching governance catalog  112  (see  FIG.  1   ) for clone rules associated with the metadata obtained in step  304 . If the search in step  306  does not find the clone rules, then autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) determines that the clone rules do not exist in governance catalog  112  (see  FIG.  1   ), the No branch of step  306  is followed, and step  308  is performed. 
     In step  308 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) selects or sets default clone parameters for cloning the application. The default clone parameters include, for example, specifications of the particular components of the application to be cloned and where to store the clone. 
     In step  310  and in response to performing step  308 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) initiates the cloning of the application (i.e., the cloning of the specified components of the application) by employing the default clone parameters. 
     In one embodiment, prior to step  310 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) quiesces the application and schedules cloning jobs in parallel on the cloud-native containerized platform. Subsequent to the quiescing of the application, step  310  includes autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) exporting data from the specified or selected components of the application to clone repository  120  (see  FIG.  1   ) by employing the cloning jobs executed in parallel. 
     In step  312 , after the cloning is complete, autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) saves the clone of the application to clone repository  120  (see  FIG.  1   ), where the clone being saved includes data in the cloned components of the application. 
     In step  314 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) updates governance catalog  112  (see  FIG.  1   ) by adding information about the cloning of the application, where the information includes specifications of the cloned components of the application. In one embodiment, the information in step  314  includes, but is not limited to, the date and time of the cloning, an identification of the entity who initiated the cloning, the amount of time taken to complete the cloning, the size of the clone, and where the clone is stored. Autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) uses the information added in step  314  to ensure regulatory requirements are satisfied. 
     Following step  314 , the process of  FIG.  3    ends at an end node  316 . 
     Returning to step  306 , if the search finds the clone rules, then autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) determines that the clone rules exist in governance catalog  112  (see  FIG.  1   ), the Yes branch of step  306  is followed, and step  318  is performed. 
     In step  318 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) identifies and obtains clone rules and collects information about the clone rules, which are rules that apply to a cloning of the application. 
     In one embodiment, the clone rules include, but are not limited to, specifications of (i) one or more entities who are permitted to trigger the cloning of the application, (ii) the multiple application components as being permitted to be cloned, (iii) a method for quiescing the application, (iv) a method of encryption or obfuscation required for cloning of the multiple application components, (v) a storage medium permitted to be used in the cloning of the multiple application components, and (vi) one or more entities who require notification of the cloning of the multiple application components. 
     In step  320 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) selects appropriate clone source application components from a plurality of components of the application and selects policy enforcement actions by applying parameters for cloning the application in compliance with the clone rules. Selecting the clone source application components includes selecting components of the application in compliance with the information collected in step  318  about the clone rules that apply to the application (i.e., determining that the cloning of the selected components is in compliance with the clone rules). The policy enforcement actions are based on considerations in compliance with the clone rules, where the considerations include, but are not limited to, (i) who can trigger the cloning of the application, (ii) what components of the application can be cloned, (iii) how can the components of the application be quiesced, (iv) what encryption or obfuscation measures are needed for the cloning, and (v) what storage media are to be used for storing the clone. 
     In step  310  and following step  320 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) initiates the cloning of the application (i.e., the cloning of the selected components of the application) by employing the clone rules whose information was collected in step  318 . 
     In step  312 , after the cloning is complete following steps  320  and  310  in succession, autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) saves the clone of the application to clone repository  120  (see  FIG.  1   ), where the clone being saved includes data in the cloned components of the application. Again, in step  314  autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) updates governance catalog  112  (see  FIG.  1   ) by adding information about the cloning of the application, where the information includes specifications of the cloned components of the application. Again, following step  314 , the process of  FIG.  3    ends at the end node  316 . 
       FIG.  4    is a flowchart of a process of reinstating a clone of an application in a containerized platform, where the process is implemented in the system of  FIG.  1   , in accordance with embodiments of the present invention. The process of  FIG.  4    begins at a start node  400 . In step  402 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) triggers an initiation of reinstating a clone of a cloud-native containerized application. Hereinafter in the discussion of  FIG.  4   , the cloud-native containerized application is also referred to simply as “the application.” The triggering of the initiation of reinstating in step  402  is in response to a scheduled reinstatement of the clone of the application (e.g., initiate reinstating the most recent clone every Monday) or the autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) receiving instructions to initiate reinstating the clone of the application based on an occurrence of an event or activity. 
     In step  404 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) obtains metadata about the application from governance catalog  112  (see  FIG.  1   ). In one embodiment, step  404  is performed subsequent to one or more performances of the process of  FIG.  2   , which obtains metadata for one or more containerized applications to update governance catalog  112  (see  FIG.  1   ). 
     In step  406 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) determines whether reinstate rules for the application (i.e., rules specifying the reinstatement of the clone of the application) exist in governance catalog  112  (see  FIG.  1   ) by searching governance catalog  112  (see  FIG.  1   ) for reinstate rules associated with the metadata obtained in step  404 . If the search in step  406  does not find the reinstate rules, then autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) determines that the reinstate rules for the application do not exist in governance catalog  112  (see  FIG.  1   ), the No branch of step  406  is followed, and step  408  is performed. 
     In step  408  and in response to determining that the reinstate rules do not exist in step  406 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) selects or sets default reinstate parameters for reinstating a clone of the application. The default clone parameters include, for example, specifications of who is permitted to initiate the reinstatement of the clone of the application and where to store the reinstated clone. 
     In step  410 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) initiates the reinstatement of the clone of the application by employing the default reinstate parameters that were set in step  408 . 
     In one embodiment, prior to step  410 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) schedules reinstating jobs in parallel on the cloud-native containerized platform. Subsequent to the scheduling of the reinstating jobs, step  410  includes autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) importing data from clone repository  120  (see  FIG.  1   ) for the reinstatement of the clone of the application by employing the reinstating jobs executed in parallel. 
     In step  412 , after the reinstatement of the clone of the application is complete, autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) updates governance catalog  112  (see  FIG.  1   ) by adding information about the reinstatement of the clone of the application, where the information includes specifications of the reinstated clone of the application. In one embodiment, the information in step  412  includes, but is not limited to, the date and time of the reinstating, an identification of the entity who initiated the reinstating, the amount of time taken to complete the reinstating, the size of the reinstated clone, and where the reinstated clone is stored. Autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) uses the information added in step  412  to ensure regulatory requirements are satisfied. 
     Following step  412 , the process of  FIG.  4    ends at an end node  414 . 
     Returning to step  406 , if the search finds the reinstate rules, then autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) determines that the reinstate rules exist in governance catalog  112  (see  FIG.  1   ), the Yes branch of step  406  is followed, and step  416  is performed. 
     In step  416 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) identifies and obtains reinstate rules and collects information about the reinstate rules, which are rules that apply to reinstating a clone of the application. 
     In one embodiment, the reinstate rules include, but are not limited to, specifications of (i) one or more entities who are permitted to trigger the reinstatement of the clone of the application, (ii) a storage medium permitted to be used in the reinstatement of the clone of the application, and (iii) one or more entities who require a notification of the reinstatement of the clone of the application. 
     In step  418 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) selects an appropriate reinstate target by applying parameters for reinstating the clone of the application in compliance with the reinstate rules. Step  418  includes autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) selecting policy enforcement actions based on considerations in compliance with the reinstate rules, where the considerations include, but are not limited to, (i) who can trigger the reinstating of the clone of the application, (ii) what entity or entities need to be notified about the reinstatement, and (iii) what storage media options can be used for storing the reinstated clone. 
     In step  410  and following step  418 , autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) initiates the reinstating of the clone of the application by employing the reinstate rules whose information was collected in step  416 . 
     In step  412 , after the cloning is complete following steps  418  and  410  in succession, autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) updates governance catalog  112  (see  FIG.  1   ) by adding information (as described above) about the reinstatement of the clone of the application. Again, following step  412 , the process of  FIG.  4    ends at the end node  414 . 
     Machine Learning-Based Classifier 
       FIG.  5    is a block diagram of machine learning-based cognitive classifier  114  and components used to train the machine learning-based cognitive classifier  114 , where the classifier is included in the system of  FIG.  1   , in accordance with embodiments of the present invention. In a training phase, a classification algorithm  502  receives the following input from a plurality of applications in the cloud-native containerized platform: data dictionary  504 , namespace metadata  506  (i.e., metadata of namespaces associated with microservices included in the plurality of applications), service metadata  508  (i.e., metadata of the microservices included in the plurality of applications), and service interactions data  510  (i.e., data about interactions among the microservices). Classification algorithm  502  uses the aforementioned input to train machine learning-based cognitive classifier  114  and generate a classification model. Autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) employs the classification model to determine inter-relationships between enterprise assets, such as inter-relationships among the application, other application(s), and clone(s) of the application. 
     For example, autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) can use the classification model generated by machine learning-based cognitive classifier  114  to determine that a human resources application XYZABC being cloned is related to a sales application GHI. Over time, the classification model learns more relationships among enterprise assets. For a subsequent cloning of a different application XYZDEF, machine learning-based cognitive classifier  114  determines that attributes of application XYZDEF match attributes of application XYZABC (e.g., both applications have names that start with the same combination of letters “XYZ”), and therefore determines that application XYZDEF is a human resources application (i.e., the same type of application as application XYZABC) and is related to a sales application (i.e., related to an application of the same type as application GHI, which is a sales application related to human resources application XYZABC). 
     EXAMPLES 
       FIG.  6    is an example  600  of cloning, archiving, and reinstating an application in a containerized platform using the processes of  FIG.  3    and  FIG.  4    and the system of  FIG.  1   , in accordance with embodiments of the present invention. Steps in example  600  are illustrated by circled numbers 1 through 10. Steps 1-2 describe prefatory steps for cloning and reinstating of the cloud-native containerized application. Steps 3-6 describe a cloning of the cloud-native containerized application. Steps 7-10 describe a reinstating of a clone of the cloud-native containerized application. 
     In step 1, clone &amp; reinstate module  108  inserts and/or updates clone metadata and activity information in governance catalog  112  for a cloning of a cloud-native containerized application. In step 2, clone &amp; reinstate module  108  checks rules in governance catalog  112  to enforce governance for the cloning of the cloud-native containerized application. 
     In step 3, clone &amp; reinstate module  108  downloads (i) metadata that defines component of the cloud-native containerized application and (ii) the current state of the cloud-native containerized application. The current state includes then number of replicas of the cloud-native containerized application that are running. The current state allows the running of multiple replicas of the cloud-native containerized application to provide high availability of the application. 
     In step 4, clone &amp; reinstate module  108  spawns parallel data export jobs in source cluster  116 . Source cluster  116  analyzes the cloning of each of the components of the cloud-native containerized application. In step 5, source cluster  116  copies information resulting from the analysis of the cloning of each of the components to clone repository  120 . Clone repository  120  may be a high-performance clustered file system (e.g., General Parallel File System), a distributed file system (e.g., network file system (NFS)), or another storage system. 
     In step 6, clone &amp; reinstate module  108  publishes the metadata and the state for the clone in clone repository  120 . 
     Steps 1 and 2 are again performed prior to step 7. In step 1 in preparation for step 7, clone &amp; reinstate module  108  inserts and/or updates metadata and activity information in governance catalog  112  for a reinstatement of a clone of the cloud-native containerized application. In step 2 immediately preceding step 7, clone &amp; reinstate module  108  checks rules in governance catalog  112  to enforce governance for the reinstatement of the clone of the cloud-native containerized application. 
     In step 7, clone &amp; reinstate module  108  downloads metadata and the state of the cloud-native containerized application from clone repository  120 . 
     In step 8, clone &amp; reinstate module  108  applies the downloaded metadata to target cluster  118  and restores the state of the cloud-native containerized application to target cluster  118 . 
     In step 9, clone &amp; reinstate module  108  spawns parallel data import jobs in target cluster  118 . 
     In step 10, in response to performing the parallel data import jobs, target cluster  118  downloads data for the reinstatement from clone repository  120 . 
     After the cloning and reinstating, clone &amp; reinstate module  108  updates governance catalog  112  with information about the cloning and reinstating, as described above in the discussions of  FIG.  3    and  FIG.  4   . 
     In one embodiment, steps 1-10 are used to generate and reinstate clones of different applications in multiple source clusters. Clone &amp; reinstate module  108  stores the clones of the different applications in clone repository  120 . Clone &amp; reinstate module  108  can reinstate the clones of the different applications from clone repository  120 . 
     Computer System 
       FIG.  7    is a block diagram of a computer that is included in the system of  FIG.  1    and that implements the processes of  FIG.  2   ,  FIG.  3   , and  FIG.  4   , in accordance with embodiments of the present invention. Computer  102  is a computer system that generally includes a central processing unit (CPU)  702 , a memory  704 , an input/output (I/O) interface  706 , and a bus  708 . Further, computer  102  is coupled to I/O devices  710  and a computer data storage unit  712 . CPU  702  performs computation and control functions of computer  102 , including executing instructions included in program code  714  for autonomous and optimal clone and reinstate system  104  (see  FIG.  1   ) to perform a method of autonomous and optimal cloning, reinstating, and archiving of a containerized application, where the instructions are executed by CPU  702  via memory  704 . CPU  702  may include a single processing unit or processor or be distributed across one or more processing units or one or more processors in one or more locations (e.g., on a client and server). 
     Memory  704  includes a known computer readable storage medium, which is described below. In one embodiment, cache memory elements of memory  704  provide temporary storage of at least some program code (e.g., program code  714 ) in order to reduce the number of times code must be retrieved from bulk storage while instructions of the program code are executed. Moreover, similar to CPU  702 , memory  704  may reside at a single physical location, including one or more types of data storage, or be distributed across a plurality of physical systems or a plurality of computer readable storage media in various forms. Further, memory  704  can include data distributed across, for example, a local area network (LAN) or a wide area network (WAN). 
     I/O interface  706  includes any system for exchanging information to or from an external source. I/O devices  710  include any known type of external device, including a display, keyboard, etc. Bus  708  provides a communication link between each of the components in computer  102 , and may include any type of transmission link, including electrical, optical, wireless, etc. 
     I/O interface  706  also allows computer  102  to store information (e.g., data or program instructions such as program code  714 ) on and retrieve the information from computer data storage unit  712  or another computer data storage unit (not shown). Computer data storage unit  712  includes one or more known computer readable storage media, where a computer readable storage medium is described below. In one embodiment, computer data storage unit  712  is a non-volatile data storage device, such as, for example, a solid-state drive (SSD), a network-attached storage (NAS) array, a storage area network (SAN) array, a magnetic disk drive (i.e., hard disk drive), or an optical disc drive (e.g., a CD-ROM drive which receives a CD-ROM disk or a DVD drive which receives a DVD disc). 
     Memory  704  and/or storage unit  712  may store computer program code  714  that includes instructions that are executed by CPU  702  via memory  704  to autonomously and optimally clone, reinstate, and archive a containerized application. Although  FIG.  7    depicts memory  704  as including program code, the present invention contemplates embodiments in which memory  704  does not include all of code  714  simultaneously, but instead at one time includes only a portion of code  714 . 
     Further, memory  704  may include an operating system (not shown) and may include other systems not shown in  FIG.  7   . Clone repository  120  (see  FIG.  1   ) may be included in computer data storage unit  712 . 
     As will be appreciated by one skilled in the art, in a first embodiment, the present invention may be a method; in a second embodiment, the present invention may be a system; and in a third embodiment, the present invention may be a computer program product. The computer program product may include one or more computer readable storage media (i.e., memory  704  and computer data storage unit  712 ) having computer readable program code  714  collectively stored on the one or more computer readable storage media, where the program code is executed by CPU  702  to cause computer  102  to perform aspects of the present invention. 
     Any of the components of an embodiment of the present invention can be deployed, managed, serviced, etc. by a service provider that offers to deploy or integrate computing infrastructure with respect to autonomous and optimal cloning, reinstating, and archiving of a containerized application. Thus, an embodiment of the present invention discloses a process for supporting computer infrastructure, where the process includes providing at least one support service for at least one of integrating, hosting, maintaining and deploying computer-readable code (e.g., program code  714 ) in a computer system (e.g., computer  102 ) including one or more processors (e.g., CPU  702 ), wherein the processor(s) carry out instructions contained in the code causing the computer system to autonomously and optimally clone, reinstate, and archive a containerized application. Another embodiment discloses a process for supporting computer infrastructure, where the process includes integrating computer-readable program code into a computer system including a processor. The step of integrating includes storing the program code in a computer-readable storage device of the computer system through use of the processor. The program code, upon being executed by the processor, implements a method of autonomous and optimal cloning, reinstating, and archiving of a containerized application. 
     While it is understood that program code  714  for autonomous and optimal cloning, reinstating, and archiving of a containerized application may be deployed by manually loading directly in client, server and proxy computers (not shown) via loading a computer-readable storage medium (e.g., computer data storage unit  712 ), program code  714  may also be automatically or semi-automatically deployed into computer  102  by sending program code  714  to a central server or a group of central servers. Program code  714  is then downloaded into client computers (e.g., computer  102 ) that will execute program code  714 . Alternatively, program code  714  is sent directly to the client computer via e-mail. Program code  714  is then either detached to a directory on the client computer or loaded into a directory on the client computer by a button on the e-mail that executes a program that detaches program code  714  into a directory. Another alternative is to send program code  714  directly to a directory on the client computer hard drive. In a case in which there are proxy servers, the process selects the proxy server code, determines on which computers to place the proxy servers&#39; code, transmits the proxy server code, and then installs the proxy server code on the proxy computer. Program code  714  is transmitted to the proxy server and then it is stored on the proxy server. 
     Another embodiment of the invention provides a method that performs the process steps on a subscription, advertising and/or fee basis. That is, a service provider can offer to create, maintain, support, etc. a process of autonomous and optimal cloning, reinstating, and archiving of a containerized application. In this case, the service provider can create, maintain, support, etc. a computer infrastructure that performs the process steps for one or more customers. In return, the service provider can receive payment from the customer(s) under a subscription and/or fee agreement, and/or the service provider can receive payment from the sale of advertising content to one or more third parties. 
     The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) (i.e., memory  704  and computer data storage unit  712 ) having computer readable program instructions  714  thereon for causing a processor (e.g., CPU  702 ) to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions (e.g., program code  714 ) for use by an instruction execution device (e.g., computer  102 ). The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions (e.g., program code  714 ) described herein can be downloaded to respective computing/processing devices (e.g., computer  102 ) from a computer readable storage medium or to an external computer or external storage device (e.g., computer data storage unit  712 ) via a network (not shown), for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card (not shown) or network interface (not shown) in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions (e.g., program code  714 ) for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations (e.g.,  FIG.  2   ,  FIG.  3   , and  FIG.  4   ) and/or block diagrams (e.g.,  FIG.  1    and  FIG.  7   ) of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions (e.g., program code  714 ). 
     These computer readable program instructions may be provided to a processor (e.g., CPU  702 ) of a general purpose computer, special purpose computer, or other programmable data processing apparatus (e.g., computer  102 ) to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium (e.g., computer data storage unit  712 ) that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions (e.g., program code  714 ) may also be loaded onto a computer (e.g. computer  102 ), other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be accomplished as one step, executed concurrently, substantially concurrently, in a partially or wholly temporally overlapping manner, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     While embodiments of the present invention have been described herein for purposes of illustration, many modifications and changes will become apparent to those skilled in the art. Accordingly, the appended claims are intended to encompass all such modifications and changes as fall within the true spirit and scope of this invention. 
     Cloud Computing Environment 
     It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
     Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). 
     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. 
     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. 
     Service Models are as follows: 
     Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). 
     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes. 
     Referring now to  FIG.  8   , illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  includes one or more cloud computing nodes  10  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Nodes  10  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment  50  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  54 A,  54 B,  54 C and  54 N shown in  FIG.  8    are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
     Referring now to  FIG.  9   , a set of functional abstraction layers provided by cloud computing environment  50  (see  FIG.  8   ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG.  9    are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
     Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
     In one example, management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provides pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  90  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and autonomous and optimized cloning, reinstating, and archiving of an application in a containerized platform  96 .