Patent Publication Number: US-2023138925-A1

Title: Enterprise cognitive solutions lock-in avoidance

Description:
BACKGROUND 
     The present disclosure relates generally to the field of artificial intelligent (AI)-specific tooling, and more specifically to enterprise cognitive solutions lock-in avoidance. 
     Unlike conversational (e.g., simulation, conversational simulation, etc.) solutions from a business to consumer (b2c) domain, enterprise conversational agents integrate multiple services under a common solution. For example, a natural language processing solution used in AI assistants is just one of multiple system components and a service used is to translate the natural language into commands that the solution can use in order to execute various actions in an enterprise is another component. Accordingly, with multiple services/components for a solution, there is an inherent drawback in the form of vendor lock-in. 
     In service mesh-based architecture there is a request routing concept that allows dynamic routing to different versions of a same application based on user identity. In every system there may be activities by prohibited users, especially if the system is publicly accessible. Most of the users of service mesh architecture may be allowed, however, a prohibited user may impact user experience and security of the allowed users by network intrusion, denial-of-service attacks, etc. 
     SUMMARY 
     Embodiments of the present disclosure include a method, computer program product, and system for avoiding lock-in in the context of cognitive solutions. A processor may analyze a communication associated with a simulation program. The processor may determine whether the simulation program is running. The processor may capture at least one request/response pair in the communication. The processor may store the at least one request/response pair. The processor may train at least one registered natural language processing provider with the request/response pair. 
     The above summary is not intended to describe each illustrated embodiment or every implementation of the present disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The drawings included in the present disclosure are incorporated into, and form part of, the specification. They illustrate embodiments of the present disclosure and, along with the description, serve to explain the principles of the disclosure. The drawings are only illustrative of certain embodiments and do not limit the disclosure. 
         FIG.  1 A  illustrates a block diagram of an example lock-in avoidance system, in accordance with aspects of the present disclosure. 
         FIG.  1 B  illustrates a block diagram of an internal enterprise network, in accordance with aspects of the present disclosure. 
         FIG.  2    illustrates a flowchart of an example method for avoiding lock-in in the context of cognitive solutions, in accordance with aspects of the present disclosure. 
         FIG.  3 A  illustrates a cloud computing environment, in accordance with aspects of the present disclosure. 
         FIG.  3 B  illustrates abstraction model layers, in accordance with aspects of the present disclosure. 
         FIG.  4    illustrates a high-level block diagram of an example computer system that may be used in implementing one or more of the methods, tools, and modules, and any related functions, described herein, in accordance with aspects of the present disclosure. 
     
    
    
     While the embodiments described herein are amenable to various modifications and alternative forms, specifics thereof have been shown by way of example in the drawings and will be described in detail. It should be understood, however, that the particular embodiments described are not to be taken in a limiting sense. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the disclosure. 
     DETAILED DESCRIPTION 
     Aspects of the present disclosure relate generally to the field of artificial intelligent (AI)-specific tooling, and more specifically to enterprise cognitive solutions lock-in avoidance. While the present disclosure is not necessarily limited to such applications, various aspects of the disclosure may be appreciated through a discussion of various examples using this context. 
     Unlike conversational (e.g., simulation, conversational simulation, etc.) solutions from a business to consumer (b2c) domain, enterprise conversational agents (e.g., AI assistants, chatbots, etc.) integrate multiple services under a common solution. For example, a natural language processing solution used in AI assistants is just one of multiple system components and a service used is to translate the natural language into commands that the solution can use in order to execute various actions in an enterprise is another component. A command is a combination of entities plus the intent for any given sentence provided by a user. 
     For example, if a user says: “Please generate the Saturday employee report for me”, a robot (e.g., chatbot) will pass the sentence to a natural language processing (NLP) solution (e.g., AI such as IBM® Watson Assistant) via a REST API and the NLP will return an intent+entities (e.g., intent: SAP_EMPLOYEE_REPORT, Entities: Saturday). Then the robot will have a mapping in a database so that if the intent is “SAP_EMPLOYEE_REPORT”, then it should/will execute the report for Saturday (e.g., the entity) on the employees table from an HR database. 
     As with various cloud platforms, there is the inherent risk in the drawback of vendor lock-in for their NLP services. As a wide majority of enterprises use more than one cloud solution for their business applications, it is important to have a method of easy migration between these vendors. 
     A second topic to consider is that various cloud platforms charge differently, and the charges are most likely based on a per request basis. Having a method of replicating the natural language processing on a local enterprise environment, would reduce the number of queries done on the remote cloud platform and hence reduce the total cost of ownership for the solution. This also reduces vendor lock-in as the enterprise will have a lower dependency on the remote cloud platform APIs. Accordingly, this disclosure proposes solutions for the problem of vendor lock-in (e.g., sometimes referred to herein as “lock-in”) in a conversational (e.g., sometimes referred herein as a “simulation program”) agent&#39;s domain (e.g., enterprise cognitive solutions). 
     It is noted that there are currently no known solutions for the problem domain (e.g., conversational agent&#39;s domain, enterprise cognitive solutions domain, etc.) in the industry. Traditionally, the conversational agents are built on top of a vendor-specific platform, and the conversational agents evolve without the possibility of migration due to vendor lock-in (e.g., where a user of a product/service becomes dependent on a vendor for the products/services and is unable to use another vendor without substantial switching costs). By reducing the number of calls to a remote cloud API (e.g., for cost reduction), traditional cache solutions can be used, however there is a strong limitation related to the fact that there is no exact key match for input sentences. This makes it difficult for the programmers to use them. Thus, the solution provided herein solves these issues. 
     The proposed solution is described throughout this specification as “V-Lock”. V-Lock is a defensive tool that removes vendor lock-in for simulation programs/conversational agents and ensures an easy and automatic migration between NLP service providers. 
     It is noted that some aspects of novelty of the proposed solution(s) are that of a sniffer that can analyze application layer data, a persistent storage, a training method for NLP platforms and an adaptive learning facility that can simulate cloud NLP services in an on-premise environment by approximating the cloud NLP services next moves (e.g., what response is likely to be give/generated, etc.). In some embodiments, the training for the NLP platforms is done vial a neural network, which automatically trained. In some embodiments, the solution(s) provided herein can make recommendations between multiple NLP cloud services based on their performance (e.g., one service is likely to provide a better response to a request than another service, etc.). 
     In some embodiments, the solution(s) is/are reducing the total cost of ownership by reducing the number of calls to a remote cloud NLP service (e.g., by allowing the remote cloud NLP service associate with one client/user to be about to answer previously unanswerable requests, which in turn alleviates the need for multiple requests to be sent/made to the service). In some embodiments, the solution(s) automatically analyze(s) the behavior of a conversational agent (e.g., a first chatbot) and mimics the same behavior with the purpose of replacing the conversational agent with another conversation agent (e.g., a second chatbot); in such an embodiment, the second chatbot is trained to answer requests it previously could not and it allows for the first chatbot to be contacted less because the second chatbot can now adequately respond to requests directed toward the second chatbot. 
     The major advantages from the novelties discussed above are that a system requires no custom programming in order to achieve the purpose of replacing one conversational agent with another, which in-turn reduces vendor lock-in for cloud NLP services. Further, V-Lock allows an enterprise (e.g., entity, business, user) to choose the best NLP provider for its needs. 
     Before turning to the FIGS., it is noted that V-Lock is a software solution that has network access, and which can use a network interface in a mode that causes a controller to pass all traffic it receives to a central processing unit (CPU) rather than passing only the frames that the controller is specifically programmed to receive. Such a mode is normally used for packet sniffing that takes place on a router or on a computer connected to a wired network or one being part of a wireless LAN. Interfaces are placed into such a mode by software bridges often used with hardware virtualization. It is noted that in some embodiments V-Lock can also be a hardware appliance, however the preferred approach is as a software solution. 
     The system (discussed below in regard to  FIG.  1 A ) that includes the V-Lock can capture traffic on the network between an NLP platform and a custom conversational agent code. The V-Lock can conduct an analysis in order to extract ontology, for example, by using metadata in the NLP service response and/or group user provided sentences based on an identified intent (which is explicitly mentioned in the metadata, together with the confidence level of the match). Thus, this means that for a specific conversational log, the V-Lock may be able to build a more exhaustive list of sentences than what was had in an original workspace of a related NLP service (e.g., IBM® Watson Assistant). It is noted that, entities (e.g., what is the object being requested, what action is being requested, etc.) are traditionally highlighted in the metadata of the NLP service response hence V-Lock can also extract the entities in the context of the intent (e.g., what is a client/user asking a conversational agent for [e.g., a report, a picture, destination information, etc.]). 
     In some embodiments, the solution provided herein may utilize a dialogue tree. In such an embodiment, analysis of the dialog tree can utilize various approaches. For example, a neural network can be used to approximate the next intent in a chain for a given identified intent based on historical data (e.g., captured from the network by the V-Lock). Then, after analysis, the neural network will use the information obtained to replace an NLP service provider in different ways depending on a strategy chosen by an administrator (e.g., user, controller, etc.). 
     Such strategies may include, but are not limited to: 
     Training additional NLP providers based on the collected data in order to ensure a migration (e.g., of the collected data, request/response pairs, etc.) in the near future (e.g. within a migration timeframe, such as, when a provider is obligated to provide system within a specific time period). In some embodiments, V-Lock may be able to achieve the integration via direct REST API access of a remote NLP service or via a Robotic Process Automation component which will use the tooling of the NLP to define intents, etc., similar to a human user. 
     For example, uploading a list of sentences identified for specific intents and entities can be uploaded by a UI Robot that can automatically click/engage buttons in/for a tooling of the remote cloud platform in order to define the configuration. V-Lock can use the approach to train multiple NLP cloud providers then use the feedback from users (e.g., most GUIs allow a: “Did the chatbot solve your problem?” type of question) to select the winning cloud platform (e.g., the cloud platform with the most accurate chatbot after training) for migration; and/or 
     Acting as an “in-house” solution, where the V-Lock can reply to a specific request (e.g., message, query, etc.) from a user similar to a situation when an actual NLP service would be called (e.g., allowing for full in-house automation). This allows the enterprise to reduce the total cost of ownership (e.g., not only monetary, but with computing/processing costs due to less strain caused by contacting a remote service, etc.) with the cloud platform. In an embodiment of this strategy, a wide majority of calls will be served by a local neural network that was trained by the method specified above, and throughout this disclosure, and which can estimate the next most likely reply of the remote NLP service. In this way there is no need for additional calls to the remote service and the cost (e.g., monetary, computing, processing, etc.) is reduced. 
     An overview of the proposed solution, in system form, can be visualized in the  FIG.  1 A  below.  FIG.  1 A  illustrates a block diagram of an example lock-in avoidance system  100 , in accordance with aspects of the present disclosure. As depicted, the lock-in avoidance system  100  includes a chatbot with NLP  102  (an NLP running chatbot), NLP providers  104 A-N, a V-Lock  108 , a chatbot client  106 , an enterprise client  110  (e.g., SLACK®, cloud storage service(s), etc.) and actions  112 A-N. 
     In some embodiments, the chatbot with NLP  102  and the NLP providers  104 A-N are considered as public/private cloud chatbot NLP providers. In some embodiments, the V-Lock  108 , chatbot client  106 , the enterprise client  110 , and the actions  112 A-N are considered as (a part of) an enterprise domain. 
     In some embodiments, the chatbot with NLP  102  is in communication with the chatbot client  106 , which is in turn in communication with the enterprise client  110 . In some embodiments, the chatbot client  106  receives a request (e.g., query, question to be answered, etc.) from the enterprise client  110 . The chatbot client  106  may communicate with the chatbot with NLP  102  and receive a response to the request. 
     In some embodiments, the chatbot client  106  may then provide and/or automatically perform actions  112 A-N depending/based on the response. The actions  112 A-N may include enterprise solutions such as APIs, Java®, and/or business flows such as IBM® Blueworks, robotic process automation, etc. 
     In some embodiments, the request and response sent between the chatbot with NLP  102  and the chatbot client  106  may be bundled and paired together as a request/response pair. The request/response pair may be captured by and/or sent to the V-Lock  108 . The V-Lock  108  may then use the request/response pair to train the NLP providers  104 A-N. 
     Before turning to further FIGS., it is noted that in some embodiments, the lock-in avoidance system  100  may be installed on a network and may be able to access traffic (e.g., communications) between public/private cloud chatbot NLP providers (e.g., a [remote] cloud platform) and the enterprise client  110  code that is implementing the logic of the chatbot client  106 , e.g., a conversational agent. 
     In some embodiments, various connectors may exist that allow the lock-in avoidance system  100  to tap into specific cloud NLP providers  104 A-N (e.g., IBM® Watson Assistant, etc.). In some embodiments, the V-Lock  108  may have access to the network and hence can analyze all meta-data of the response provided by the NLP cloud service (e.g., the chatbot with NLP  102 ). It is noted that this allows the lock-in avoidance system  100  to extract the intent (e.g., what is being asked in the request, what is being answered in the response, etc.) and entities (e.g., is a user providing the request, where is the response coming from, etc.) identified by the cloud platform as well as additional relevant metadata like the confidence level (e.g., how well does the response answer the request, etc.); all these details can be used in the generation process for training the NLP providers  104 A-N, which in turn prevents lock-in now that the trained NLP providers  104 A-N can respond to requests that they could previously not respond/understand. 
     In some embodiments, the V-Lock  108  may use the collected details in two ways depending on its configuration, e.g.: 
     Train a plurality of other cloud platforms (e.g., NLP providers  104 A-N), automatically, by using the collected information (e.g., request/response pair). In such a configuration, the V-Lock  108  can determine, after a period of time, which of the tested cloud NLP services (e.g., NLP providers  104 A-N) is performing best by analyzing the confidence levels for the same training sets (e.g., after being trained with the request/response pair, when a new request comes in, does the NLP provider correctly respond); and 
     Attempt to replicate the behavior of the remote NLP in order to reduce the total cost of ownership by reducing the number of calls that a conversational agent/chatbot (e.g., chatbot client  106 ) is doing on the remote cloud NLP service (e.g. chatbot with NLP  102 ). 
     In some embodiments, to determine if the training of the NLP providers  104 A-N is over, the V-Lock  108  can run a parallel assessment with an existing NLP platform in order to confirm if the responses provided by the lock-in avoidance system  100  are similar from a confidence level with a real NLP platform (e.g., determine if the trained providers are responding within a threshold range (e.g., 90%, etc.) as the NLP platform where the request/response pair was generated/captured by the V-Lock  108 ). In some embodiments, if the response is within the confidence level, then the training is over and the V-Lock  108  can be used in production to train more NLP providers  104 A-N (which again, in return eliminates lock-in by allowing other NLP providers to now respond to requests that they before could not). 
     Referring now to  FIG.  1 B , illustrated is a block diagram of an internal enterprise network  120 , in accordance with aspects of the present disclosure. As depicted, the internal enterprise network  120  includes an enterprise client software  122 , an NLP service  124 , a V-Lock  130  (which may be the same or substantially similar to the V-Lock  108  in  FIG.  1 A ), and a service alternative  150 . 
     As further depicted, the V-Lock  130  is incorporated with a network interface  126  and a REST API  128 . The V-Lock  130  further includes an NLP sniffer  132 , a UI Robot  134 , a persistent storage  136 , API adapter(s)  138 , a(n) (adaptive) learning facility  140 , and a configuration  142 . 
     In some embodiments, the NLP sniffer  132  is a component that is an advanced sniffing solution that allows the V-Lock  130  to capture the REST API  128  traffic being exchanged on the internal enterprise network  120  between the NLP service  124  and the enterprise client software  122 . This allows the internal enterprise network  120  to tap into the communication and obtain additional information from metadata of each request and response between the NLP service  124  and the enterprise client software  122 . The information collected is then used for training additional on-line system (NLS) Service alternatives (e.g., service alternative  150 ). In some embodiments, the information collected may be: a user sentence, intent, entities, context variables, etc. The NLP sniffer  132  can understand the API of the NLP service  124  as it has different adaptors that are vendor specific. This is feasible as the number of main NLP cloud service vendors is limited (e.g., since there is a limited number of providers the adapters are easily identifiable as proprietary). 
     In some embodiments, the API adapter(s)  138  is/are component(s) that is/are “plug and play,” which allows the V-Lock  130  to understand additional REST APIs from other NLP vendors. After specific communication is captured by the NLP sniffer  132 , the API adapter(s)  138  may then identify key fields from a JSON as per the API documentation of the NLP service  124 . For example, the API adapter(s)  138  may identify that the name of the intent is located in the “Intent” property from the “data” object of a response. 
     In some embodiments, the (adaptative) learning facility  140  is a component used to approximate a correlation between intents (of requests) by approximating the next intent in a chain based on historical data (e.g., how a response to a request was correctly answered, what question in response to a request was asked next, etc.). In some embodiments, the (adaptative) learning facility  140  can be a neural network that is trained base on data captured from the internal enterprise network  120 . 
     In some embodiments, the (adaptative) learning facility  140  may also be able to extract ontology by capturing a REST API (e.g., the REST API  128 ) response where it is specified for a specific user sentence what are the identified intents, entities, and context variables (e.g., what is being solved, for who/by who, and why the request was sent, etc.). In some embodiments, all this information will be used during training, either of a local neural network, or of a remote NLP Service provider(s). It is noted that as there is a limited number of relevant NLP service provides, specific adapters and tooling can be developed in various embodiments. 
     In some embodiments, the UI Robot  134  is a component that may be optional, and the main purpose of the UI Robot  134  may be to execute operations on a user interface of a given cloud NLP service (e.g.,  124 , or service alternative  150 ) for which there is no REST API integration available. The number of existing NLP providers that don&#39;t offer REST API integration is very limited, however, Robotic Process Automation technology can be used to bypass any kind of limitation and allow full access to training features of these platforms. 
     In some embodiments, the persistent storage  136  is a component where messages exchanged between the enterprise client software  122  and the NLP service  124  may be persisted for later analysis. The persistence storage  136  can be implemented as a database, a file system, etc. Additionally, besides the messages, the persistent storage  136  may also be used to store any code generated, such as the resulting IBM® Watson Conversation workspaces, etc. 
     In some embodiments, the configuration  142  is a component that is used by a system administrator via a command line in order to specify various configuration parameters used by the internal enterprise network  120 . For example, the administrator may need to configure source IP, or source URL, for the NLP service provider  124  so that the NLP sniffer  132  can correctly capture traffic. Additional configuration parameters are also available, such as, a migration strategy, target similarity level, etc. 
     In some embodiments, the REST API  128  allows the V-Lock  130  to communicate with the alternative service  150  (e.g., an alternative NLP service provider(s)). In such an embodiment, custom coding is required in order for the V-Lock  130  to communicate with the remote services (e.g., service alternatives  150 ). Further, authentication and authorization are other concepts that relate to the REST API  128 ; the details are collected from the configuration  142 . 
     In some embodiments, the NLP service  124  is the main natural language processing service which needs to be replaced, or which needs to be called less, for reducing total cost of ownership. The V-Lock  130  may analyze the behavior of the NLP service  124  and attempt to completely remove it from the communication flow as soon as there is enough data for training of the service alternative  150  (and/or other NLP service providers/alternatives), and mimicked response results of the service alternative  150  are above a response threshold (e.g., the service alternative  150  has correctly responded to mimicked requests 95% of the time, etc.). 
     In some embodiments, the service alternative  150  is a (remote) NLP service component that represents one or more NLP service providers which will replace the main NLP service (e.g., the NLP service  124 ) after being trained by the V-Lock  130 . In some embodiments, the code for updating/migrating to the service alternative  150  is automatically deployed via the REST API  128  and/or via the UI Robot  134  in case the service alternative  150  requires manual deployment or software artifacts. 
     In some embodiments, the enterprise client software  122  is a conversational agent within the internal enterprise network  120 . In some embodiments, the enterprise client software  122  always uses an advanced custom code that allows proper integration with various enterprise services like SAP®, company databases, etc. In such an embodiment, this allows a conversational system (e.g., a chatbot) to deliver value to its users by allowing training and migration of request/response pairs. 
     Referring now to  FIG.  2   , illustrated a flowchart of an example method  200  for avoiding lock-in in the context of cognitive solutions (e.g., particularly in enterprise/enterprise cognitive solutions), in accordance with aspects of the present disclosure. In some embodiments, the method  200  may be performed by a processor (e.g., of the lock-in avoidance system  100  of  FIG.  1 A , of the internal enterprise network  120  of  FIG.  1 B , etc.). 
     In some embodiments, the method  200  begins at operation  202  where the processor analyzes, a communication associated with a simulation program (e.g., a chatbot, a simulation of a conversation, a conversation simulation, etc.). In some embodiments, the method  200  proceeds to decision block  204  where it is determined whether the simulation program is running (e.g., is a chatbot service/conversational agent/NLP service in current use). 
     If, at decision block  204  it is determined that the simulation program is not running, the method  200  may end. If, at decision block  204  it is determined that the simulation program is running, the method  200  may proceed to operation  206 , where the processor captures at least one request/response pair in the communication. 
     In some embodiments, the method  200  proceeds to operation  208 , where the processor stores the at least one request/response pair (e.g., in persistent storage). In some embodiments, the method  210  proceeds to operation  210 , where the processor trains at least one registered natural language (processing) providers (or neural network, or models associated with/used by the NLP providers) with the request/response pair. In some embodiments, after operation  210 , the method  200  may end. 
     In some embodiments, discussed below, there are one or more operations of the method  200  not depicted for the sake of brevity and which are discussed throughout this disclosure. Accordingly, in some embodiments, the processor may further determine if an amount of the at least one request/response pair is above an NLP threshold (e.g., an amount of request/response pairs is above a specific number that allows for training of an NLP service, etc.). 
     In some embodiments, if the amount of the at least one request/response pair is above the NLP threshold, each of the at least one registered natural language processing providers is trained. If the amount of the at least one request/response pair is not above the NLP threshold, the method  200  may loop back to operation  206  and continue to capture request/response pairs until the NLP threshold is met/exceeded. 
     In some embodiments, training the at least one registered natural language processing provider is automatic. In some embodiments, the processor may analyze an interaction (e.g., behavior) of an operator to capture the at least one request/response pair (e.g., the conversational agent/user/chatbot may be analyzed/monitored) and the processor may then simulate the interaction (e.g., for a subsequent interaction that is closely related to the analyzed interaction or to be used for training, etc.). 
     In some embodiments, the communication is application layer data that is analyzed by the processor on an application layer. In some embodiments, the processor may simulate remote cloud natural language processing services of the at least one natural language processing provider in an on-premise environment and predict a future action of the remote cloud natural language processing service (e.g., or provider or from a provider). 
     In some embodiments, the processor may generate one or more recommendations associated with the remote natural language processing services. The one or more recommendations may be based on the future action (e.g., and/or their performance, either predicted, or occurred/real). The processor may further provide one or more recommendations to the at least one natural language processing provider (or neural network or a user). That is, during training the provider/alternative service replacing an NLP provider/service may be provided with recommended responses for a request and when the recommended responses are accurate within a specified degree (e.g., 80%, or a correct number of responses within a certain timeframe), the provider is considered trained. 
     In some embodiments, the processor may reduce a total number of requests (or communications) to the remote cloud natural language processing services of the at least one natural language processing services (e.g., cloud NLP service providing a majority of the NLP services for an on-premise NLP service may be reduced by having the on-premise NLP service now being able to provide correct responses for various requests; in such a way, lock-in is mitigated due to the NLP service being able to adapt as a service expands). 
     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 disclosure 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 portion independence in that the consumer generally has no control or knowledge over the exact portion of the provided resources but may be able to specify portion 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. 
       FIG.  3 A , illustrated is a cloud computing environment  310  is depicted. As shown, cloud computing environment  310  includes one or more cloud computing nodes  300  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  300 A, desktop computer  300 B, laptop computer  300 C, and/or automobile computer system  300 N may communicate. Nodes  300  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  310  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  300 A-N shown in  FIG.  3 A  are intended to be illustrative only and that computing nodes  300  and cloud computing environment  310  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
       FIG.  3 B , illustrated is a set of functional abstraction layers provided by cloud computing environment  310  ( FIG.  3 A ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG.  3 B  are intended to be illustrative only and embodiments of the disclosure are not limited thereto. As depicted below, the following layers and corresponding functions are provided. 
     Hardware and software layer  315  includes hardware and software components. Examples of hardware components include: mainframes  302 ; RISC (Reduced Instruction Set Computer) architecture based servers  304 ; servers  306 ; blade servers  308 ; storage devices  311 ; and networks and networking components  312 . In some embodiments, software components include network application server software  314  and database software  316 . 
     Virtualization layer  320  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  322 ; virtual storage  324 ; virtual networks  326 , including virtual private networks; virtual applications and operating systems  328 ; and virtual clients  330 . 
     In one example, management layer  340  may provide the functions described below. Resource provisioning  342  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  344  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  346  provides access to the cloud computing environment for consumers and system administrators. Service level management  348  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  350  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  360  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  362 ; software development and lifecycle management  364 ; virtual classroom education delivery  366 ; data analytics processing  368 ; transaction processing  370 ; and avoiding lock-in in the context of cognitive solutions  372 . 
       FIG.  4   , illustrated is a high-level block diagram of an example computer system  401  that may be used in implementing one or more of the methods, tools, and modules, and any related functions, described herein (e.g., using one or more processor circuits or computer processors of the computer), in accordance with embodiments of the present disclosure. In some embodiments, the major components of the computer system  401  may comprise one or more CPUs  402 , a memory subsystem  404 , a terminal interface  412 , a storage interface  416 , an I/O (Input/Output) device interface  414 , and a network interface  418 , all of which may be communicatively coupled, directly or indirectly, for inter-component communication via a memory bus  403 , an I/O bus  408 , and an I/O bus interface unit  410 . 
     The computer system  401  may contain one or more general-purpose programmable central processing units (CPUs)  402 A,  402 B,  402 C, and  402 D, herein generically referred to as the CPU  402 . In some embodiments, the computer system  401  may contain multiple processors typical of a relatively large system; however, in other embodiments the computer system  401  may alternatively be a single CPU system. Each CPU  402  may execute instructions stored in the memory subsystem  404  and may include one or more levels of on-board cache. 
     System memory  404  may include computer system readable media in the form of volatile memory, such as random access memory (RAM)  422  or cache memory  424 . Computer system  401  may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system  426  can be provided for reading from and writing to a non-removable, non-volatile magnetic media, such as a “hard drive.” Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), or an optical disk drive for reading from or writing to a removable, non-volatile optical disc such as a CD-ROM, DVD-ROM or other optical media can be provided. In addition, memory  404  can include flash memory, e.g., a flash memory stick drive or a flash drive. Memory devices can be connected to memory bus  403  by one or more data media interfaces. The memory  404  may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of various embodiments. 
     One or more programs/utilities  428 , each having at least one set of program modules  430  may be stored in memory  404 . The programs/utilities  428  may include a hypervisor (also referred to as a virtual machine monitor), one or more operating systems, one or more application programs, other program modules, and program data. 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. Programs  428  and/or program modules  430  generally perform the functions or methodologies of various embodiments. 
     Although the memory bus  403  is shown in  FIG.  4    as a single bus structure providing a direct communication path among the CPUs  402 , the memory subsystem  404 , and the I/O bus interface  410 , the memory bus  403  may, in some embodiments, include multiple different buses or communication paths, which may be arranged in any of various forms, such as point-to-point links in hierarchical, star or web configurations, multiple hierarchical buses, parallel and redundant paths, or any other appropriate type of configuration. Furthermore, while the I/O bus interface  410  and the I/O bus  408  are shown as single respective units, the computer system  401  may, in some embodiments, contain multiple I/O bus interface units  410 , multiple I/O buses  408 , or both. Further, while multiple I/O interface units are shown, which separate the I/O bus  408  from various communications paths running to the various I/O devices, in other embodiments some or all of the I/O devices may be connected directly to one or more system I/O buses. 
     In some embodiments, the computer system  401  may be a multi-user mainframe computer system, a single-user system, or a server computer or similar device that has little or no direct user interface, but receives requests from other computer systems (clients). Further, in some embodiments, the computer system  401  may be implemented as a desktop computer, portable computer, laptop or notebook computer, tablet computer, pocket computer, telephone, smartphone, network switches or routers, or any other appropriate type of electronic device. 
     It is noted that  FIG.  4    is intended to depict the representative major components of an exemplary computer system  401 . In some embodiments, however, individual components may have greater or lesser complexity than as represented in  FIG.  4   , components other than or in addition to those shown in  FIG.  4    may be present, and the number, type, and configuration of such components may vary. 
     As discussed in more detail herein, it is contemplated that some or all of the operations of some of the embodiments of methods described herein may be performed in alternative orders or may not be performed at all; furthermore, multiple operations may occur at the same time or as an internal part of a larger process. 
     The present disclosure 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) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. 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 described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, 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 or network interface 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 for carrying out operations of the present disclosure 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 disclosure. 
     Aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. 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. 
     These computer readable program instructions may be provided to a processor of a computer, or other programmable data processing apparatus 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 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 may also be loaded onto a computer, 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 disclosure. 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 blocks 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. 
     The descriptions of the various embodiments of the present disclosure have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. 
     Although the present disclosure has been described in terms of specific embodiments, it is anticipated that alterations and modification thereof will become apparent to the skilled in the art. Therefore, it is intended that the following claims be interpreted as covering all such alterations and modifications as fall within the true spirit and scope of the disclosure.