Serverless composition of functions into applications

A processor may receive a query from a user. The query may include one or more portions. The processor may identify a primary function. The processor may determine to segment the primary function into two or more subsidiary functions. The processor may process a first portion of the query with a first subsidiary function. The processor may display a processed outcome of the query to the user.

BACKGROUND

The present disclosure relates generally to the field of functions-as-a-service (FaaS), and more specifically to managing cloud native applications in a way that shift the burden of managing the servers to a cloud platform operator (i.e., serverless computing).

Serverless computing, of which functions-as-a-service is one example, has rampantly become a cloud-based tool for supporting scalable, event-driven applications. FaaS computing allows for the use of short-running, generally stateless, functions that can be triggered by events.

SUMMARY

Embodiments of the present disclosure include a method, computer program product, and system for processing queries by composing multiple stateless or stateful functions together. A processor may receive a query from a user. The query may include one or more portions. The processor may identify a primary function. The processor may determine to segment the primary function into two or more subsidiary functions. The processor may process a first portion of the query with a first subsidiary function. The processor may display a processed outcome of the query to the user.

DETAILED DESCRIPTION

Aspects of the present disclosure relate generally to the field of cloud computing, and more specifically to severless computing; in general, serverless refers to shifting the burden of the operating and managing servers from the consumer to the cloud provider, and offering pay-as-you and for what you use billing, at fine grained time increments, minimizing or not charging for idle time, and scaling resources automatically based on application needs. 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.

A user (e.g., a developer, a customer, etc.) may desire to manage overhead by not directly specializing hardware (e.g., creating or utilizing specific servers and/or computers for a single purpose) to host a particular application. The user may turn to cloud-based serverless hosting, which may allow the user the host their particular application without the need for explicitly provisioning specialized hardware. The user may still desire to manage overhead by not having their particular application constantly running (thereby increasing the overhead costs of managing the application) on the (cloud-based) server hosting the particular application. The user may turn to reactive or trigger-based execution of fine grained functions, which are generally stateless, to diminish overhead (e.g., by only running on a processor when an event trigger the function) and additionally increase the processing speed and efficiency of their particular application (e.g., by utilizing the parallel nature associated with stateless functions).

In some embodiments, a processor may receive a query from a user. In some embodiments, the query may include one or more portions. The processor may identify a primary function. In some embodiments, the primary function may be an application. The processor may determine to segment the primary function into two or more subsidiary functions. In some embodiments, the two or more subsidiary functions may each process a portion of the query (e.g., a first subsidiary function may process a first portion, a second subsidiary function may process a second portion, etc.). In some embodiments, the processor may process a first portion of the query with a first subsidiary function. The processor may display a processed outcome of the query (e.g., the outcome generated after the query has been fully processed by the two or more subsidiary functions) to the user.

In some embodiments, the order in which the processor may process the outcome of the query may or may not matter. For example, two or more portions of a query may sequentially be processed, e.g., a result may be generated for the first portion processed by a first subsidiary function and stored for use by a second subsidiary function to process a second portion. Or, the first subsidiary function may process the first portion in parallel to the second subsidiary function processing the second portion.

For example, a user operating a laptop that is connected to the Internet may input a query into a cloud server, which limits the processing of queries to one minute. The user may be searching for a detailed analysis of the most efficient table turnover rate for a restaurant. The query for “most efficient table turnover rate for a restaurant” may trigger a processor in the cloud server to search the entirety of the cloud server's storages for a primary function associated with table turnover rates. The primary function may be identified with a metadata tag indicating the table turnover rate association. The processor may determine that the primary function is too large to process the query within one minute because the primary function, in addition to providing table turnover rates, may produce other restaurant operating diagnostics (e.g., such as price points of food, drinks, etc.).

The processor may, in response to determining that the primary function is too large to process the query within one minute, segment the primary function into two or more subsidiary functions. The processor may identify that one of the subsidiary functions is the function for table turnover rates and process the query using table turnover rate function. The processor may then display the table turnover rate to the user.

In some embodiments, the processor may determine to segment the primary function into two or more subsidiary functions by receiving a callback after completion of the primary function. That is, the processor may receive an indication that the primary function did not process in an allotted amount of time (e.g., is too large to process as a single function, etc.) and that the primary function should be “called-back” in the form of the two or more subsidiary functions. In some embodiments, the processor may receive an indication that the primary function was fully processed, thus, the primary function does not need to be segmented and the processing of the primary function may end.

In some embodiments, upon receiving a query, the processor may search the cloud server directly for one or more functions (e.g., primary, subsidiary, etc.) associated with the query and compose the one or more functions together to process the query. That is, in some embodiments, the processor may not segment a primary function; instead, the processor may begin processing a query by identifying a function that may process a portion of the query and use the output of the function from the portion of the query to identify a second function, and so forth, until the query is fully processed.

In some embodiments, processor may identify that the first portion of the query has been processed. In some embodiments, the processor may identify that the first portion of the query has been processed by the first subsidiary function generating a first output. In some embodiments, the processor may determine that a second portion of the query remains unprocessed. In some embodiments, the processor may process the second portion of the query with a second subsidiary function. In some embodiments, the processor may determine that the second portion of the query remains unprocessed by identifying that the first output is a first input for the second subsidiary function.

In some embodiments, the processor may identify that the second portion of the query has been processed. In some embodiments, the processor may identify that the second portion of the query has been processed by the second subsidiary function generating a second output. In some embodiments, the processor may determine that a third portion of the query remains unprocessed. In some embodiments, the processor may process the third portion of the query with a third subsidiary function. In some embodiments, the process may generate the processed outcome of the query.

For example, a processor in cloud server may receive a query from a user looking to find the reflux ratio of a distillation column (e.g., the amount of distillate collected in a receiver versus the amount of product re-distilled) while only knowing the percent of vapors is 0.4 (e.g., V=0.4). To find the reflux ratio, the processor may search the cloud server for a primary function relating to reflux ratios. The processor may identify the primary function as having multiple sets of subsidiary functions relating to reflux ratios and determine to separate the subsidiary functions. The subsidiary functions may be that Reflux Ratio=Liquid/Distillate (i.e., R=L/D); Liquid+Vapor=1 (i.e., L+V=1); and Distillate=Vapor/Liquid (D=V/L).

The processor may the begin processing a portion of the query using the subsidiary function of L+V=1 (e.g., first subsidiary function) and determine an output of the subsidiary function is L=0.6 (i.e., L=1-V, where V is 0.4). The processor may identify that L=0.6 may be used by the subsidiary function of D=V/L (e.g., the second subsidiary function).

The processor may then process a second portion of the query using the subsidiary function D=V/L and determine an output of the second subsidiary function is D=0.67 (e.g., D=0.4/0.6). The processor may identify that D=0.67, now with L=0.6 may be used by the subsidiary function of R=L/D (e.g., the third subsidiary function).

The processor may then process a third portion of the query using the subsidiary function of R=L/D, knowing L=0.6 and D=0.67. The processor may determine that the Reflux Ratio=0.896 (e.g., R=0.6/0.67). The program may identify 0.896 as the processed outcome of the reflux ratio query and display the reflux ratio of the distillation column to a user.

In some embodiments, the processor may register (e.g., save, tag for future use, indicate with an identifier, etc.) the composed together subsidiary functions as an application. For example, the processor may identify the subsidiary function of Liquid+Vapor=1, composed with the subsidiary function of Distillate=Vapor/Liquid, composed with the subsidiary function of Reflux Ratio=Liquid/Distillate is an application.

In some embodiments, the processor may identify the primary function by accessing a database (e.g., a cloud-server, a repository, a computer hard drive, etc.). In some embodiments, the database may include source code for one or more primary functions. In some embodiments, the processor may identify the primary function as being able to process the query. In some embodiments, the processor may identify the primary function as being able to process the query by identifying that the primary function has an identifier associated with a primary article (e.g., word, image, etc.) of the query.

For example, a user may input a query into a program connected to a cloud-environment and a processor in a part of the cloud environment may receive the query. The query may be “how to make cookies.” The processor may parse the query into individual words and be programmed to disregard functional words (e.g., “how” and “to” from the query) and focus on verbs (e.g., “make” from the query) and nouns (e.g., “cookies” from the query).

The processor may identify the primary article of the query is “making cookies” and access the cloud environment to which the processor belongs. The processor may search the cloud environment for primary functions that are tagged (e.g., with metadata, an indicator, etc.) with cookie making tutorials and the like. In some embodiments, the processor may identify the primary functions most associated with the query and automatically process, in parallel (e.g., at the same time, simultaneously, etc.), the query using each primary function. In some embodiments, the processor may display a processed outcome of the query to the user based on which primary function was fastest at processing the query.

In some embodiments, the processor may determine to segment the primary function into two or more subsidiary functions by determining that the primary function will not process the query within a time threshold. In some embodiments, the processor may determine that the primary function will not process the query within the time threshold by executing the primary function in a sandbox environment. In some embodiments, the processor may determine that the primary function will not process the query within the time threshold by executing the primary function.

For example, a processor may be queried to determine the circumference of a circle having a radius of 5 (e.g., R=5). Additionally, the processor may have a time constraint imposed, which only allows the processor to process functions that take less than 0.001 seconds to process. The processor may identify (e.g., by accessing a database and identifying associated tags) two primary functions associated with finding circumference. The two primary functions may be: Circumference=pi*Diameter (e.g., C=π*D) or Circumference=2*pi*Radius (e.g., C=2*π*R). The processor may simultaneously process (e.g., execute) both primary functions to find the circumference.

The processor may identify that during the processing, that the processor only has information for the radius, and the processing for the circumference using diameter now takes an additionally step (e.g., a subsidiary function of multiplying 2*R) before using the pi*Diameter primary function. The processor may finish processing the pi*Diameter primary function and determine that it takes longer than 0.001 seconds (e.g., the time threshold). The processor may then choose to only process the query using the 2*pi*Radius primary function to find the circumference because it is the faster equation to process.

In some embodiments, simultaneously processing two or more functions (e.g., primary and/or subsidiary functions) at once and proceeding with the fastest function may reduce the cost of running an application as a cloud-based service. For example, simultaneously processing two or more functions may utilize the same costs (e.g., hardware, economic, etc.) associated with that of processing one function because the resources already being allocated to on function do not require any additional resources to process another function simultaneously. Additionally, if one function is found to be more efficient (e.g., processed faster), the total cost of processing the query may be mitigated because less hardware usage is required.

In some embodiments, the processor may learn which functions (e.g., primary and/or subsidiary) are more efficient depending on inputted data. Following the example above, the processor may have learned that processing for the circumference of a circle when only having the radius, only requires the primary function of 2*pi*Radius. The processor may no longer simultaneously try to process the primary functions of 2*pi*Radius and pi*Diameter. In some embodiments, the learning of which functions to forgo may lower costs (e.g., computing resource costs such as processing time, memory usage, etc. and economic costs) associated with running an application.

In some embodiments, the processing of the query may be done in one language (e.g., monoglot) using any programming language, such as, but not limited to JAVA, SWIFT, PYTHON, etc.), or as native binaries. In some embodiments, one of the subsidiary functions used to process the query may be a function relating to a programming language that is different from the other programming languages used by the other subsidiary function(s). The processor may run subsidiary functions that use different programming languages at the same time or on specialized hardware if it is determined by a user or the processor that it is more effective to do so. This may allow for the query to be more efficiently and quickly processed.

For example, one subsidiary function may be programmed in JAVA, whereas another subsidiary function, which may be a part of the same query, may be programmed in PYTHON; this may be dictated by the original coder finding it easier to program one function in one language and anther function in another language. The processor may decide to simultaneously process a first portion of the query using JAVA and a second portion of the query using PYTHON. This may allow the processor to more efficiently and more quickly relay at processed outcome of the query to a user instead of transforming the output of one function in one language to another language in order for the output to be used as an input in the other language.

Referring now toFIG. 1A, depicted is an example of processing a function-as-a-service application100, in accordance with embodiments of the present disclosure. In some embodiments, the application100may be stored in a cloud-computing environment. In some embodiments, the application100may be a primary function. In some embodiments, the application100may include (e.g., be comprising of) a function102(e.g., a primary function). In some embodiments, the function102may include a read state104, a compute state106, and a write state108. In some embodiments, each state104-108may be processed on a respective processor included in the cloud-computing environment (e.g., a first processor for the read state104, a second processor for the compute state106, and a third processor for the write state108).

In some embodiments, the application100may receive a query and the query may trigger the application100to begin processing the query using the function102. In some embodiments, upon determining to process the query using the function102, all three processors associated with the states104-108may be activated and dedicated to the processing of the query. In some embodiments, the three processors may be active for the entire processing of the query using the function102.

That is, for example, as the query is being processed, the read state104running on the first processor may be the only state processing a first portion of the query, however, the other two processors are still active. This may hinder the cloud-computing environment from allocating the processors to perform another task while waiting for the read state104to process its portion of the query.

In some embodiments, the read state104may finish processing the first portion of the query, outputting a result that may allow the compute state106to begin processing a second portion of the query on the second processor. In some embodiments, the first processor may still be active even though the read state104has finished processing the first portion of the query. Additionally, the third processor may be active, waiting for an output from the compute stage106in order to invoke the write state108.

In some embodiments, the compute state106may finish processing the second portion of the query on the second processor and output a result that may allow the write state108to begin processing a third portion of the query on the third processor. In some embodiments, the first and second processors may still be active even though the read state104and the compute state106have finished processing the first and second portions of the query. This again may hinder the ability of the cloud-computing environment to allocate the processors elsewhere when not being used for processing of data.

In some embodiments, the write state108may finish processing the third portion of the query on the third processor and the function102may have fully processed the query. In some embodiments, the application100may identify that the query is fully processed and output the result of the query to a user. During the time of the full processing, all three processors may be activated and dedicated to the one query regardless of if one or more of the processors are actively processing a portion of the query.

Referring now toFIG. 1B, depicted is an example of processing the function-as-a-service application100ofFIG. 1Aafter the primary function has been split into three distinct functions112,116, and120, in accordance with embodiments of the present disclosure. Like reference numerals are used to designate like parts of in the accompanying drawings. In some embodiments, the application100may be stored in a cloud-computing environment. In some embodiments, the application100may be a primary function.

In some embodiments, the application100may include (e.g., comprising of) a first function112, a second function116, and a third function120. In some embodiments, the first through third functions112,116, and120may be subsidiary functions of the function102ofFIG. 1A. In some embodiments, the application100, upon receiving a query, may separate the function102into the first through third functions112,116, and120by corresponding each of the first through third functions112,116, and120respectively to one of the states104,106, and108.

In some embodiments, the function102may be prescribed when to separate into the third through third functions112,116, and120and which states the first through third functions112,116, and120should correspond to. For example, a programmer may code the function102to only separate into subsidiary functions upon a certain input. Additionally, upon the certain input the subsidiary functions may be designated to correspond to certain states (e.g., the first function112could correspond to the compute state106, etc.) In some embodiments, the first function112may include the read state104ofFIG. 1A, the second function116may include the compute state106ofFIG. 1A, and the third function120may include the write state108ofFIG. 1A.

In some embodiments, the first function112, the second function116, and the third function may be the same or similar function as the function102as detailed inFIG. 1A. In some embodiments, each state104-108of the function102as detailed inFIG. 1Amay be treated as a separate function and processed, respectively, as the first function112, the second function116, and the third function120of the presentFIG. 1B. In some embodiments, the first function112, the second function116, and the third function120ofFIG. 1Bmay be treated like and processed as the function102ofFIG. 1A.

In some embodiments, each state104,106, and108may be processed on a respective processor included in the cloud-computing environment (e.g., a first processor for the read state104, a second processor for the compute state106, and a third processor for the write state108). In some embodiments, the application100may receive a query and the query may trigger the application100to separate each state104,106, and108into the first through third functions112,116, and120. In some embodiments, the application100may begin processing the query using the first function112. In some embodiments, upon determining to process the query using the first function112, only the first processor associated with the read state104may be activated and dedicated to the processing a first portion of the query. In some embodiments, the second and third processors remain inactive for application100during the processing of the first portion of query, allowing the second and third processors to be allocated for other applications and/or computing tasks until the first portion of the query is processed, greatly increasing the efficiency of the cloud-computing environment.

In some embodiments, the read state104may finish processing the first portion of the query, outputting a result that may trigger the second function116and allow the compute state106to begin processing a second portion of the query on the second processor. In some embodiments, the first processor may still be excused (e.g., terminated from use by the application100) after the read state104has finished processing the first portion of the query. Allowing the first processor to be allocated for other applications and/or computing tasks. Additionally, the third processor may remain inactive, waiting for an output from the compute stage106in order to invoke the write state108.

In some embodiments, the compute state106may finish processing the second portion of the query on the second processor and output a result that may trigger the third function120and allow the write state108to begin processing a third portion of the query on the third processor. In some embodiments, the first and second processors may be excused after the read state104and the compute state106have finished processing the first and second portions of the query. This again, may increase the efficiency of the cloud-computing environment by allowing the cloud-computing environment to allocate the processors elsewhere when not being used for the application100.

In some embodiments, the write state108may finish processing the third portion of the query on the third processor and the function120may have fully processed the query. In some embodiments, the application100may identify that the query is fully processed and output the result of the query to a user. During the time of the full processing, only one of the three processors may be activated and dedicated to processing a portion of the query at a time.

In some embodiments, the processor may not invoke the application100at all. That is, one of the three processors associated with each state104,106, and108may be invoked by the cloud-computing environment in response to the cloud-computing environment receiving a query. The chosen processor may process a portion of the query and if a result is achieved with the portion of the query, another potion of the query may be processed on another of the three processors. In some embodiments, the cloud-computing environment may identify in which order the states104,106, and108produced an outcome for the query and the cloud-computing environment may store the order of the states104,106,108as their respective functions112,116, and120as the application100. That is, the application100may not exist until it is generated by the cloud-computing environment determining which functions and/or states produce an outcome to a query.

Referring now toFIG. 2, illustrated is an example computing environment200for processing and displaying an outcome of a query, in accordance with embodiments of the present disclosure. In some embodiments, the computing environment200may include a computer202, a cloud206, and a computer222. In some embodiments, the computer202and the computer222may be the same computer. In some embodiments, the cloud206may include and/or be hosted on a remotely-located server or servers (e.g., processors, computers, etc.).

In some embodiments, the computer202may include a query204, which may be inputted by a user. In some embodiments, the cloud206may include a first database226and a second database228. In some embodiments, the first database226and the second database228may be partitioned using virtual machines. In some embodiments, the first database may include a first primary function208A, a second primary function208B, and a third primary function208C. In some embodiments, the second database228may include a first function210, a second function212, a third function214, a fourth function216, a fifth function218, and a sixth function220. In some embodiments, the second database228may only be accessed through the first primary function208A. In some embodiments, the first through sixth functions210-220may be subsidiary functions of the first primary function208A. In some embodiments, the computer222may include a display224(e.g., a touch screen, an LCD/LED screen, etc.).

In some embodiments, the computer202may be operated by a user, and the user may input the query204. The query204may be transmitted to and/or received by a processor associated with the cloud206. In some embodiments, the processor may determine that the query204is directed to a primary article and the processor may access the first database226. The processor may identify, using tags associated with the first through third primary functions208A-C, that the primary article of the query is addressed by the first primary function208A. In some embodiments, the query204may include a bypass instruction which may allow the processor to forgo identifying any primary function associated with the specific article of the query204, and proceed directly to the first through sixth functions210-220that may be used to process the query204.

In some embodiments, after identifying the first primary function208A as being associated with the primary article of the query204the processor may access the first rule208A and process the query204by using one or more of the first through sixth functions210-220. In some embodiments, the processor may begin processing the query204by using the first function210. In some embodiments, the first function210may process a first portion of the query204, and the first function210may generate an outcome for the first portion of the query204that may act as a trigger for the fourth function216.

In some embodiments, the processor may continue to process the query204by using the fourth function216. In some embodiments, the fourth function216may process a second portion of the query204, and the fourth function216may generate an outcome for the second portion of the query204that may act as a trigger for the fifth function218.

In some embodiments, the processor may continue to process the query204by using the fifth function218. In some embodiments, the fifth function218may process a third portion of the query204, and the fifth function218may generate an outcome for the third portion of the query204that may act as a trigger for the third function214.

In some embodiments, the processor may finish processing the query204using the third function214. In some embodiments, the third function214may process a fourth portion of the query204, and the third function214may generate a processed outcome for the query204. In some embodiments, the processor may send the processed outcome to the computer222, and the computer222may display the processed outcome to the user on the display224.

In some embodiments, the processor may determine in which order to process the functions210-220based on the outcome of the processed function. That is, the processor may not know which function will be processed next until an outcome is determined for the previous function and the outcome is used as a trigger for the subsequent function. In some embodiments, not all the functions210-220associated with the first rule208A may be processed. Different functions of the function210-220may generated outcomes that are not usable as triggers by any of the functions210-220.

For example, the query204may be about lathing metal and the first rule208A may be about lathing. Each function210-220may be associated to the rule and identify certain facets of lathing, however the second function212and the sixth function220may only deal with lathing wood. Therefore, they were not triggered to be processed because they did not deal with metal lathing techniques.

In some embodiments, the first through sixth functions210-220may be a part of a state-machine (e.g., a processor), and as one portion of the query is processed with one of the functions, the state machine may transition to a new function (e.g., a level that is one of the remaining one through sixth functions210-220). In some embodiments, when transitioning to the new function, the previous function may terminate completely, which in turn may reduce costs associated with processing the query.

Referring now toFIG. 3, illustrated is a flowchart of an example method300for processing a query using multiple subsidiary functions, in accordance with embodiments of the present disclosure. In some embodiments, the method300may be performed by a processor (e.g., on a computer, server, cloud-server, etc.).

In some embodiments, the method300may begin at operation302. At operation302, the processor may receive a query from a user. In some embodiments, the method300may proceed to operation304. At operation304, the processor may identify a primary function from a database.

In some embodiments, the method300may proceed to decision block306. At decision block306, the processor may determine if the primary function should be segmented into two or more subsidiary functions. In some embodiments, the processor may determine to segment the primary function into two or more subsidiary functions by determining that the primary function will not process the query within a time threshold.

For example, a processor located on a sever may receive a query for “Transitioning a Laplace Transform from Taylor Series,” and have a 1 second threshold for outputting a response to the query. The processor may begin processing the query by identifying a primary article of the query as “Taylor Series” and analyze the server for data regarding Taylor Series. The processor, after narrowing the search to Taylor Series may then analyze the narrowed search for sub-information regarding “Laplace Transform.” The processor may identify a technique (e.g., a primary function) for transitioning a Taylor Series into a Laplace Transform and execute the technique only to identify that the time threshold will be exceeded. The processor may then determine to separate the technique into three steps (e.g., subsidiary functions).

In some embodiments, the processor may determine if each subsidiary function will process a portion of the query within the time threshold. In some embodiments, at decision block306the processor may determine to segment the primary function even if only a portion of the subsidiary functions are needed to process the query. For example, the processor may receive a query for “finding the enthalpy of a reaction.” The processor may access a science database and identify a primary function in the database relating to “thermodynamics,” which may include enthalpy equations, entropy equations, fugacity equations, etc. (e.g., subsidiary functions). The processor may determine that although the primary function of “thermodynamics,” can eventually process the query using all the equations, that only the enthalpy equations is needed. The processor may then segment the primary function of “thermodynamics” into the separate equations and proceed to only process the query with the enthalpy equation.

If at decision block306, the processor determines that the primary function should not be segmented, the processor may process the query using the primary function and display a processed outcome of the query to the user. If, at decision block306, the processor determines that the primary function should be segmented, the method300may proceed to operation308.

At operation308, the processor may segment the primary function into two or more subsidiary functions. In some embodiments, the method300may proceed to operation310. At operation310, the processor may process a portion of the query using one of the two or more subsidiary functions. In some embodiments, the method300may proceed to decision block312, which will be discussed further in regard toFIG. 4.

At decision block312, the processor may determine if each portion of the query has been processed. For example, the query may be split into three portions, each portion being associated with a subsidiary function associated with the primary function and two subsidiary functions acting as a trigger for one of the other subsidiary functions, or one subsidiary function acting as a trigger to display the processed outcome of the query.

In some embodiments, if the processor determines at decision block312that each portion of the query has not been processed, the processor may repeat operations308,310and decision block312until each portion of the query has been processed. The processor may repeat operation308, in order to determine if more subsidiary functions are needed to process the entirety of the query. In some embodiments, if the processor determines at decision block312that each portion of the query has been processed, the method300may proceed to operation314. At operation314, the processor may display a processed outcome of the query to the user. In some embodiments, after operation314, the method300may end.

Referring now toFIG. 4, illustrated is a flowchart of an example method400for determining if each portion of a query has been processed, in accordance with embodiments of the present disclosure. In some embodiments, the method400may begin at operation402. At operation402, the processor, as previously mentioned in regard to decision block312ofFIG. 3, may initiate another of the two or more subsidiary functions (e.g., a second, third, fourth subsidiary function, etc.) after determining that each portion of the query has not yet been processed.

In some embodiments, the method400may proceed to operation404. At operation404, the processor may begin processing another portion of the query (e.g., a second, third, fourth portion, etc.) with the other subsidiary function. In some embodiments, the method400may proceed to decision block406. In some embodiments, at decision block406, the processor may determine if the other portion of the query will be processed within a certain time (e.g., a time constraint, a threshold, etc.). The certain time may be provided by a user to limit unnecessary waiting by the user. In some embodiments, a user may only desire an output to the query and may provide no time constraint for processing the query.

In some embodiments, the processor may determine if the other portion of the query will be processed within the certain time by executing the portion of the query using a subsidiary function and identifying if the query was processed within the certain time or not. In some embodiments, the processor may determine if the other portion of the query will be processed within the certain time by identifying that the portion of the query is too large to be processed within the certain time.

If, at decision block406, it is determined by the processor that the other portion of the query will not be processed within the certain time, the processor may repeat operations402,404, and decision block406. In some embodiments, the processor may repeat operations402,404, and decision block406because a user has input that there is not time constraint and that an output for the query is needed. That is, the processor may continue, to find an output indefinitely until an output is found or until the user explicitly stops the reiterating of operations402,404, and decision block406. In some embodiments, if it is determined by the processor that the other portion of the query will not process within the certain time, the method400may end and the user may be provided with an “error” message indicating that an outcome to the query could not be produced within the certain time. This may allow the user to limit the time they spend on finding an outcome and move to another query.

In some embodiments, if, at decision block406, it is determined by the processor that the other portion of the query will process within the certain time, the method400may proceed to operation408.

At operation408, the processor may finish processing the other portion of the query with the other subsidiary function. In some embodiments, the method400may proceed to decision block410. At decision block410the processor may, again, regarding decision block312ofFIG. 3, determine if all portions of the query have been processed. If, at decision block410it is determined that all portions of the query have not been processed, the processor may repeat operation402, operation404, decision block406, operation408, and decision block410(e.g., the entire method400) until all portions of the query have been processed. In some embodiments, if it is determined by the processor at decision block410that all portions of the query have been processed, the method400may end.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Referring now toFIG. 5, illustrative cloud computing environment510is depicted. As shown, cloud computing environment510includes one or more cloud computing nodes500with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone500A, desktop computer500B, laptop computer500C, and/or automobile computer system500N may communicate. Nodes500may 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 environment510to 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 devices500A-N shown inFIG. 5are intended to be illustrative only and that computing nodes500and cloud computing environment510can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

Hardware and software layer600includes hardware and software components. Examples of hardware components include: mainframes602; RISC (Reduced Instruction Set Computer) architecture based servers604; servers606; blade servers608; storage devices610; and networks and networking components612. In some embodiments, software components include network application server software614and database software616.

Virtualization layer620provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers622; virtual storage624; virtual networks626, including virtual private networks; virtual applications and operating systems628; and virtual clients630.

Workloads layer660provides 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 navigation662; software development and lifecycle management664; virtual classroom education delivery666; data analytics processing668; transaction processing670; and mobile desktop672.

Referring now toFIG. 7, shown is a high-level block diagram of an example computer system701that 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 system701may comprise one or more CPUs702, a memory subsystem704, a terminal interface712, a storage interface716, an I/O (Input/Output) device interface714, and a network interface718, all of which may be communicatively coupled, directly or indirectly, for inter-component communication via a memory bus703, an I/O bus708, and an I/O bus interface unit710.

The computer system701may contain one or more general-purpose programmable central processing units (CPUs)702A,702B,702C, and702D, herein generically referred to as the CPU702. In some embodiments, the computer system701may contain multiple processors typical of a relatively large system; however, in other embodiments the computer system701may alternatively be a single CPU system. Each CPU702may execute instructions stored in the memory subsystem704and may include one or more levels of on-board cache.

One or more programs/utilities728, each having at least one set of program modules730may be stored in memory704. The programs/utilities728may 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. Programs728and/or program modules730generally perform the functions or methodologies of various embodiments.

Although the memory bus703is shown inFIG. 7as a single bus structure providing a direct communication path among the CPUs702, the memory subsystem704, and the I/O bus interface710, the memory bus703may, 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 interface710and the I/O bus708are shown as single respective units, the computer system701may, in some embodiments, contain multiple I/O bus interface units710, multiple I/O buses708, or both. Further, while multiple I/O interface units are shown, which separate the I/O bus708from 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.

It is noted thatFIG. 7is intended to depict the representative major components of an exemplary computer system701. In some embodiments, however, individual components may have greater or lesser complexity than as represented inFIG. 7, components other than or in addition to those shown inFIG. 7may be present, and the number, type, and configuration of such components may vary.