Patent Publication Number: US-11650983-B2

Title: Selecting an optimal combination of systems for query processing

Description:
BACKGROUND 
     Technical Field 
     The present invention relates generally to information processing and, in particular, to selecting an optimal combination of systems for query processing. 
     Description of the Related Art 
     Currently, there are many query engine/framework choices that can be selected and employed by a user. Such query engines/frameworks can include, but are not limited to, Spark, Tez®, Impara, Presto, and so forth. Similarly, execution runtimes can also involve several choices. Such execution runtimes can include, but are not limited to, JVM® (OpenJDK, Open J9, and so forth), and so forth. These frameworks and runtimes are widely used in recent big data processing eco-systems. 
     A system and runtime can be chosen for our analytical query workloads (e.g., Spark and OpenJDK, respectively). However, another system is rarely selected even if another system could achieve better performance (e.g., Tez and J9). 
     Hence, a problem exists in that no single combination of systems (i.e., a couple of query engines and JVM) is best for all queries. However, it is difficult to make a decision regarding which system should be used for a given query, since the decision can depend on factors including, but not limited to, the underlying system, configuration, characteristics of the query, engine and runtime, and so forth. Accordingly, there is a need for a way to select an optimum combination of systems for query processing. 
     SUMMARY 
     According to aspects of the present invention, a computer-implemented method is provided for generating a classification model configured to select an optimal execution combination for query processing. The method includes providing, to a processor, a plurality of training queries and a plurality of different execution combinations for executing the plurality of training queries. Each of the plurality of different execution combinations involve a respective one of a plurality of different query engines and a respective one of a plurality of different runtimes. The method further includes extracting, by the processor from a set of Directed Acyclic Graphs (DAGs) using a set of Cost-Based Optimizers (CBOs), a set of feature vectors for each of the plurality of training queries. The method also includes adding, by the processor to each of merged feature vectors a respective label indicative of the optimal execution combination based on actual respective execution times of the plurality of different execution combinations, to obtain a set of labels. The method additionally includes training, by the processor, the classification model by learning the set of merged feature vectors with the set of labels. 
     According to other aspects of the present invention, a computer program product is provided for generating a classification model configured to select an optimal execution combination for query processing. The computer program product includes a non-transitory computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processor of a computer to cause the computer to perform a method. The method includes providing, to a processor, a plurality of training queries and a plurality of different execution combinations for executing the plurality of training queries. Each of the plurality of different execution combinations involve a respective one of a plurality of different query engines and a respective one of a plurality of different runtimes. The method further includes extracting, by the processor from a set of Directed Acyclic Graphs (DAGs) using a set of Cost-Based Optimizers (CBOs), a set of feature vectors for each of the plurality of training queries. The method also includes adding, by the processor to each of merged feature vectors a respective label indicative of the optimal execution combination based on actual respective execution times of the plurality of different execution combinations, to obtain a set of labels. The method additionally includes training, by the processor, the classification model by learning the set of merged feature vectors with the set of labels. 
     According to yet other aspects of the present invention, a system is provided for generating a classification model configured to select an optimal execution combination for query processing. The system includes a processor, configured to access a plurality of training queries and a plurality of different execution combinations for executing the plurality of training queries. Each of the plurality of different execution combinations involve a respective one of a plurality of different query engines and a respective one of a plurality of different runtimes. The processor is further configured to extract, from a set of Directed Acyclic Graphs (DAGs) using a set of Cost-Based Optimizers (CBOs), a set of feature vectors for each of the plurality of training queries. The processor is also configured to add, to each of merged feature vectors, a respective label indicative of the optimal execution combination based on actual respective execution times of the plurality of different execution combinations, to obtain a set of labels. The processor is additionally configured to train the classification model by learning the set of merged feature vectors with the set of labels. 
     These and other features and advantages will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The following description will provide details of preferred embodiments with reference to the following figures wherein: 
         FIG.  1    is a block diagram showing an exemplary processing system to which the invention principles may be applied, in accordance with an embodiment of the present invention; 
         FIG.  2    is a block diagram showing an exemplary system, configured to select a combination of systems for query processing, in a training stage, in accordance with an embodiment of the present invention; 
         FIG.  3    is a flow diagram showing an exemplary method for training a system that is configured to select a combination of systems for query processing, in accordance with an embodiment of the present invention; 
         FIG.  4    is a block diagram further showing the system of  FIG.  2   , in a scheduling stage, in accordance with an embodiment of the present invention; 
         FIG.  5    is a flow diagram showing an exemplary method for scheduling query processing by selecting a combination of systems for query processing, in accordance with an embodiment of the present invention; 
         FIG.  6    is a block diagram showing another exemplary system, configured to select a combination of systems for query processing, in accordance with an embodiment of the present invention; 
         FIG.  7    shows a cloud computing environment, in accordance with an embodiment of the present invention; and 
         FIG.  8    shows abstraction model layers, in accordance with an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     The present invention is directed to selecting an optimum combination of systems for query processing. 
     In an embodiment, various execution combinations formed from a respective query engine (from among a set of different query engines) and a respective runtime (from among a set of different runtimes) are evaluated for a given query in order to select an optimal combination to execute the given query. In this way, each query can be processed by a most suitable execution combination from among a set of different execution combinations. Accordingly, various embodiments of the present invention can adaptively maximize query performance and can also minimize available cloud resources, among a myriad of other advantages readily apparent to one of ordinary skill in the art, given the teachings of the present invention provided herein. 
     Hence, in an embodiment, the present invention can involve two functions as follows: (1) creating a classification model; and (2) scheduling a query execution on a specified combination of available systems (query engines+runtimes), based on the classification model. 
     For creating a classification model, the present invention first gathers training data for supervised learning. The training data can include, for example, a set of feature vectors extracted from an execution plan Directed Acyclic Graph (DAG) generated by a Cost-Based-Optimizer (CBO) of a query engine (e.g., a catalyst from SparkSQL, a calcite from Hive, and so forth). 
     In an embodiment, the DAG can include various information (such as the number of maps, the number of reduces, the number of joins, the size of the input tables, execution order, etc.) relating to and/or otherwise implicated by the various execution combinations. 
     In an embodiment, the present invention can add labels (e.g., indicative of the most suitable combination) for the feature by testing all combinations, and then can train a classification model using the features and the labels. 
     In an embodiment relating to scheduling a query, a scheduler uses this classifier to predict what combination of systems are most suitable for a given query. Once the present invention receives a SQL, the system asks query engines for generating DAGs before query execution, which can be performed without actual execution (i.e. explain). The present invention then extracts feature vector from those DAGs and predicts a label with this classifier. Based on the prediction result, the present invention can launch a specified execution combination and submit the query to be executed according to the specified combination. 
       FIG.  1    is a block diagram showing an exemplary processing system  100  to which the invention principles may be applied, in accordance with an embodiment of the present invention. The processing system  100  includes at least one processor (CPU)  104  operatively coupled to other components via a system bus  102 . A cache  106 , a Read Only Memory (ROM)  108 , a Random Access Memory (RAM)  110 , an input/output (I/O) adapter  120 , a sound adapter  130 , a network adapter  140 , a user interface adapter  150 , and a display adapter  160 , are operatively coupled to the system bus  102 . At least one Graphics Processing Unit (GPU)  194  is operatively coupled to the system bus  102 . 
     A first storage device  122  and a second storage device  124  are operatively coupled to system bus  102  by the I/O adapter  120 . The storage devices  122  and  124  can be any of a disk storage device (e.g., a magnetic or optical disk storage device), a solid state magnetic device, and so forth. The storage devices  122  and  124  can be the same type of storage device or different types of storage devices. 
     A speaker  132  is operatively coupled to system bus  102  by the sound adapter  130 . A transceiver  142  is operatively coupled to system bus  102  by network adapter  140 . A display device  162  is operatively coupled to system bus  102  by display adapter  160 . 
     A first user input device  152 , a second user input device  154 , and a third user input device  156  are operatively coupled to system bus  102  by user interface adapter  150 . The user input devices  152 ,  154 , and  156  can be any of a keyboard, a mouse, a keypad, an image capture device, a motion sensing device, a microphone, a device incorporating the functionality of at least two of the preceding devices, and so forth. Of course, other types of input devices can also be used, while maintaining the spirit of the present invention. The user input devices  152 ,  154 , and  156  can be the same type of user input device or different types of user input devices. The user input devices  152 ,  154 , and  156  are used to input and output information to and from system  100 . 
     Of course, the processing system  100  may also include other elements (not shown), as readily contemplated by one of skill in the art, as well as omit certain elements. For example, various other input devices and/or output devices can be included in processing system  100 , depending upon the particular implementation of the same, as readily understood by one of ordinary skill in the art. For example, various types of wireless and/or wired input and/or output devices can be used. Moreover, additional processors, controllers, memories, and so forth, in various configurations can also be utilized as readily appreciated by one of ordinary skill in the art. These and other variations of the processing system  100  are readily contemplated by one of ordinary skill in the art given the teachings of the present invention provided herein. 
     Moreover, it is to be appreciated that system  200  described below with respect to  FIGS.  2  and  3    is a system for implementing respective embodiments of the present invention. Part or all of processing system  100  may be implemented in one or more of the elements of system  200 . 
     Further, it is to be appreciated that processing system  100  may perform at least part of the method described herein including, for example, at least part of method  300  of  FIG.  3    and/or at least part of method  500  of  FIG.  5   . Similarly, part or all of system  200  may be used to perform at least part of method  300  of  FIG.  3    and/or at least part of method  500  of  FIG.  5   . 
       FIG.  2    is a block diagram showing an exemplary system  200 , configured to select a combination of systems (query engine+runtime) for query processing, in a training stage, in accordance with an embodiment of the present invention.  FIG.  3    is a flow diagram showing an exemplary method  300  for training a system that is configured to select a combination of systems for query processing, in accordance with an embodiment of the present invention. In an embodiment, method  300  is performed by system  200 , for example, by the elements of system  200  corresponding to the training stage as shown in  FIG.  2   . 
     The system  200  includes a Cost-Based-Optimizer (CBO)  220  for query engine A, a CBO  230  for query engine B, a database  240  of table statistics, a query scheduler  250 , an execution portion  260 , and a classifier  280 . The system  200  receives a Structured Query Language (SQL) statement  210  as input. 
     The execution portion  260  includes the following four execution combinations for the sake of illustration (noting that other combinations are possible, while maintaining the spirit of the present invention): 
     (1) engine A+runtime A (referred to as execution combination  261 ); 
     (2) engine B+runtime B (referred to as execution combination  262 ); 
     (3) engine C+runtime A (referred to as execution combination  263 ); and 
     (4) engine B+runtime B (referred to as execution combination  264 ). 
     At block  310 , receive, by the CBOs  220  and  230  and the query scheduler  250 , a set of SQL statements  210  for processing (i.e., for training the classifier  280  to become a classification model  280 A). In an embodiment, each of the SQL statements  210  provided to the CBOs  220  and  230  can be preceding by the word “explain” in order for the SQL statements  210  to behave like SQL queries (and hence are interchangeably referred to as “SQL queries”  210 ). In an embodiment, the SQL statements  210  provided to the query scheduler  250  can be preceded by the word “submit” in order to directly submit the SQL statement  210  to the query scheduler  250  for processing. 
     At block  320 , generate (gen), by each of the CBOs  220  and  230  for each SQL query  210 , a respective physical execution plan in the form of a respective Directed Acyclic Graph (DAG) (DAGs  221  and  231 , respectively), based on that SQL query  210  and table statistics from the database  240  of table statistics. For each SQL query  210 , the DAGs  221  and  231  can be generated to include various types of information relating to the various execution combinations including, but not limited to, the number of maps, the number of reduces, the number of joins, the size(s) of the input tables, execution time, execution order, and so forth that can be involved and/or otherwise implicated by the various execution combinations. The preceding various types of information can be obtained from the database  240  of table statistics. In an embodiment, each DAG represents a particular execution combination(s) involving a respective one of the query engines (A or B), such that DAG  221  always involves query engine A, and such that DAG  231  always involves query engine B, but each capable of using varying runtimes to form different combinations with these query engines. 
     At block  330 , extract, by the CBO  220  for each SQL query  210 , a feature vector A  221 A from DAG  221 . Also, extract, by the CBO  230  for each SQL query  210 , a feature vector B  231 B from DAG  231 . Additionally, for each SQL query  210 , merge the feature vector  221 A with the feature vector  231 B to form a merged feature vector  277 . The merging can be performed by the CBOs  220  and  230 . While this embodiment and others relating thereto described herein utilize the CBOs  220  and  230  to extract and merge feature vectors, in other embodiments, a set of feature vector extractors can be used to extract and merge feature vectors. 
     The merged feature vector  277  for each SQL query  210  can be used for creating/training a classification model  280 A implemented by the classifier  280 , as described herein below with respect to step  350 . In an embodiment, the feature vector  221 A for a particular SQL query  210  can include a set of features pertaining to all or part of the information in the DAG  221  corresponding to a particular execution combination(s) involving query engine A, and feature vector  231 B for a particular SQL query  210  can include a set of feature pertaining to all or part of the information in the DAG  231  corresponding to a particular execution combination(s) involving query engine B. To the preceding end, while constrained to a particular query engine (A or B), each of the feature vectors  221   a  and  231 B can include a set of features pertaining to information for one or more runtimes. 
     At block  340 , execute (exec) all of the various execution combinations (e.g., execution combinations  261 - 264 ), and generate and add (and/or otherwise associate) a respective label  266  to the merged feature vector of each SQL query  210  based on the various execution combinations  261 - 264 . In an embodiment, the label  266  can be indicative of the most suitable execution combination for each of the SQL queries  210  (and/or can include information relating to an ordering of the execution combinations, e.g., from best to worst (e.g., fastest to slowest, least computationally expensive to most computationally expensive, and so forth)) and can be associated with the merged feature vector  277  obtained for that SQL query  210 . In an embodiment, each label  266  can correspond to a feature (query engine and/or runtime feature) that, in turn, corresponds to a particular execution combination (from among execution combinations  261 - 264 ). In an embodiment, each label  266  can be based on the execution time of (i.e., the time it takes to execute) a respective one of the execution combinations and/or other parameters (e.g., computational expense, etc.). 
     At block  350 , train the classifier  280  using the merged feature vector  277  for each of the SQL queries  210  and the label(s)  266  corresponding thereto in order to form a classification model  280 A therefrom. The figure reference numeral  280  is thus used herein to refer to an untrained classifier/classification model, while the figure reference numeral  280 A is used herein to refer to a trained classifier/classification model. In an embodiment, the classifier  280  is trained to become the classification model  280 A by learning the merged feature vector  277  and corresponding label(s)  266  for each of the SQL queries  210 . 
     In the embodiment shown in  FIG.  2   , the elements thereof are interconnected by a bus(es)/network(s) (not specifically shown). However, in other embodiments, other types of connections can also be used. Moreover, in an embodiment, at least one of the elements of system  200  is processor-based. Further, while one or more elements may be shown as separate elements, in other embodiments, these elements can be combined as one element. The converse is also applicable, where while one or more elements may be part of another element, in other embodiments, the one or more elements may be implemented as standalone elements. Moreover, one or more elements of  FIG.  2    can be implemented in a cloud configuration including, for example, in a distributed configuration. Additionally, one or more elements in  FIG.  2    may be implemented by a variety of devices, which include but are not limited to, Digital Signal Processing (DSP) circuits, programmable processors, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), Complex Programmable Logic Devices (CPLDs), and so forth. These and other variations of the elements of system  200  are readily determined by one of ordinary skill in the art, given the teachings of the present invention provided herein, while maintaining the spirit of the present invention. 
       FIG.  4    is a block diagram further showing the system  200  of  FIG.  2   , in a scheduling stage, in accordance with an embodiment of the present invention.  FIG.  5    is a flow diagram showing an exemplary method  500  for scheduling query processing by selecting a combination of systems for query processing, in accordance with an embodiment of the present invention. In an embodiment, method  500  is performed by system  200 , for example, by the elements of system  200  corresponding to the scheduling stage as shown in  FIG.  4   . 
     In  FIG.  4   , the scheduling stage involves the following elements of system  200  of  FIG.  2   : the query scheduler  250 ; the CBOs  220  and  230 ; and the classifier  280 . 
     At block  510 , receive, by the CBOs  220  and  230 , a SQL statement  410  for which a physical execution plan is to be determined (i.e., a particular one of the various execution combinations is to be selected). In an embodiment, the SQL statement  410  provided to the query scheduler  250  can be preceded by the word “explain” in order for the SQL statement  410  to behave like a SQL query (and hence is interchangeably referred to as “SQL query”  410 ). 
     At block  520 , generate, by the CBOs  220  and  230 , at least one DAG for each query engine (A and B) to generate a set of DAGs (e.g., DAG  421  and  431 ). In an embodiment, the DAG(s) for each query engine can involve multiple different runtimes. In an embodiment, the set of DAGs can be generated before/without execution of the involved execution combinations. 
     At block  530 , extract and merge, by the CBOs  220  and  230 , a set of feature vectors from each of the DAGs in the set to form a merged feature vector  477 . Each of the feature vectors can correspond to a particular execution combination (query engine+runtime). 
     At block  540 , predict, by applying the classification model  280 A to the merged feature vector, a label(s)  466  indicative of a particular (optimal) execution combination for processing the SQL statement  410 . 
     At block  550 , execute, by the query scheduler  250 , the SQL statement  410  using the particular (optimal) execution combination. In this example, the particular (optimal) execution combination is engine A+runtime B, as evidenced from the labels  266  (A+B). 
       FIG.  6    is a block diagram showing another exemplary system  600 , configured to select a combination of systems for query processing, in accordance with an embodiment of the present invention. System  600  can be considered a more specific example of system  200  as shown in  FIGS.  2  and  4   , where specific examples of certain query engines and runtimes are employed. Moreover, system  600  is shown mixing various aspects from the training and scheduling stages relating to the embodiments shown in  FIGS.  2  and  4   . 
     System  600  includes a Cost-Based-Optimizer (CBO)  620  for a Spark query engine, a CBO for a Tez query engine  630 , a database  640  of table statistics, and a classifier/classification model  680 / 680 A. 
     A SQL statement  610  is received by the CBOs  620  and  630 , which then generate respective DAGs  621  and  631  for each query engine (i.e., Spark and Tez). A feature vector ( 621 A and  631 B, respectively) is extracted from each of the DAGs  621  and  631  that is specific to a particular engine (i.e., the top DAG  621  is specific to Spark and the bottom DAG  631  is specific to Tez). The feature vector for each query engine ( 621 A and  631 B) is merged to obtain a merged feature vector  677 . The classifier  680  is trained using, for example, the execution history  688  relating to (the information in) the DAGs  621  and  631  in order to form a classification model  680 A. The execution history  688  can be in the form of previously determined merged feature vectors and labels for various different execution combinations. The trained classifier, that is, the classification model  680 A then predicts labels  666  for the SQL statement  610  as follows: Tez®+OpenJDK; Tez®+J9; Spark+OpenJDK; and Spark+J9. The preceding labels can be provided in a particular order when order position indicates optimality of a given execution combination. 
     It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
     Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). 
     Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
     Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. 
     Service Models are as follows: 
     Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). 
     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes. 
     Referring now to  FIG.  7   , illustrative cloud computing environment  750  is depicted. As shown, cloud computing environment  750  includes one or more cloud computing nodes  710  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  754 A, desktop computer  754 B, laptop computer  754 C, and/or automobile computer system  754 N may communicate. Nodes  710  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  750  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  754 A-N shown in  FIG.  7    are intended to be illustrative only and that computing nodes  710  and cloud computing environment  750  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
     Referring now to  FIG.  8   , a set of functional abstraction layers provided by cloud computing environment  750  ( FIG.  7   ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG.  8    are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  860  includes hardware and software components. Examples of hardware components include: mainframes  861 ; RISC (Reduced Instruction Set Computer) architecture based servers  862 ; servers  863 ; blade servers  864 ; storage devices  865 ; and networks and networking components  866 . In some embodiments, software components include network application server software  867  and database software  868 . 
     Virtualization layer  870  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  871 ; virtual storage  872 ; virtual networks  873 , including virtual private networks; virtual applications and operating systems  874 ; and virtual clients  875 . 
     In one example, management layer  880  may provide the functions described below. Resource provisioning  881  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  882  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  883  provides access to the cloud computing environment for consumers and system administrators. Service level management  884  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  885  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  890  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  891 ; software development and lifecycle management  892 ; virtual classroom education delivery  893 ; data analytics processing  894 ; transaction processing  895 ; and optimum system combination selection for query processing  96 . 
     The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     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 invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, 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 conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose 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 invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, 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. 
     Reference in the specification to “one embodiment” or “an embodiment” of the present invention, as well as other variations thereof, means that a particular feature, structure, characteristic, and so forth described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrase “in one embodiment” or “in an embodiment”, as well any other variations, appearing in various places throughout the specification are not necessarily all referring to the same embodiment. 
     It is to be appreciated that the use of any of the following “/”, “and/or”, and “at least one of”, for example, in the cases of “A/B”, “A and/or B” and “at least one of A and B”, is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of both options (A and B). As a further example, in the cases of “A, B, and/or C” and “at least one of A, B, and C”, such phrasing is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of the third listed option (C) only, or the selection of the first and the second listed options (A and B) only, or the selection of the first and third listed options (A and C) only, or the selection of the second and third listed options (B and C) only, or the selection of all three options (A and B and C). This may be extended, as readily apparent by one of ordinary skill in this and related arts, for as many items listed. 
     Having described preferred embodiments of a system and method (which are intended to be illustrative and not limiting), it is noted that modifications and variations can be made by persons skilled in the art in light of the above teachings. It is therefore to be understood that changes may be made in the particular embodiments disclosed which are within the scope of the invention as outlined by the appended claims. Having thus described aspects of the invention, with the details and particularity required by the patent laws, what is claimed and desired protected by Letters Patent is set forth in the appended claims.