Patent Application
20220207444
The present invention relates to Automated Machine Learning (AutoML) and cloud computing, and particularly methods and systems for running cloud-based AutoML systems and the pricing of models generated by cloud-based AutoML systems.
Systems and methods are known for cloud-based services, or the on-line provisioning of computing resources as services. Service providing entities now offer cloud-based AutoML services where artificial intelligence or machine learned models are generated and built by and for end use customers.
Currently, such entities provisioning cloud-based models implement a cost or pricing scheme according to current cloud-based pricing models, e.g., a cloud resource utilization-based pricing model where pricing is based according to the amount of resources being used.
For example, current automated machine learning systems (AutoML systems or services) are cloud-based and the pricing structure of these systems is based on the standard cloud pricing structure, which charges for per unit of compute used by AutoML systems.
In such current cloud-based pricing models, users are allocated compute resources in the cloud, and users are charged based on their usage. As an example, some current AutoML service providers charge for usage services by computation (e.g., number of nodes, duration, memory usage, etc.) and input data volume. However, such pricing does not account for model quality or input data characteristics.
For example, to earn more revenue with current cloud-based pricing structures, it would be necessary to run the AutoML systems for more time and this objective directly conflicts with the objective of AutoML systems, which is to create accurate models and fast.
The following summary is merely intended to be exemplary. The summary is not intended to limit the scope of the claims.
According to an aspect, a system and method is provided for a cloud-based service provide to price models generated by cloud-based AutoML systems on the performance enhancement they deliver to the end-user.
According to a further aspect, a system and method is provided for building a pricing model for automated machine learning usage that is in-line with an optimization objective of machine learning systems.
Further aspects include implementing a pricing scheme for pricing a customer's generation and use of cloud-based AutoML and AI models that prices according to the quality of the model and other user-defined metrics —such model quality and user-defined metrics criteria defining the model's utility availed by the end user.
According to an aspect, a system and method implementing a pricing scheme for pricing a customer's generation and use of cloud-based AutoML and AI models that modifies a current cloud-based pricing model according to the quality of the model and other user-defined metrics.
According to one aspect, there is provided a computer-implemented method of managing provision of model prediction services. The method comprises: receiving, by a hardware processor, a user request for providing model prediction services over a network, the user request comprising one or more performance improvement metrics; determining, by the hardware processor, a base model pipeline for the prediction services; determining, by the hardware processor, a first value commensurate with provision of the base model pipeline for the prediction service; determining, by the hardware processor, performance enhancements to the base model pipeline that improve the prediction service performance according to the one or more performance improvement metrics; determining, by the hardware processor, an add-on value commensurate with the improved performance when providing for the prediction service; providing, by the hardware processor, the prediction service including the base model pipeline enhancements; and assessing, by the hardware processor, a charge to the user for receiving the prediction service according to the first value and add-on value.
According to one aspect, there is provided a computer-implemented system for managing provision of model prediction services. The system comprises: a memory storage device for storing a computer-readable program, and at least one processor adapted to run the computer-readable program to configure the at least one processor to: receive a user request for providing model prediction services over a network, the user request comprising one or more performance improvement metrics; determine a base model pipeline for the prediction services; determine a first value commensurate with provision of the base model pipeline for the prediction service; determine performance enhancements to the base model pipeline that improve the prediction service performance according to the one or more performance improvement metrics; and determine an add-on value commensurate with the improved performance when providing for the prediction service; provide the prediction service including the base model pipeline enhancements; and assess a charge to the user for receiving the prediction service according to the first value and add-on value.
In a further aspect, there is provided a computer program product for performing operations. The computer program product includes a storage medium readable by a processing circuit and storing instructions run by the processing circuit for running a method. The method is the same as listed above.
The foregoing aspects and other features are explained in the following description, taken in connection with the accompanying drawings, wherein:
According to an embodiment, the present disclosure provides for a system and a method for service providers to implement a Pay-As-You-Go (PAYG) automated machine learning (AutoML) prediction system where the end user is charged for model pipeline usage on the basis of performance improvement achieved by the system. As referred to herein, a “pipeline” means the machine learning models produced by the AutoML system which include a sequence of data transformation and modeling steps and for which a price is determined using a “base” model as reference. As used herein, a model architecture pipeline(s) refers to AutoML pipeline(s), AutoML model pipeline(s), or AutoML model(s).
In an embodiment, the performance improvement is determined relative to the performance obtained by a base model which is also a machine learning model or machine learning pipeline (also referred to as a base ML model or base ML model pipeline). For example, the base model price for the given user dataset is identified as a “simple” model whose results can be replicated by the user on any system or on any system of the competitor. The base model is priced using the current cloud resource utilization-based pricing model (i.e., price the base model according to the amount of resources being used). As these results are easily replicable on other clouds, this model is not priced in any other way but by using resource utilization.
In embodiments, the system and method further implements receiving user specified performance metric(s) and the system responsively performs a ranking of AutoML model pipelines based on user specified metric, or combination of metrics (for example: accuracy, prediction time and F1 score).
In an embodiment, the price for each of the ranked AutoML model pipelines is determined based on a “surrogate” model. In such an embodiment, the method performs fitting a surrogate model to the base model price and the maximum price for a model. Then, the pricing increments according to linear differences or fixed percentage differences in the metrics.
As shown in
In accordance with an aspect of the invention, via the AutoML APIs, the end user 105 provides input training data 115 into the system for use in generating and building customized prediction models. Such training data 115 includes but not limited to: ground-truth data consisting of data that the ML models are to predict, data labels and application-specific performance metrics used to evaluate the built or customized model. Such application-specific performance metrics include utility based metrics the user cares about for evaluating a quality of the customized model. Example utility-based metrics used for evaluating a quality of the customized model includes, but is not limited to: a time (e.g., a lag time or throughput in nanoseconds) it takes the model to generate a prediction, the accuracy of the prediction (e.g., precision, recall, F1 score). In an embodiment, the user can enter a desired run time for solving a time series prediction problem, and a desired time series prediction result (prediction) accuracy. Alternately, the user can specify a run time ratio preference which is a ratio of additional gain accuracy gain over run time, e.g., a 1% higher accuracy per hour, which means one additional hour run time will lead to 1% higher accuracy conditional on the current accuracy.
In an embodiment, the computing and data processing environment 100 provides a service endpoint within which the AutoML system API 130 performs methods to input or retrieve a created dataset or import data into or update a dataset for use in training/customizing one or more models using the model builder component 150. That is, a library of algorithms are available for configuring models to perform: regression, complex multiclass classification, and deep learning. Further methods include: methods to create/delete or deploy a customized model; methods to export a trained model to a user specified storage location; methods implemented to perform on-line predictions, list models and their corresponding pricing, and methods to obtain a model evaluation or list model evaluations.
In an example implementation, an end-user 105 inputs a dataset dependent upon the business use case or application. For example, in a computer vision use case scenario, an example dataset entered by a user or specified at another location for input may include a .csv file with the location and labels for each of multiple training images where a classified image is assigned a single label, or an image is assigned multiple labels. The AutoML API function initiates a training operation using the model builder component 150 to build one or more customized models. Dependent upon the user-specified utility-based metrics, several prediction models may be generated that aim to satisfy one or more of the metrics, e.g., multilayer perceptron (MPL) models, convolution neural network models (CNN) or recurrent neural network models (RNN). When training is complete, a model identifier is returned for each customized model generated.
After training, the AutoML API function initiates a quality evaluation of the model, e.g., by reviewing a time for it to produce a prediction result given a dataset, or by reviewing the model's accuracy, e.g., by evaluating its precision, recall, and F1 score. In embodiments, a combination of user-specified metrics, e.g., time, accuracy, F1 score, or combinations thereof, is used for evaluating the model quality for pricing purposes.
In an embodiment, at the service endpoint, the AutoML API 130 further initiates running of performance tracking methods 180 for evaluating the customized model being made. For example, using a test dataset the performance tracker component 180 receives and evaluates test predictions for a customized model. Once a model is built that achieves performance metrics specified by a user, the updated model is rendered available via an API 130 for subsequent access via user device 105. For example, the newly customized model may be hosted by the cloud service provider or downloaded for use in an application by the end-user.
At the service endpoint, the AutoML API 130 further initiates running of real-time pricing model(s) 175 implementing one or more functions that take input parameters to determine a cost for the end-user who use the infrastructure to train, deploy and/or host custom models and receive the benefit from the ML system.
In one embodiment, a first pricing model(s) 175A determines AutoML model pricing based on model quality using a base model price and a linear pricing model. A second pricing model(s) 175B determines AutoML model pricing based on model quality using a base model pipeline price and an exponential pricing model. Such pricing models 175A, 175B run functions implementing logic to charge the end-user 105 on the basis of performance improvement achieved by the configurable model pipeline relative to a base model pipeline from which it is built. In an embodiment, the performance improvement is determined from user-specified performance metric(s).
In embodiments, the base model pipeline is a simple model whose results can be replicated by the user on any system or on any system of a competitor. Initially, a base model price for the given user dataset can be a fixed price. The base model price for the given user dataset is first identified using any current cloud resource utilization-based pricing model, i.e. price the base model according to the amount of resources being used. This pricing structure is based on resource usage and can include for instance pricing charge based on computation (e.g., number of nodes, number of processing cores, duration, RAM usage, temporary/external storage memory usage, etc.) and input data volume. This is because these results are easily replicable on other clouds, so this model cannot be priced in any other way but by using resource utilization.
Then, continuing to 206, based upon the specified prediction model type and specified performance metric(s), the AutoML method determines a base model pipeline from which to build the requested prediction model with enhanced performance as requested. From this base model, the system further determines all procurable model pipelines that can be built off the base model that can achieve the performance/quality improvement metric(s) as specified using the AutoML program. Each of these individual procurable model pipelines have an associated “Pay as you go” (PAYG) price or additional cost value to be added onto the base model price. Alternatively, or in addition, the potential models can be ranked according to their performance enhancement(s)/achievable metric(s) and the model pipelines valued or priced accordingly.
To price a specific prediction model to offer the end user, at step 206, or at a time even prior to receiving the request, given a base model pipeline for the particular prediction problem to be solved and potential performance enhancement(s)/achievable metric(s), a determination is made to value potential procurable models according to a linear pricing scheme or exponential pricing scheme.
Such linear or exponential pricing schemes for valuing potential pipeline models is an additional value or cost ultimately added-on to a pre-determined or fixed base model pipeline usage cost. In embodiments, the pre-determined base model pipeline is priced as a simple model whose results can be replicated by the user on any system or on any system of a competitor. For example, the base model pipeline is priced using a cloud resource utilization-based pricing model (i.e., price the base model pipeline according to the amount of resources being used). This is because these results are easily replicable on other clouds, so this model cannot be priced in any other way but by using resource utilization.
If an improved prediction model is subject to a linear pricing scheme, the system will invoke methods to compute the PAYG price by applying the linear pricing scheme. The linear pricing scheme invokes methods for identifying the base model pipeline price for the given user dataset (e.g., some fixed price) and performs a ranking of different model pipelines that can be built based on the user specified metric, e.g., run time, prediction accuracy, explainability. Finally, the method specifies a price for ranked pipelines based on a linear model.
Returning to
Returning to
In the embodiments herein, one system API used in the AutoML system 100 includes an API for evaluating the quality of a pipeline model's prediction. The performance tracker/evaluator module 180 may implement functions assessing prediction errors for purposes of model quality evaluation. These metrics include but are not limited to one or more of: classification metrics, multilabel ranking metrics, regression metrics and clustering metrics. Types of performance metrics that can be specified include, but are not limited to: Precision_recall_curve, Roc_curve, balanced_accuracy_score, cohen_kappa_score, confusion_matrix, hinge_loss, Matthews_corrcoef, accuracy_score, classification_report, f1_score, fbeta_score, hamming_loss, jaccard_score, log-loss, multilabel_confusion_matrix, precision_recall_fscore_support, precision_score, recall_score, roc_auc_score, zero_one_Joss, average_precision_score, precision_score, recall_score, mean_squared_error, mean_absolute_error, explained_variance_score and r2_score.
Additionally presented to the user via the user interface for each model pipeline is a respective model pipeline value or corresponding PAYG price 407 for implementing (deploying or downloading) the customized model. This corresponding PAYG price 407 is in accordance with a relevant currency (e.g., U.S. dollars) determined according to the linear or exponential pricing scheme.
Although not shown in
The end user may then select a customized model with the indicated processing that meets the specified quality standards. That is, returning to step 213,
Otherwise, at 216,
Continuing to step 218,
Further, at 220,
In an embodiment, the providers of the PAYG system can further charge to end users a profit margin which is included in a fixed base model usage charge or price according to the base model.
In embodiments, the end user can be additionally optionally charged a periodic access usage charge.
In some embodiments, the computer system may be described in the general context of computer system executable instructions, embodied as program modules stored in memory 16, being executed by the computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks and/or implement particular input data and/or data types in accordance with the present invention (see e.g.,
The components of the computer system may include, but are not limited to, one or more processors or processing units 12, a memory 16, and a bus 14 that operably couples various system components, including memory 16 to processor 12. In some embodiments, the processor 12 may execute one or more modules 11 that are loaded from memory 16, where the program module(s) embody software (program instructions) that cause the processor to perform one or more method embodiments of the present invention. In some embodiments, module 11 may be programmed into the integrated circuits of the processor 12, loaded from memory 16, storage device 18, network 24 and/or combinations thereof.
Bus 14 may represent one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnects (PCI) bus.
The computer system may include a variety of computer system readable media. Such media may be any available media that is accessible by computer system, and it may include both volatile and non-volatile media, removable and non-removable media.
Memory 16 (sometimes referred to as system memory) can include computer readable media in the form of volatile memory, such as random access memory (RAM), cache memory an/or other forms. Computer system may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system 18 can be provided for reading from and writing to a non-removable, non-volatile magnetic media (e.g., 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”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to bus 14 by one or more data media interfaces.
The computer system may also communicate with one or more external devices 26 such as a keyboard, a pointing device, a display 28, etc.; one or more devices that enable a user to interact with the computer system; and/or any devices (e.g., network card, modem, etc.) that enable the computer system to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces 20.
Still yet, the computer system can communicate with one or more networks 24 such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 22. As depicted, network adapter 22 communicates with the other components of computer system via bus 14. It should be understood that although not shown, other hardware and/or software components could be used in conjunction with the computer system. Examples include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.
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, 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's computer, partly on the user's computer, as a stand-alone software package, partly on the user'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'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 flowcharts 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 blocks 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.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The corresponding structures, materials, acts, and equivalents of all elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present invention has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form 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 invention. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.
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'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'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'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
Referring now to
Hardware and software layer 60 includes hardware and software components. Examples of hardware components include: mainframes 61; RISC (Reduced Instruction Set Computer) architecture based servers 62; servers 63; blade servers 64; storage devices 65; and networks and networking components 66. In some embodiments, software components include network application server software 67 and database software 68.
Virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers 71; virtual storage 72; virtual networks 73, including virtual private networks; virtual applications and operating systems 74; and virtual clients 75.
In one example, management layer 80 may provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 84 provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 85 provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.
Workloads layer 90 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom education delivery 93; data analytics processing 94; transaction processing 95; and processing 96 to automatically price prediction model pipelines used in solving user prediction problems within specified performance metrics constraints.
