Patent Application
20230289650
The present invention relates generally to computing systems, and more particularly, to continuous learning systems for containerized environments with limited resources.
In today's society, consumers, businesspersons, educators, and others communicate over a wide variety of mediums in real time, across great distances, and many times without boundaries or borders. With the increased usage of computing networks, such as the Internet, humans are currently inundated and overwhelmed with the amount of information available to them from various structured and unstructured sources. Due to the recent advancement of information technology and the growing popularity of the Internet, a wide variety of computer systems have been used in machine learning.
Machine learning, a subset of artificial intelligence (AI), allows a device to automatically learn from past data without using explicit instructions, relying on patterns and inferences instead. Machine learning algorithms build a mathematical model based on sample data, known as “training data,” in order to make predictions or decisions without being explicitly programmed to perform the task. The machine learning algorithms are updated or retrained as new training data becomes available.
According to a non-limiting embodiment, a continuous machine learning system includes a data generator module, a pipeline search module, a pipeline refinement module, and a pipeline training module. The data generator module obtains raw training data defining a total data size and generates a plurality of data batches from the raw training data. The pipeline search module obtains an initial data batch from among the plurality of data batches and determines a best machine learning model pipeline among a plurality of machine learning model pipelines based on the initial data batch. The pipeline refinement module receives the best machine learning model pipeline and refines the best machine learning model pipeline to generate a refined pipeline that consumes the plurality of data batches. The pipeline training module incrementally trains the refined pipeline using remaining data batches among the plurality of data batches generated after the initial data batch.
According to another non-limiting embodiment, a computer-implemented method comprises obtaining by a data generator module raw training data defining a total data size, and generating by the data generator module a plurality of data batches from the raw training data. The method further comprises obtaining, by a pipeline search module in signal communication with the data generator module, an initial data batch from among the plurality of data batches, and determining by the pipeline search module a best machine learning model pipeline among a plurality of machine learning model pipelines based on the initial data batch. The method further comprises receiving by a pipeline refinement module in signal communication with the pipeline search module the best machine learning model pipeline, and refining by the pipeline refinement module the best machine learning model pipeline to generate a refined pipeline configured to consume the plurality of data batches. The method further comprises incrementally training by the pipeline training module the refined pipeline using remaining data batches among the plurality of data batches generated after the initial data batch.
According to yet another non-limiting embodiment, A computer program product to control continuous machine learning system to generate data batches used to determine, refine and train a best pipe line, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by an electronic computer processor to control the continuous machine learning system to perform operations comprising obtaining by a data generator module raw training data defining a total data size, and generating by the data generator module a plurality of data batches from the raw training data. The method further comprises obtaining, by a pipeline search module in signal communication with the data generator module, an initial data batch from among the plurality of data batches, and determining by the pipeline search module a best machine learning model pipeline among a plurality of machine learning model pipelines based on the initial data batch. The method further comprises receiving by a pipeline refinement module in signal communication with the pipeline search module the best machine learning model pipeline, and refining by the pipeline refinement module the best machine learning model pipeline to generate a refined pipeline configured to consume the plurality of data batches. The method further comprises incrementally training by the pipeline training module the refined pipeline using remaining data batches among the plurality of data batches generated after the initial data batch.
Other embodiments of the present invention implement features of the above-described method in computer systems and computer program products.
Additional technical features and benefits are realized through the techniques of the present invention. Embodiments and aspects of the invention are described in detail herein and are considered a part of the claimed subject matter. For a better understanding, refer to the detailed description and to the drawings.
The specifics of the exclusive rights described herein are particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other features and advantages of the embodiments of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which:
The diagrams depicted herein are illustrative. There can be many variations to the diagrams or the operations described therein without departing from the spirit of the invention. For instance, the actions can be performed in a differing order or actions can be added, deleted or modified. Also, the term “coupled” and variations thereof describes having a communications path between two elements and does not imply a direct connection between the elements with no intervening elements/connections between them. All of these variations are considered a part of the specification.
The innovations in artificial intelligence and machine learning have resulted in an increased market demand for training systems capable of operating in containerized environments. Containerized environments such as docker image environments, for example, facilitate runtimes and images that can be conveniently deployed on any cloud provider (e.g., both private and public cloud providers).
The same market also demands the ability for machine learning training system to support larger tabular data pools. Conventional automatic machine learning trainings systems currently available in the market require that the entire data pool be directly available so that the data is passed to the training system to determine the available machine learning models. Therefore, the environment running the machine learning training system must have sufficient resources, e.g., memory, to handle the data pool. However, containerized environments typically have limited resources, e.g., limited available memory. For example, a containerized environment (e.g., a docker image) may be deployed with 16 gigabytes (GB) of memory, but the data pool may contain 100 GB of training data. Consequently, the limited resources of the docker image may not allow a conventional machine learning training system to properly train models using the entire training data, or may even crash the containerized environment.
Embodiments of the present invention overcome the shortcomings of the current methods by providing a continuous machine learning system configured to operate in a containerized environment having limited resources. The continuous machine learning system employs a pipeline search module and a pipeline refinement module to automatically determine the best pipeline among a plurality of pipelines, and then automatically refine the best pipeline to facilitate automatic and continue machine learning of records and resources assigned to the container environment.
Various embodiments of the invention are described herein with reference to the related drawings. Alternative embodiments of the invention can be devised without departing from the scope of this invention. Various connections and positional relationships (e.g., over, below, adjacent, etc.) are set forth between elements in the following description and in the drawings. These connections and/or positional relationships, unless specified otherwise, can be direct or indirect, and the present invention is not intended to be limiting in this respect. Accordingly, a coupling of entities can refer to either a direct or an indirect coupling, and a positional relationship between entities can be a direct or indirect positional relationship. Moreover, the various tasks and process steps described herein can be incorporated into a more comprehensive procedure or process having additional steps or functionality not described in detail herein.
The following definitions and abbreviations are to be used for the interpretation of the claims and the specification. As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having,” “contains” or “containing,” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a composition, a mixture, process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but can include other elements not expressly listed or inherent to such composition, mixture, process, method, article, or apparatus.
Additionally, the term “exemplary” is used herein to mean “serving as an example, instance or illustration.” Any embodiment or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or designs. The terms “at least one” and “one or more” may be understood to include any integer number greater than or equal to one, i.e. one, two, three, four, etc. The terms “a plurality” may be understood to include any integer number greater than or equal to two, i.e. two, three, four, five, etc. The term “connection” may include both an indirect “connection” and a direct “connection.”
The terms “about,” “substantially,” “approximately,” and variations thereof, are intended to include the degree of error associated with measurement of the particular quantity based upon the equipment available at the time of filing the application. For example, “about” can include a range of±8% or 5%, or 2% of a given value.
For the sake of brevity, conventional techniques related to making and using aspects of the invention may or may not be described in detail herein. In particular, various aspects of computing systems and specific computer programs to implement the various technical features described herein are well known. Accordingly, in the interest of brevity, many conventional implementation details are only mentioned briefly herein or are omitted entirely without providing the well-known system and/or process details.
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.
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.
With reference 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 comprise application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 84 provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 85 provides pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.
Workloads layer 90 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom education delivery 93; data analytics processing 94; transaction processing 95; and various workloads and functions 96 for performing continuous machine learning in a containerized environment having limited resources.
Referring to
In exemplary embodiments, the processing system 100 includes a graphics processing unit 130. Graphics processing unit 130 is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display. In general, graphics processing unit 130 is very efficient at manipulating computer graphics and image processing and has a highly parallel structure that makes it more effective than general-purpose CPUs for algorithms where processing of large blocks of data is done in parallel.
Thus, as configured in
Turning now to
With continued reference to
According to one or more non-limiting embodiments, the data generator module 402 can load a batch queue 415 with a maximum number of data batches. As the continuous machine learning system 400 performs automatic machine learning, the data generator module 402 continues to load the batch queue 415 with data batches to maintain the maximum number of data batches until the training operation is stopped or the entire raw training data is exhausted from the database 410. According to one or more non-limiting embodiments, the data generator module 402 loads the batch queue 415 with a new data batch in response to outputting a loaded data batch from the batch queue 415 to the pipeline search module 404. In one or more non-limiting embodiments, the data generator module 402 can include a data reader 412 configured to determine descriptive statistics and add the descriptive statistics to each data batch. The descriptive statistics can include, but are not limited to, a distribution of categorical values. Accordingly, the pipeline search module 404, pipeline refinement module 406, and pipeline training module 408 can determine various descriptive statistics corresponding to each data batch output from the batch queue 415.
The pipeline search module 404 is implemented at a pipeline search stage 405. The pipeline search module 404 is in signal communication with the data generator module 402 to obtain an initial data batch. Using the initial data batch, the pipeline search module 404 analyzes a plurality of different types of machine learning model pipelines (referred to simply as a pipelines) operating according to different operating parameters and to determine an accuracy of each pipeline. The pipeline search module 404 determines the pipeline having a highest accuracy as the best pipeline among the plurality of pipelines.
According to one or more non-limiting embodiments, the pipeline search module 404 is configured to analyze one or more pipelines, along with corresponding metadata. A pipeline can be defined as a sequence of transformers and estimators and an ensemble of machine learning pipelines (e.g., a machine learning model pipeline as a sequence of data transformers followed by an estimator algorithm). A pipeline or a plurality of pipelines can represent or can be used to implement a machine learning model. One or more pipelines can be generated using an automated estimator engine and/or an automated model synthesizer. The pipeline search module 404 can analyze one or more of the pipelines and extracted metadata and apply a ranked score according to metadata ranking criteria and pipeline ranking criteria. In one or more non-limiting embodiments, an interactive visualization graphical user interface (“GUI”) can display the machine learning model corresponding to a given pipeline, the ensemble of a plurality of machine learning model pipelines (or combination thereof), and the rankings assigned to pipelines.
With continued reference to
The automated estimator engine 414 includes a neural network synthesis engine such as “NeuNetS” provided by IBM, which is configured to provide a plurality of different estimators. Each estimator can be defined by a machine learning (ML) algorithm such as, for example, a LGBM classifier and a XGB classifier. In one or more non-limiting embodiments of the invention, each estimator can be further defined by one or more ML enhancements. The ML enhancements include, but are not limited to, a model optimization hyperparameter (HPO-N) and a feature engineering option (e.g., FF, FE, etc.). The HPO-N enhancement aims at finding a well-performing hyperparameter configuration of a given machine learning model on a dataset at hand, including the machine learning model, its hyperparameters and other data processing steps. The feature engineering option defines a measurable input that can be used in a predictive model such as, for example, a color of an object or the sound of someone's voice, and can be used to convert raw observations into desired features using statistical or machine learning approaches.
The automated ML engine 416 is in signal communication with the data generator module 402 and the automated estimator engine 414. The automated ML engine 416 is configured to generate the plurality of pipelines based on the initial data batch and the estimators. In one or more non-limiting embodiments, the automated ML engine 416 can output the pipelines, ML models, and their ranking (e.g., individual ranked scores) in leaderboards displayed in the GUI. The leaderboards can display the pipelines and their respective estimators/parameters according to their respective rankings. For example, pipelines can be displayed in the leaderboard from highest ranking to lowest ranking.
The pipeline bank 418 is loaded with the pipelines created by the automated ML engine 416. The pipeline search module 404 can access the pipeline bank 418, select the best pipeline (e.g., highest ranked pipeline), and deliver it to the pipeline refinement module 406. In one or more non-limiting embodiments, the pipeline search module 404 can select a best pipeline in response to analyzing training results provided by the pipelines included in the pipeline bank 418. Following an initial training operation using the initial data batch, the pipelines pass test data through a sequence of data transformations (e.g., preprocessing, data cleaning, feature engineering, mathematical transformations, etc.) and use an estimator operation of the estimators (e.g., Logistic Regression, Gradient Boosting Trees, etc.) to yield predictions for the test data corresponding to each pipeline. The predictions can then be used to generate the ranking scores for each pipeline. The pipeline search module 404 can the identify the pipeline in the pipeline bank 418 with the highest ranking score as the best pipeline.
With continued reference to
In one or more non-limiting embodiments, the pipeline refinement module 406 refines the best pipeline by replacing the estimator of the best pipeline with a refined estimator. The refined estimator includes one or both of the Hyperparameter Optimization and the feature engineering option of the estimator, along with one or more of an additional Hyperparameter Optimization or additional feature engineering option obtained from the automated estimator engine
With continued reference to
In one or more non-limiting embodiments, the data generator module 402 includes a mini-batch module 500 and a mini-batch queue 504. The data generator 402 utilizes the mini-batch module 500 and a mini-batch queue 504 to control the loading of data batches 412b-412n into the batch queue 415.
The mini-batch module 500 receives each iteration of a subsequently generated data batch 512b-512n and generates one or more mini-data batches 502a, 502b, 502n. For example, if the data generator module 402 generates data batches 512a-512n having a size of 1 GB, the mini-batch module 500 can generate individual mini-data batches 502a-502n each having a smaller size, e.g., each mini-data batch 502a-502n having a size of about 100 MB. The mini-data batches 502a-502n can then be stored in the mini-batch queue 504.
With continued reference to
Turning to
When, however, the data generator module 402 determines that the batch queue 415 is not empty, the data generator module 402 proceeds confirms (i.e., by default) that the batch queue 415 currently contains a previously loaded data batch at operation 528, and loads the loads the “queued data batch” 412 into a second position in the batch queue 415 at operation 530. Accordingly, the batch queue 415 now contains two loaded data batches 412. At operation 532, the data generator module 402 temporality blocks reading any further threads to prevent obtaining any further data batches 412. At operation 534, the data generator module 402 obtains the loaded data batch 412 stored in the first queue position of the data queue 415 (referred to as the “leading data batch”) and delivers it to the pipeline refinement module 406 (see
At operation 536, the data generator module 402 unblocks reading threads so that new data batches 412 can be obtained. At operation 538, the loaded data batch stored in the second queue position of the data queue (i.e., the lagging data batch) is obtained and is delivered to the pipeline refinement module 406 for processing (see
With reference now to
Turning to operation 616, the refined pipeline is incrementally trained using subsequently generated data batches that include sub-sets of the remaining raw training data. Each time the refined pipeline is trained with a new data batch, a new version of the refined pipeline is generated. At operation 618, a determination is made as to whether a best version of the refined pipeline is generated. When a best version of the refined pipeline is not identified, the method returns to operation 616 an continues to incrementally train the refined pipeline. When, however, the best version of the refined pipeline is identified, the best version of the pipeline is output at operation 620, and the method ends at operation 622.
Various embodiments of the invention are described herein with reference to the related drawings. Alternative embodiments of the invention can be devised without departing from the scope of this invention. Various connections and positional relationships (e.g., over, below, adjacent, etc.) are set forth between elements in the following description and in the drawings. These connections and/or positional relationships, unless specified otherwise, can be direct or indirect, and the present invention is not intended to be limiting in this respect. Accordingly, a coupling of entities can refer to either a direct or an indirect coupling, and a positional relationship between entities can be a direct or indirect positional relationship. Moreover, the various tasks and process steps described herein can be incorporated into a more comprehensive procedure or process having additional steps or functionality not described in detail herein.
One or more of the methods described herein can be implemented with any or a combination of the following technologies, which are each well known in the art: a discrete logic circuit(s) having logic gates for implementing logic functions upon data signals, an application specific integrated circuit (ASIC) having appropriate combinational logic gates, a programmable gate array(s) (PGA), a field programmable gate array (FPGA), etc.
For the sake of brevity, conventional techniques related to making and using aspects of the invention may or may not be described in detail herein. In particular, various aspects of computing systems and specific computer programs to implement the various technical features described herein are well known. Accordingly, in the interest of brevity, many conventional implementation details are only mentioned briefly herein or are omitted entirely without providing the well-known system and/or process details.
In some embodiments, various functions or acts can take place at a given location and/or in connection with the operation of one or more apparatuses or systems. In some embodiments, a portion of a given function or act can be performed at a first device or location, and the remainder of the function or act can be performed at one or more additional devices or locations.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. 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, element components, and/or groups thereof.
The corresponding structures, materials, acts, and equivalents of all means or step plus function 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 present disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to 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 disclosure. The embodiments were chosen and described in order to best explain the principles of the disclosure and the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various embodiments with various modifications as are suited to the particular use contemplated.
The diagrams depicted herein are illustrative. There can be many variations to the diagram or the steps (or operations) described therein without departing from the spirit of the disclosure. For instance, the actions can be performed in a differing order or actions can be added, deleted or modified. Also, the term “coupled” describes having a signal path between two elements and does not imply a direct connection between the elements with no intervening elements/connections therebetween. All of these variations are considered a part of the present disclosure.
The following definitions and abbreviations are to be used for the interpretation of the claims and the specification. As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having,” “contains” or “containing,” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a composition, a mixture, process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but can include other elements not expressly listed or inherent to such composition, mixture, process, method, article, or apparatus.
Additionally, the term “exemplary” is used herein to mean “serving as an example, instance or illustration.” Any embodiment or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or designs. The terms “at least one” and “one or more” are understood to include any integer number greater than or equal to one, i.e. one, two, three, four, etc. The terms “a plurality” are understood to include any integer number greater than or equal to two, i.e. two, three, four, five, etc. The term “connection” can include both an indirect “connection” and a direct “connection.”
The terms “about,” “substantially,” “approximately,” and variations thereof, are intended to include the degree of error associated with measurement of the particular quantity based upon the equipment available at the time of filing the application. For example, “about” can include a range of±8% or 5%, or 2% of a given value.
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 instruction 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 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 descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments described herein.
