Workload Allocation Method and System Based on Computational Gravity within Different Computational Paradigms

Abstract

It provides for distributing a computing workload among computing nodes in different computing paradigms. The computing gravities of each computing node in the cloud computing paradigm within the system Internet and each computing node in the client network computing paradigm are calculated. Based on the calculated computing gravities of each corresponding computing node within the system Internet, each component part of the algorithm is distributed to the appropriate computing nodes in the cloud computing paradigm and the client network computing paradigm. The computing workload of each component part of the algorithm is distributed to the corresponding computing nodes in the cloud computing paradigm and the client network computing paradigm having the corresponding component part of the algorithm for processing.

Description

Technical Field

[0001] The present disclosure generally relates to content delivery networks and, more particularly, to the computing gravity of each computing node within different computing paradigms of the Internet of computing systems in order to distribute the computing workload of large applications having complex artificial intelligence components among the computing nodes of different computing paradigms. Background Art

[0002] A content delivery network refers to a geographically distributed group of servers that work together to provide rapid distribution of data content. The content delivery network allows for the fast transfer of assets required to load data content, such as HTML pages, JavaScript files, style sheets, images, videos, etc. The popularity of content delivery network services continues to grow, and currently most network traffic is served through content delivery networks, including traffic from major websites (e.g., social media websites).

[0003] Although content delivery networks do not host content and cannot replace the need for web hosting, content delivery networks do help cache content at the network edge, which improves website performance. Many websites have difficulty meeting their performance requirements through traditional hosting services, which is why these websites choose content delivery networks. By leveraging caching to reduce hosting bandwidth, help prevent service interruptions, and improve security, content delivery networks are a popular choice for alleviating some of the problems associated with traditional web hosting.

[0004] Content delivery networks locate servers at the points of exchange between different networks. These points of exchange are the primary locations where different data providers connect in order to provide each other with access to traffic on different networks originating from them. Content delivery network edge servers are computers that exist at the logical poles or edges of a network. Edge servers generally act as a connection between different networks. The primary purpose of content delivery network edge servers is to cache data content as close as possible to the requesting client device, thereby reducing latency and improving load times.

[0005] Fog computing is an architecture that uses edge servers to perform a large amount of computing, storage, and communication. Fog networking consists of a control plane and a data plane. For example, on the data plane, fog computing enables computing services to reside at the edge of the network rather than on servers in the data center. Compared with cloud computing, fog computing emphasizes proximity to end users and client devices, dense geographical distribution and local resource pools, latency reduction and backbone bandwidth savings for better quality of service, and edge analytics / stream mining, which results in an excellent user experience. Fog computing facilitates the operation of computing, storage, and networking services between endpoint devices and cloud computing data centers. While edge computing generally refers to the location where services are instantiated, fog computing means that communication, computing, storage resources, and services are distributed on or near devices and systems under the control of end users.

[0006] Large-scale computing systems with complex data processing pipelines typically run on hybrid clouds. A data processing pipeline is a set of serially connected data processing devices, where the output of one device is the input of the next serially connected device. Ad-hoc services can be obtained through a hybrid cloud of stateless microservices that accept data as a payload. Platform as a service (e.g., Kubernetes with Docker) provides many computing resources for these compute-intensive applications. Other computer algorithms that require high input / output, network bandwidth, and dedicated hardware requirements need infrastructure as a service or a shareable machine with virtual machines that traditional Kubernetes cannot supply. However, the traditional cloud computing paradigm breaks down with high user demand.

[0007] In these cases of high usage demand, the original servers are shielded by content delivery network edge servers. The edge servers provide a web acceleration layer for serving static content from the servers. However, artificial intelligence-based applications and other dynamic content generation platforms create petabytes of data every day. The amount of information can be represented by a combinatorial explosion in the feature space. The combination of billions of requests and dynamically generated data makes content delivery network edge computing and hybrid cloud computing insufficient. Summary of the Invention

[0008] According to an illustrative embodiment, a computer-implemented method for distributing a computing workload among computing nodes of different computing paradigms is provided. The computing gravity of each computing node in the cloud computing paradigm and each computing node in the client network computing paradigm within the system Internet is calculated. Each component part of the algorithm is distributed to the appropriate computing nodes in the cloud computing paradigm and the client network computing paradigm based on the calculated computing gravity of each corresponding computing node within the system Internet. The computing workload of each component part of the algorithm is distributed to the corresponding computing nodes in the cloud computing paradigm and the client network computing paradigm having the corresponding component part of the algorithm for processing. According to other illustrative embodiments, a computer system and a computer program product for distributing a computing workload among computing nodes of different computing paradigms are provided.

[0009] According to different illustrative embodiments, the results of each corresponding computing workload corresponding to each component part of the algorithm are cached on a content delivery network edge server, and the content delivery network edge server provides the results to other client computing nodes without repeating the calculation. Further, the different illustrative embodiments identify the component parts of the algorithm located in the cloud computing paradigm of the system Internet. In addition, the different illustrative embodiments receive an input for deploying the component parts of the algorithm in the system Internet, and calculate the computing gravity of each computing node in the cloud computing paradigm and each computing node in the client network computing paradigm for the component parts of the algorithm to be deployed in the system Internet. Then, the different illustrative embodiments determine the computing node having the highest computing gravity score in the system Internet for deploying the component parts of the algorithm based on the computing gravity corresponding to each computing node in the cloud computing paradigm and each computing node in the client network computing paradigm, and deploy the component parts of the algorithm to the computing node having the highest computing gravity score in the system Internet.

[0010] Thus, the different illustrative embodiments distribute the computing workload of the component parts of the algorithm among the computing nodes of different computing paradigms by calculating the computing gravity of each computing node within different computing paradigms of the system Internet, thereby providing technical effects and practical applications in the field of data processing. Therefore, the advantages of the different illustrative embodiments include: reducing the workload of computing nodes within different computing paradigms, reducing network latency, reducing loading time, increasing data transmission speed and performance, and reducing the response time to user requests. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Embodiments of the present invention will now be described by way of example only with reference to the accompanying drawings, in which:

[0012] Figure 1Is a graphical representation of a network of a data processing system that can implement an illustrative embodiment;

[0013] Figure 2 Is a diagram of a data processing system that can implement an illustrative embodiment;

[0014] Figure 3 Is a diagram showing a cloud computing environment that can implement an illustrative embodiment;

[0015] Figure 4 Is a diagram showing an example of an abstraction layer of a cloud computing environment according to an illustrative embodiment;

[0016] Figure 5 Is a diagram showing an example of a system Internet according to an illustrative embodiment;

[0017] Figure 6 Is a flowchart showing a process for deploying an artificial intelligence application within a system Internet according to an illustrative embodiment; and

[0018] Figure 7 Is a flowchart showing a process for distributing the computational workload of component parts of an artificial intelligence application based on computational gravity within a system Internet according to an illustrative embodiment. Detailed Description

[0019] The present invention can be a system, method, and / or computer program product of any possible level of technological detail 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 execute aspects of the present invention.

[0020] A computer-readable storage medium can be a tangible device that retains and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example but 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 computer-readable storage media includes the following: a portable computer disk, 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 disc (DVD), a memory stick, a floppy disk, a mechanical encoding device such as a punched card or a raised structure in a groove having instructions recorded thereon, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium should not be construed as a transitory signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., an optical pulse passing through an optical fiber cable) or an electrical signal transmitted through a wire.

[0021] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a corresponding computing / processing device via a network (e.g., the Internet, a local area network, a wide area network, and / or a wireless network), or to an external computer or external storage device. The network can include 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 the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium within the corresponding computing / processing device.

[0022] The computer-readable program instructions for carrying out operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuits, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages (such as Smalltalk, C++, etc.) and procedural programming languages (such as the "C" programming language or similar programming languages). The computer-readable program instructions may be executed 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 case, 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 may be connected to an external computer (e.g., using an Internet service provider via the Internet). In some embodiments, an electronic circuit, including, for example, a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), can execute the computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuit, so as to carry out aspects of the present invention.

[0023] The present invention will be described below with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0024] These computer-readable program instructions can be provided to a processor of a computer or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in one or more boxes of the flowchart and / or block diagram. These computer-readable program instructions may also be stored in a computer-readable storage medium that causes a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer-readable storage medium storing the instructions comprises an article of manufacture including instructions which implement aspects of the functions / acts specified in one or more boxes of the flowchart and / or block diagram.

[0025] The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, such that a series of operational steps are performed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, so that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions / acts specified in one or more boxes of the flowchart and / or block diagram.

[0026] 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. Accordingly, each box 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. In some alternative implementations, the functions noted in the boxes may occur out of the order noted in the figures. For example, two boxes shown in succession may, in fact, be accomplished as one step, executed simultaneously, substantially simultaneously, in part, or entirely in a time-overlapped manner, or the boxes may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each box of the block diagrams and / or flowchart, and combinations of boxes in the block diagrams and / or flowchart, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

[0027] Now referring to the drawings, and specifically referring to Figures 1 - 5 , there is provided a diagram of a data processing environment in which illustrative embodiments may be implemented. It should be understood that Figures 1 - 5 is only an example and is not intended to assert or imply any limitation to the environments in which different embodiments may be implemented. Many modifications may be made to the depicted environments.

[0028] Figure 1Depicts a graphical representation of a network of data processing systems in which illustrative embodiments may be implemented. The network data processing system 100 is a network of computers, data processing systems, and other devices (e.g., the system Internet) in which illustrative embodiments may be implemented. The network data processing system 100 includes a network 102, which is a medium for providing communication links between computers, data processing systems, and other devices connected together within the network data processing system 100. For example, the network 102 may be a content delivery network that includes, for example, edge servers with connections such as wired communication links, wireless communication links, fiber optic cables, etc.

[0029] In the depicted example, server 104 and server 106 are connected to network 102 along with storage 108. Server 104 and server 106 may be, for example, server computers with high-speed connections to network 102. Additionally, server 104 and server 106 may provide one or more services to client devices, such as artificial intelligence data services, etc. Moreover, it should be noted that server 104 and server 106 may each represent multiple server computers, such as centralized classical computers and quantum computers in a cloud computing paradigm or environment.

[0030] Clients 110, 112, and 114 are also connected to network 102. Clients 110, 112, and 114 are clients of server 104 and server 106. Clients 110, 112, and 114 represent multiple client-side computing nodes or devices within a client network computing paradigm or environment. In this example, clients 110, 112, and 114 are shown as desktop computers or personal computers having wired communication links to network 102. However, it should be noted that clients 110, 112, and 114 are merely examples and may represent other types of data processing systems having wired or wireless communication links to network 102, such as network computers, laptop computers, handheld computers, smartphones, smartwatches, smart TVs, smart appliances, smart homes, smart vehicles, gaming devices, self-service terminals, etc. Users of clients 110, 112, and 114 may utilize clients 110, 112, and 114 to request services provided by server 104 and server 106.

[0031] The storage device 108 is a network storage device capable of storing any type of data in a structured or unstructured format. Additionally, the storage device 108 can represent multiple network storage devices. Further, the memory 108 can store artificial intelligence applications or computer algorithms having multiple component parts, identifiers and network addresses of the multiple component parts of each respective artificial intelligence application or computer algorithm, the computational complexity and computational requirements of each respective component part of each respective artificial intelligence application or algorithm, etc. Additionally, the storage device 108 can store other types of data, such as authentication or credential data, which can include, for example, a username, password, and biometric data associated with a user of the client device.

[0032] In addition, it should be noted that the network data processing system 100 can include any number of additional servers, clients, storage devices, and other devices not shown. Program code located in the network data processing system 100 can be stored on a computer-readable storage medium and downloaded to a computer or other data processing device for use. For example, the program code can be stored on a computer-readable storage medium on the server 104 and downloaded through the network 102 to the client 110 for use on the client 110.

[0033] In the depicted example, the network data processing system 100 can be implemented as many different types of communication networks, such as the Internet, an intranet, a local area network (LAN), a wide area network (WAN), a telecommunications network, or any combination thereof. Figure 1 This is intended to be only an example and not an architectural limitation for different illustrative embodiments.

[0034] Now referring to Figure 2 , a diagram of a data processing system in accordance with an illustrative embodiment is depicted. The data processing system 200 is an example of a computer (such as Figure 1 the server 104 or the client 110 in

[0035] which the computer-readable program code or instructions implementing the processing of the illustrative embodiment can be located. In this example, the data processing system 200 includes a communication structure 202 that provides communication between a processor unit 204, a memory 206, a permanent storage device 208, a communication unit 210, an input / output (I / O) unit 212, and a display 214.

[0036] Memory 206 and persistent storage 208 are examples of storage devices 216. A computer-readable storage device is any hardware capable of storing information (such as, but not limited to, data, computer-readable program code in functional form, and / or other suitable information based on transient or persistent). Further, computer-readable storage devices exclude propagation media. In these examples, memory 206 can be, for example, random access memory (RAM) or any other suitable volatile or non-volatile storage device, such as flash memory. Persistent storage 208 can take various forms depending on the particular implementation. For example, persistent storage 208 can include one or more devices. For example, persistent storage 208 can be a disk drive, solid state drive, rewritable optical disc, rewritable magnetic tape, or some combination of the above. The medium used by persistent storage 208 can be removable. For example, a removable hard disk drive can be used for persistent storage 208.

[0037] In this example, communication unit 210 provides communication with other computers, data processing systems, and devices via a network (such as, Figure 1 the network 102 in). Communication unit 210 can provide communication by using both physical and wireless communication links. The physical communication link can utilize, for example, wired, cable, universal serial bus, or any other physical technology to establish a physical communication link for data processing system 200. The wireless communication link can utilize, for example, shortwave, high frequency, ultra-high frequency, microwave, wireless fidelity (Wi-Fi), Bluetooth technology, global system for mobile communications (GSM), code division multiple access (CDMA), second generation (2G), third generation (3G), fourth generation (4G), 4G long term evolution (LTE), advanced LTE, fifth generation (5G), or any other wireless communication technology or standard to establish a wireless communication link for data processing system 200.

[0038] Input / output unit 212 allows for the input and output of data with other devices that can be connected to data processing system 200. For example, input / output unit 212 can provide a connection for user input through a keypad, keyboard, mouse, microphone, and / or some other suitable input device. Display 214 provides a mechanism for displaying information to the user and can include touchscreen capabilities that allow the user to make on-screen selections through, for example, a user interface or input data.

[0039] Instructions for an operating system, applications, and / or programs can be located in storage device 216, which communicates with processor unit 204 through communication structure 202. In this illustrative example, the instructions are in functional form on permanent storage device 208. These instructions can be loaded into memory 206 for execution by processor unit 204. The processes of different embodiments can be performed by processor unit 204 using computer-implemented instructions that can be located in a memory (e.g., memory 206). These program instructions are referred to as program code, computer-usable program code, or computer-readable program code that can be read and executed by a processor in processor unit 204. In different implementations, the program instructions can be implemented on different physical computer-readable storage devices, such as memory 206 or permanent storage device 208.

[0040] Program code 218 is in functional form on a selectively removable computer-readable medium 220 and can be loaded onto or transmitted to data processing system 200 for execution by processor unit 204. Program code 218 and computer-readable medium 220 form a computer program product 222. In one example, computer-readable medium 220 can be a computer-readable storage medium 224 or a computer-readable signal medium 226. Computer-readable storage medium 224 can include, for example, an optical disc or a magnetic disk that is inserted into or placed in a drive or other device that is part of permanent storage device 208 for transfer onto a storage device (such as a hard disk drive) that is part of permanent storage device 208. Computer-readable storage medium 224 can also take the form of a permanent storage device, such as a hard disk drive, a thumb drive, or a flash memory that is connected to data processing system 200. In some instances, computer-readable storage medium 224 may not be removable from data processing system 200.

[0041] Alternatively, computer-readable signal medium 226 can be used to transmit program code 218 to data processing system 200. Computer-readable signal medium 226 can be, for example, a propagated data signal that contains program code 218. For example, computer-readable signal medium 226 can be an electromagnetic signal, an optical signal, and / or any other suitable type of signal. These signals can be transmitted through a communication link (e.g., a wireless communication link, a fiber optic cable, a coaxial cable, a wire, and / or any other suitable type of communication link). In other words, in the illustrative example, the communication link and / or connection can be physical or wireless. The computer-readable medium can also take the form of a non-tangible medium, such as a communication link or a wireless transmission that contains program code.

[0042] In some illustrative embodiments, program code 218 can be downloaded from another device or data processing system to the permanent storage device 208 via a network through a computer-readable signal medium 226 for use within the data processing system 200. For example, program code stored in a computer-readable storage medium in a data processing system can be downloaded to the data processing system 200 via a network from the data processing system. The data processing system providing the program code 218 can be a server computer, a client computer, or some other device capable of storing and transmitting the program code 218.

[0043] The different components shown for the data processing system 200 do not imply an architectural limitation on the manner in which different embodiments can be implemented. Different illustrative embodiments can be implemented in a data processing system that includes components in addition to or in place of those shown for the data processing system 200. Figure 2 The other components shown can be different from the illustrative examples shown. Different embodiments can be implemented using any hardware device or system capable of executing program code. As an example, the data processing system 200 can include organic components integrated with inorganic components and / or can consist entirely of organic components excluding humans. For example, a storage device can be composed of an organic semiconductor.

[0044] As another example, a computer-readable storage device in the data processing system 200 is any hardware device capable of storing data. Memory 206, permanent storage device 208, and computer-readable storage medium 224 are examples of physical storage devices in tangible form.

[0045] In another example, a bus system can be used to implement the communication structure 202 and can include one or more buses, such as a system bus or an input / output bus. Of course, any suitable type of architecture that provides data transfer between different components or devices attached to the bus system can be used to implement the bus system. Additionally, the communication unit can include one or more devices for sending and receiving data, such as a modem or a network adapter. Further, the memory can be, for example, the memory 206 or a cache such as found in an interface and a memory controller hub that may be present in the communication structure 202.

[0046] It should be understood that although this disclosure includes a detailed description of cloud computing, the implementation of the teachings recited herein is not limited to cloud computing environments. Instead, the illustrative embodiments can be implemented in conjunction with any other type of computing environment now known or later developed. Cloud computing is a model for service delivery that enables convenient, on-demand network access to a shared pool of configurable computing resources such as networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services, which can be rapidly provisioned and released with minimal management effort or interaction with the provider of the service. The cloud model can include at least five characteristics, at least three service models, and at least four deployment models.

[0047] These characteristics can include, for example, on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. On-demand self-service allows cloud consumers to unilaterally and automatically provision computing capabilities such as server time and network storage on demand without human interaction with the provider of the service. Broad network access provides the ability to be available over a network and accessed through standard mechanisms that facilitate the use of heterogeneous thin client platforms or thick client platforms (e.g., mobile phones, laptop computers, and personal digital assistants). Resource pooling allows the provider's computing resources to be pooled to serve multiple consumers using a multi-tenant model, where different physical and virtual resources are dynamically allocated and reallocated as needed. There is a sense of location independence, as consumers generally do not have control or knowledge of the exact location of the resources provided, but may be able to specify a location at a higher level of abstraction, such as a country, state, or data center. Rapid elasticity provides the ability to be rapidly and elastically provisioned (automatically in some cases) to quickly scale out and quickly release to quickly scale in. For the consumer, the capabilities available for provisioning generally appear to be unlimited and can be purchased in any quantity at any time. Measured service allows cloud systems to automatically control and optimize resource use by leveraging metering capabilities 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.

[0048] Service models can include, for example, Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Software as a Service provides the consumer with the ability to use an application that the provider runs on a cloud infrastructure. The application can be accessed from different client devices through a thin client interface such as a web browser (e.g., web-based email). The consumer does not manage or control the underlying cloud infrastructure, including the network, servers, operating systems, storage, or even the individual application capabilities, with the possible exception of limited user-specific application configuration settings. Platform as a Service provides the consumer with the ability to deploy onto the cloud infrastructure an application created or acquired by the consumer using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure, including the network, servers, operating systems, or storage, but has control over the deployed application and possibly the application hosting environment configuration. Infrastructure as a Service provides the consumer with the ability to provision processing, storage, networks, and other fundamental computing resources where the consumer can deploy and run arbitrary software, which can include operating systems and application programs. The consumer does not manage or control the underlying cloud infrastructure but has control over the operating systems, storage, deployed applications, and possibly limited control over selected networking components such as, for example, a host firewall.

[0049] Deployment models can include, for example, private cloud, community cloud, public cloud, and hybrid cloud. A private cloud is a cloud infrastructure operated solely for an organization. The private cloud can be managed by the organization or a third party and can exist on-premises or off-premises. A community cloud is a cloud infrastructure shared by several organizations and supports a specific community that shares concerns such as missions, security requirements, policies, and compliance considerations. The community cloud can be managed by the organization or a third party and can exist on-premises or off-premises. A public cloud is a cloud infrastructure available to the general public or a large industry group and is owned by an organization that sells cloud services. A hybrid cloud is a cloud infrastructure composed of two or more clouds (such as, for example, private cloud, community cloud, and public cloud) that remain unique entities but are bound together by standardized or proprietary technologies that enable data and application portability (such as, for example, cloud bursting for load balancing between clouds).

[0050] Cloud computing environments are service-oriented, focusing on statelessness, low coupling, modularity, and semantic interoperability. The core of cloud computing is an infrastructure that includes a network of interconnected nodes.

[0051] Now refer to Figure 3, which depicts a diagram of a cloud computing environment in which illustrative embodiments can be implemented. In this illustrative example, cloud computing environment 300 includes one or more cloud computing nodes 310 that a local computing device used by a cloud consumer can communicate with. The local computing device is, for example, a personal digital assistant or a smart phone 320A, a desktop computer 320B, a laptop computer 320C, and / or an automotive computer system 320N. The cloud computing nodes 310 can be, for example, Figure 1 the servers 104 and 106 in Figure 1 and can include classical computers and quantum computers. The local computing devices 320A - 320N can be, for example,

[0052] the clients 110 - 114 in

[0053] Now referring to Figure 4 , which depicts a diagram of an illustrative abstract model layer. The set of functional abstraction layers shown in this illustrative example can be provided by a cloud computing environment, such as Figure 3 the cloud computing environment 300 in Figure 4 . It should be understood in advance that the components, layers, and functions shown in

[0054] The abstraction layer of cloud computing environment 400 includes a hardware and software layer 402, a virtualization layer 404, a management layer 406, and a workload layer 408. The hardware and software layer 402 includes the hardware and software components of the cloud computing environment. The hardware components can include, for example, hosts 410, servers 412 based on RISC (Reduced Instruction Set Computer) architecture, servers 414, blade servers 416, storage devices 418, and network and networking components 420. In some illustrative embodiments, the software components can include, for example, network application server software 422 and database software 424.

[0055] The virtualization layer 404 provides an abstraction layer from which the following examples of virtual entities can be provided: virtual server 426; virtual storage device 428; virtual network 430, including virtual private networks; virtual applications and operating systems 432; and virtual clients 434.

[0056] In one example, the management layer 406 can provide the functions described below. Resource provisioning 436 provides for the dynamic procurement of computing resources and other resources for performing tasks within a cloud computing environment. Metering and pricing 438 provides cost tracking as resources are utilized within the cloud computing environment and bills or invoices for the consumption of these resources. In one example, these resources can include application software licenses. Security provides authentication for cloud consumers and tasks, as well as protection for data and other resources. The user portal 440 provides access to the cloud computing environment for consumers and system administrators. Service level management 442 provides cloud computing resource allocation and management such that the required service levels are met. Service level agreement (SLA) planning and fulfillment 444 provides for the pre-arrangement and procurement of cloud computing resources based on SLA-expected future demands.

[0057] The workload layer 408 provides examples of functions that can utilize the cloud computing environment. Example workloads and functions that can be provided by the workload layer 408 can include maps and navigation 446, software development and lifecycle management 448, virtual classroom education delivery 450, data analysis processing 452, transaction processing 454, and computational workload distribution management 456.

[0058] Fog computing gravitational components (e.g., Internet of Things) can suggest which parts of a computer algorithm can be partitioned into different computing paradigms. It should be noted that the steps of a computer algorithm can run in parallel, run on dedicated hardware, etc. An illustrative embodiment calculates fog computing gravity such that the illustrative embodiment can push a computer algorithm and corresponding static data to the client device edge for computing. Moving the algorithm to the client device edge using various programming languages such as Swift, C#, or JavaScript creates a pipeline for fog computing.

[0059] An illustrative embodiment distributes a computer algorithm or a component part of the algorithm out to the client device edge such that each client-side edge computer can execute a part of the computational workload of the algorithm and then cache the result of that part of the computational workload on a content delivery network edge server for utilization by other client computers without having to repeat the computation. The illustrative embodiment splits and transports the algorithm based on fog computing gravity. Fog computing gravity is the pull of the computational workload on a particular edge computing node based on the fit of the particular computational workload to that particular edge computing node or device.

[0060] Exemplary embodiments bridge content delivery network edge computing and hybrid cloud computing within large artificial intelligence applications having highly complex artificial intelligence components. For example, a virtual professional sports application may utilize over 14 artificial intelligence data insight services and customized mathematical and machine learning methods. Each day, over 10 billion user requests from client users may be received by the artificial intelligence data services. Currently, content delivery network edge computing and hybrid cloud computing cannot pre-compute artificial intelligence data insights due to, for example, the combined feature space of optimizing team lineups, suggesting player trades, or evaluating team value. Exemplary embodiments enable appropriate component parts of an artificial intelligence application (or computer algorithm) to be pulled up to a client-side edge computer node via fog computing gravity.

[0061] Exemplary embodiments may transform other component parts of a computer algorithm into a different computing paradigm, such as quantum computing. In this context, quantum computing is a computing accelerator for specific problems. Exemplary embodiments may accelerate the application and training of support vector machines by using a quantum computer within a cloud computing paradigm. A support vector machine is a supervised learning model with an associated learning algorithm that analyzes data for classification and regression analysis. Neuromorphic computer chips (e.g., True North chip) may accelerate the processing of neural networks, such as artificial intelligence applications.

[0062] Exemplary embodiments distribute computing workloads among a centralized cloud-based classical computer, a centralized cloud-based quantum computer, and client network edge computers within a system internet composed of different computing paradigms. Exemplary embodiments cache the results of computing the workloads of computers from different computing paradigms on a content delivery network edge server, which provides the results to client network computing nodes without repeating the computations. When computing fog computing gravity, exemplary embodiments consider the complexity of the computations associated with different component parts of the computer algorithm, the availability of computing nodes, and the computing workload fitness for determining which computing node should be assigned a given computing workload.

[0063] Edge computing brings processing closer to the data source. In other words, data does not need to be sent to a remote cloud or other centralized system for processing. By eliminating the distance and time spent sending data to a centralized system for processing, exemplary embodiments improve data transmission speed and performance. It should be noted that data transmission includes requests from users.

[0064] Fog computing is the standard that defines how edge computing works. Fog computing facilitates the operation of computing, storage, and networking services between edge computing devices and cloud computing devices. Cloud computing provides computing resources as a service via the internet, which is a centrally available utility.

[0065] Exemplary embodiments utilize a fog attraction component (which includes a computing edge node) to determine the emergence of an algorithm transmission pipeline. The algorithm transmission pipeline preprocesses data and prepares it for computation by a target edge computing node or device. Exemplary embodiments utilize the tangency of the algorithm transmission pipeline to move a particular operator (e.g., an artificial intelligence application component) to a different computing paradigm component on an edge computing device. In other words, the tangency of the algorithm transmission pipeline moves the computation to a different computing paradigm, which requires a completely different data representation. Exemplary embodiments enhance the algorithm transmission pipeline affinity based on a problem (i.e., a computing workload) that fits to an edge server of a content delivery network. Exemplary embodiments also enhance the algorithm transmission pipeline affinity based on a problem (i.e., a computing workload) that fits to cloud computing. Exemplary embodiments determine an emergency cloud from a combination of different computing paradigms (e.g., different networks) and edge computing devices that have been used to solve a full computing problem (i.e., the result of a full computing workload distributed between different computing paradigms and edge devices).

[0066] Exemplary embodiments place a computational attraction equation on each endpoint device of a system internet that includes memory, chips, cores, disks, etc. Exemplary embodiments assign a force that pulls at an algorithm or a portion of an algorithm located on a cloud to each potential algorithm transmission pipeline. Additionally, the cloud has an attracting gravitational force at the aggregation of algorithms located at an edge server, which is all the algorithms running in the system internet. An example of the computational attraction equation is as follows:

[0067]

[0068] where “F” is the force; “G” is the gravitational constant equal to the signature of the client computation, network bandwidth, request volume, and size of the data payload; “m” is the mass; “r” is the radius of the network; and

[0069]

[0070] where “x” describes the computing problem (e.g., how many computations); “c” describes the hardware of the computing node; “B t ” is the network bandwidth of the computing node; “V r ” is the request volume; “P s ” is the data size; “S” is a signature function that retrieves the signature of the model used to solve the computing problem; and “P i ” is the problem importance, which is typically a constant similar to the physical gravitational constant 9.8 m / s 2 . It should be noted that exemplary embodiments can adjust the problem importance constant to create a relative importance factor for a specific computing node.

[0071] The input data pattern to the vector model, which is input into a deep learning algorithm (e.g., an artificial intelligence application) along with the capabilities of computer nodes, provides softmax classes and probabilities for a well - computing pattern (i.e., a type of computing paradigm, such as quantum computing). Softmax assigns decimal probabilities to each class in a multi - class problem. These decimal probabilities must add up to 1.0. Softmax is implemented through a neural network layer before the output layer. The softmax layer must have the same number of nodes as the output layer. Softmax calculates the probability for each possible class. When constructing a classifier for a question with only one correct answer, softmax is applied to the raw output. Applying softmax takes into account all elements of the raw output in the denominator, which means that the different probabilities produced by the softmax function are related.

[0072] Each computing node in the computing nodes within the hybrid cloud calculates a computing gravity component score for each artificial intelligence application model or part of a machine learning pipeline (e.g., a set of artificial intelligence application models). The winning computing gravity component (i.e., the computing endpoint node with the highest computing gravity score) pulls the artificial intelligence application model to its location within the system internet.

[0073] Accordingly, the illustrative embodiments provide one or more technical solutions that overcome the technical problem of distributing computing workloads between different computing paradigms. Thus, these one or more technical solutions provide technical effects and practical applications in the field of data processing by means of the computing gravity of each computing node within different computing paradigms of the system internet, in order to distribute the computing workload of large applications with complex artificial intelligence components among the computing nodes of different computing paradigms. Therefore, the advantages of the illustrative embodiments include reduced computing node workload within different computing paradigms, reduced network latency, reduced loading time, increased data transfer speed and performance, and reduced response time to user requests.

[0074] Now refer to Figure 5 , a diagram depicting an example of the system internet according to an illustrative embodiment. The system internet 500 can be implemented in a network of data processing systems, such as Figure 1 the network data processing system 100 therein. The system internet 500 is a combination of network, hardware, and software for distributing the computing workload of large applications with complex artificial intelligence components among the computing nodes of different computing paradigms within the system internet 500 based on the calculated computing gravity corresponding to each respective computing node within different computing paradigms.

[0075] In this example, the system Internet 500 includes a cloud computing paradigm 502, a client network computing paradigm 504, and a content delivery network 506. The cloud computing paradigm 502 includes artificial intelligence applications 508, a centralized classical computer 510, and a centralized quantum computer 512. The artificial intelligence applications 508 can represent any type of artificial intelligence application or computer algorithm. The centralized classical computer 510 and the centralized quantum computer 512 can be, for example Figure 1 the servers 104 and 106 in

[0076] The client network computing paradigm 504 includes a client-side edge computer 514 and other client computers 516. The client-side edge computer 514 and the other client computers 516 can be, for example Figure 1 the clients 110 and 112 in

[0077] The content delivery network 506 includes an edge server 518. The edge server 518 is a computer that exists at the logical edge of the content delivery network 506 and serves as a connection point between different computing paradigms, the cloud computing paradigm 502 and the client network computing paradigm 504.

[0078] The artificial intelligence applications 508 include component parts 520. The component parts 520 represent multiple different software components that make up the artificial intelligence applications 508. Each of the component parts in the component parts 520 has a different function, where different computational complexities require different computational resources.

[0079] The illustrative embodiment distributes different component parts of the artificial intelligence applications 508 among the computing nodes of different computing paradigms within the system Internet 500 based on the calculated computational gravities corresponding to each respective computing node within different computing paradigms. For example, the illustrative embodiment uses a computational gravity equation (such as the computational gravity equation shown above) to calculate the computational gravity 522 of the centralized classical computer 510, the computational gravity 524 of the centralized quantum computer 512, and the computational gravity 526 of the client-side edge computer 514 based on, for example, the computational complexity, the availability of the computing node, and the fit of the computing node's computational workload corresponding to each component part 520 of the artificial intelligence applications 508.

[0080] Based on the calculated computational gravities 522 of the centralized classical computer 510, 524 of the centralized quantum computer 512, and 526 of the client-side edge computer 514, the illustrative embodiments distribute specific component parts of the component part 520, such as the artificial intelligence component part 528, the artificial intelligence component part 530, and the artificial intelligence component part 532, to the centralized classical computer 510, the centralized quantum computer 512, and the client-side edge computer 514, respectively. Further, based on which specific component part in the component part 520 is located on a specific computing node, such as the artificial intelligence component part 528 located on the centralized classical computer 510, the artificial intelligence component part 530 located on the centralized quantum computer 512, and the artificial intelligence component part 532 located on the client-side edge computer 514, the illustrative embodiments assign the corresponding computational workloads to that specific computing node, such as the workload 534 on the centralized classical computer 510, the workload 536 on the centralized quantum computer 512, and the workload 538 on the client-side edge computer 514.

[0081] Each corresponding computational workload distributed on the centralized classical computer 510, the centralized quantum computer 512, and the client-side edge computer 514 generates a computational result, e.g., the result 540, the result 542, and the result 544. The illustrative embodiments cache the results 540, 542, and 544 on the edge server, such as the cached computational result 546 on the edge server 518, within the content delivery network 506. It should be noted that the cached computational result 546 represents multiple cached computational results stored on multiple different edge servers within the content delivery network 506. Additionally, the exemplary implementation provides the multiple cached computational results stored on multiple different edge servers to other client computers, such as the other client computer 516, such that the other client computers do not have to repeat the computation. Further, the illustrative embodiments may form an emergency cloud composed of the centralized classical computer 510, the centralized quantum computer 512, and the client-side edge computer 514, which helps to generate the results of the complete computational workload distributed among different computing paradigms and computing nodes.

[0082] Now refer to Figure 6 , which shows a flowchart of a process for deploying an artificial intelligence application within the system Internet according to an illustrative embodiment. Figure 6 The process shown in Figure 2 can be implemented in a computer such as the data processing system 200 in

[0083] The process begins when the computer receives an input for calculating the computational gravity of multiple computing nodes in multiple different computing paradigms (step 602). The computer identifies each computing node in the cloud computing paradigm, each edge computing node in the client network computing paradigm, and each edge computing node in the content delivery network computing paradigm (step 604). The computer forms a system internet based on the identification of each computing node in the cloud computing paradigm, each edge computing node in the client network computing paradigm, and each edge computing node in the content delivery network computing paradigm (step 606).

[0084] Subsequently, the computer receives an input for deploying component parts of an artificial intelligence application in the system internet (step 608). The computer calculates the computational gravity of each computing node in the cloud computing paradigm, each edge computing node in the client network computing paradigm, and each edge computing node in the content delivery network computing paradigm for the component parts of the artificial intelligence application to be deployed in the system internet (step 610). The computer determines the computing node with the highest computational gravity score in the system internet based on the computational gravity of each computing node in the cloud computing paradigm, each edge computing node in the client network computing paradigm, and each edge computing node in the content delivery network computing paradigm for deploying the component parts of the artificial intelligence application (step 612).

[0085] The computer deploys the component parts of the artificial intelligence application to the computing node with the highest computational gravity score in the system internet (step 614). Additionally, the computer moves the cloud containers of the cloud computing paradigm to form an emergency cloud, which is a combination of different computing paradigms and computing nodes that results in generating a complete computational workload distributed among different computing paradigms and computing nodes. It should be noted that the emergency cloud includes the computing node with the highest computational gravity score.

[0086] Then, the computer determines whether it has received an input for deploying another component of the artificial intelligence application in the system internet (step 618). If the computer determines that it has received an input for deploying another component of the artificial intelligence application in the system internet (the output of step 618 is "yes"), the process returns to step 610, where the computer calculates the computational gravity of each computing node in the cloud computing paradigm, each edge computing node in the client network computing paradigm, and each edge computing node in the content delivery network computing paradigm for the other component of the artificial intelligence application to be deployed in the system internet. If the computer determines that it has not received an input for deploying another component of the artificial intelligence application in the system internet (the output of step 618 is "no"), then the process terminates thereafter.

[0087] Now refer to Figure 7, which shows a flowchart of a process for distributing the computational workload of components of an artificial intelligence application based on computational gravity within a system Internet Figure 7 The process shown in Figure 2 can be implemented in a computer such as the data processing system 200 in

[0088] The process begins (step 702) when the computer identifies the components of an artificial intelligence application (or algorithm) located in the cloud computing paradigm of the system Internet. The computer calculates the computational gravity of each centralized classical computer and quantum computer in the cloud computing paradigm and each edge computer in the client network computing paradigm within the system Internet, taking into account the computational complexity, computer availability, and computer computational workload fitting corresponding to the components of the artificial intelligence application (step 704).

[0089] The computer distributes each component of the artificial intelligence application to an appropriate centralized classical computer in the cloud computing paradigm, a centralized quantum computer in the cloud computing paradigm, or an edge computer in the client network computing paradigm based on the calculated computational gravity of each corresponding computer (step 706). Further, the computer distributes the computational workload of each component of the artificial intelligence application to the corresponding centralized classical computer in the cloud computing paradigm, the centralized quantum computer in the cloud computing paradigm, or the edge computer in the client network computing paradigm having the corresponding component of the artificial intelligence application for processing (step 708).

[0090] The computer caches the results of each corresponding computational workload corresponding to each component of the artificial intelligence application on a content delivery network edge server, and the content delivery network edge server provides the results to other client computers without repeated calculation (step 710). Thereafter, the process terminates.

[0091] Thus, the illustrative embodiments of the present invention provide a computer-implemented method, computer system, and computer program product for distributing the computational workload of a large application with complex artificial intelligence components among computational nodes of different computational paradigms within a system Internet based on the calculated computational gravity corresponding to each corresponding computational node within different computational paradigms. The description of the various embodiments of the present invention has been presented for purposes of illustration, but is not intended to be exhaustive or limited to the disclosed embodiments. 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 terms used herein have been chosen to best explain the principles of the embodiments, the practical application, or the technical improvement of technologies found in the marketplace, or to enable those of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A computer - implemented method, comprising: Calculating the computational gravity of each computing node in the cloud computing paradigm and each computing node in the client - network computing paradigm within the system Internet based on the computational complexity, computing node availability, and computing node computational workload fitting corresponding to the component parts of the algorithm to be distributed within the system Internet; Distributing each component part of the algorithm to appropriate computing nodes in the cloud computing paradigm and the client - network computing paradigm based on the calculated computational gravity of each corresponding computing node within the system Internet; And Distributing the computational workload of each component part of the algorithm to the corresponding computing nodes having the corresponding component parts of the algorithm in the cloud computing paradigm and the client - network computing paradigm for processing.

2. The method according to claim 1, further comprising: Caching the results of each corresponding computational workload corresponding to each component part of the algorithm on a content delivery network edge server, and the content delivery network edge server providing the results to other client computing nodes without repeated calculation.

3. The method according to claim 1, further comprising: Identifying the component parts of the algorithm located in the cloud computing paradigm of the system Internet.

4. The method according to claim 1, further comprising: Receiving an input for deploying the component parts of the algorithm in the system Internet; And Calculating the computational gravity of each computing node in the cloud computing paradigm and each computing node in the client - network computing paradigm for the component parts of the algorithm to be deployed in the system Internet.

5. The method according to claim 4, further comprising: Determining the computing node with the highest computational gravity score in the system Internet for deploying the component parts of the algorithm based on the computational gravity corresponding to each computing node in the cloud computing paradigm and each computing node in the client - network computing paradigm; And Deploying the component parts of the algorithm to the computing node with the highest computational gravity score in the system Internet.

6. The method according to claim 1, further comprising: Moving the cloud containers of the cloud computing paradigm to form an emergency cloud, where the emergency cloud is a combination of the different computing paradigms and computing nodes for generating the results of the complete computational workload distributed among different computing paradigms and computing nodes.

7. The method according to claim 1, further comprising: Identifying each computing node in the cloud computing paradigm, each computing node in the client - network computing paradigm, and each edge computing node in the content delivery network computing paradigm; And Forming the system Internet based on the identification of each computing node in the cloud computing paradigm, each computing node in the client - network computing paradigm, and each edge computing node in the content delivery network computing paradigm.

8. The method according to claim 1, wherein The algorithm transfer pipeline pre - processes data to prepare the data for calculation by the target edge computing node.

9. The method according to claim 1, wherein The computing nodes in the cloud computing paradigm include a centralized classical computer and a centralized quantum computer, and among them, the computing nodes in the client network computing paradigm include edge computers.

10. The method according to claim 1, wherein The algorithm is an artificial intelligence application with complex artificial intelligence components.

11. A computer system, comprising: A bus system; A storage device connected to the bus system, wherein the storage device stores program instructions; and A processor connected to the bus system, wherein the processor executes the program instructions to: Calculate the computing gravity of each computing node in the cloud computing paradigm and each computing node in the client network computing paradigm within the system Internet based on the computing complexity, computing node availability, and computing node computing workload fitting corresponding to the component parts of the algorithm to be distributed within the system Internet; Distribute each component part of the algorithm to the appropriate computing nodes in the cloud computing paradigm and the client network computing paradigm based on the calculated computing gravity of each corresponding computing node within the system Internet; and Distribute the computing workload of each component part of the algorithm to the corresponding computing nodes in the cloud computing paradigm and the client network computing paradigm that have the corresponding component parts of the algorithm for processing.

12. The computer system according to claim 11, wherein, The processor also executes the program instructions to: Cache the results of each corresponding computing workload corresponding to each component part of the algorithm on a content delivery network edge server, and the content delivery network edge server provides the results to other client computing nodes without repeated calculation.

13. The computer system according to claim 11, wherein, The processor also executes the program instructions to: Identify the component parts of the algorithm located in the cloud computing paradigm of the system Internet.

14. The computer system according to claim 11, wherein The processor also executes the program instructions to: Receive an input for deploying the component parts of the algorithm in the system Internet; And Calculate the computing gravity of each computing node in the cloud computing paradigm and each computing node in the client network computing paradigm for the component parts of the algorithm to be deployed in the system Internet.

15. The computer system according to claim 14, wherein, The processor also executes the program instructions to: Determine the computing node with the highest computing gravity score in the system Internet for deploying the component parts of the algorithm based on the computing gravity corresponding to each computing node in the cloud computing paradigm and each computing node in the client network computing paradigm; And Deploy the component parts of the algorithm to the computing node with the highest computing gravity score in the system Internet.

16. The computer system according to claim 11, wherein, The processor also executes the program instructions to: Move the cloud containers of the cloud computing paradigm to form an emergency cloud, and the emergency cloud is a combination of different computing paradigms and computing nodes for generating the results of the complete computing workload distributed among different computing paradigms and computing nodes.

17. The computer system according to claim 11, wherein, The processor also executes the program instructions to: Identify each computing node in the cloud computing paradigm, each computing node in the client network computing paradigm, and each edge computing node in the content delivery network computing paradigm; And The system Internet is formed based on the identification of each computing node in the cloud computing paradigm, each computing node in the client network computing paradigm, and each edge computing node in the content delivery network computing paradigm.

18. A computer program product, comprising a computer-readable storage medium having program instructions therein that are executable by a computer to cause the computer to perform a method, the method comprising: Calculating the computational gravities of each computing node in the cloud computing paradigm and each computing node in the client network computing paradigm within the system Internet based on a computational complexity, computing node availability, and computing node computational workload fit corresponding to component parts of an algorithm to be distributed within the system Internet; Distributing each component part of the algorithm to appropriate computing nodes in the cloud computing paradigm and the client network computing paradigm based on the calculated computational gravities of each respective computing node within the system Internet; And Distributing the computational workload of each component part of the algorithm to the respective computing nodes having the corresponding component parts of the algorithm in the cloud computing paradigm and the client network computing paradigm for processing.

19. The computer program product according to claim 18, further comprising: Caching the results of each respective computational workload corresponding to each component part of the algorithm on a content delivery network edge server, the content delivery network edge server providing the results to other client computing nodes without repeated computation.

20. The computer program product according to claim 18, further comprising: Identifying the component parts of the algorithm that are in the cloud computing paradigm of the system Internet.

21. The computer program product according to claim 18, further comprising: Receiving an input for deploying the component parts of the algorithm in the system Internet; And Calculating the computational gravities of each computing node in the cloud computing paradigm and each computing node in the client network computing paradigm for the component parts of the algorithm to be deployed in the system Internet.

22. The computer program product according to claim 21, further comprising: Determining, based on the computational gravities corresponding to each computing node in the cloud computing paradigm and each computing node in the client network computing paradigm, the computing node having the highest computational gravity score in the system Internet for deploying the component parts of the algorithm; And Deploying the component parts of the algorithm to the computing node having the highest computational gravity score in the system Internet.

23. The computer program product according to claim 18, further comprising: Moving cloud containers of the cloud computing paradigm to form an emergency cloud, the emergency cloud being a combination of the different computing paradigms and computing nodes for generating the results of a complete computational workload distributed among different computing paradigms and computing nodes.

24. The computer program product according to claim 18, further comprising: Identify each computing node in the cloud computing paradigm, each computing node in the client network computing paradigm, and each edge computing node in the content delivery network computing paradigm; And Form the system Internet based on the identification of each computing node in the cloud computing paradigm, each computing node in the client network computing paradigm, and each edge computing node in the content delivery network computing paradigm.

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