Patent Publication Number: US-2022214929-A1

Title: Edge Time Sharing Across Clusters Via Dynamic Task Migration

Description:
BACKGROUND 
     1. Field 
     The disclosure relates generally to edge computing and more specifically to time sharing across clusters of edge devices using dynamic task migration based on task attributes. 
     2. Description of the Related Art 
     Edge computing is a distributed computing framework that brings applications closer to data sources, such as, for example, Internet of Things devices, local edge servers, and the like. This proximity to data at its source can deliver benefits, such as, for example, increased response times and increased bandwidth availability. 
     SUMMARY 
     According to one illustrative embodiment, a computer-implemented method for edge device task management is provided. It is determined whether a subtask cancel and migrate plan exists for an edge computing framework when a request to run a higher priority subtask of a second plurality of subtasks corresponding to a second task is received while a first task comprised of a first plurality of subtasks is running on a first cluster of edge devices in the edge computing framework. In response to determining that the subtask cancel and migrate plan does exist for the edge computing framework, a lower priority subtask of the first plurality of subtasks is canceled from a designated edge device of the first cluster of edge devices designated to run the higher priority subtask of the second plurality of subtasks based on the subtask cancel and migrate plan. The lower priority subtask of the first plurality of subtasks canceled from the designated edge device of the first cluster of edge devices is migrated to another edge device that is not included in the first cluster of edge devices for running based on the subtask cancel and migrate plan. The higher priority subtask of the second plurality of subtasks is sent to the designated edge device of the first cluster of edge devices for running. According to other illustrative embodiments, a computer system and computer program product for edge device task management are provided. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a pictorial representation of a network of data processing systems in which illustrative embodiments may be implemented; 
         FIG. 2  is a diagram of a data processing system in which illustrative embodiments may be implemented; 
         FIG. 3  is a diagram illustrating a cloud computing environment in which illustrative embodiments may be implemented; 
         FIG. 4  is a diagram illustrating an example of abstraction layers of a cloud computing environment in accordance with an illustrative embodiment; 
         FIG. 5  is a diagram illustrating an example of a task management system in accordance with an illustrative embodiment; 
         FIG. 6  is a diagram illustrating an example of a first task with subtasks in accordance with an illustrative embodiment; 
         FIG. 7  is a diagram illustrating an example of a selecting edge device cluster for first task process in accordance with an illustrative embodiment; 
         FIG. 8  is a diagram illustrating an example of a passing subtask results and task status process in accordance with an illustrative embodiment; 
         FIG. 9  is a diagram illustrating an example of a second task with subtasks in accordance with an illustrative embodiment; 
         FIG. 10  is a diagram illustrating an example of an edge device attributes table in accordance with an illustrative embodiment; 
         FIG. 11  is a diagram illustrating an example of a subtask assignment process in accordance with an illustrative embodiment; 
         FIG. 12  is a diagram illustrating an example of a sending subtasks to edge devices process in accordance with an illustrative embodiment; 
         FIG. 13  is a diagram illustrating an example of a pending plan process in accordance with an illustrative embodiment; 
         FIG. 14  is a diagram illustrating an example of a cancel and migrate plan process in accordance with an illustrative embodiment; and 
         FIGS. 15A-15B  are a flowchart illustrating a process for edge time sharing across clusters via dynamic task migration in accordance with an illustrative embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     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&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a 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 accomplished as one step, executed concurrently, substantially concurrently, in a partially or wholly temporally overlapping manner, 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. 
     With reference now to the figures, and in particular, with reference to  FIGS. 1-5 , diagrams of data processing environments are provided in which illustrative embodiments may be implemented. It should be appreciated that  FIGS. 1-5  are only meant as examples and are not intended to assert or imply any limitation with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environments may be made. 
       FIG. 1  depicts a pictorial representation of a network of data processing systems in which illustrative embodiments may be implemented. Network data processing system  100  is a network of computers, data processing systems, and other devices in which the illustrative embodiments may be implemented. Network data processing system  100  contains network  102 , which is the medium used to provide communications links between the computers, data processing systems, and other devices connected together within network data processing system  100 . Network  102  may include connections, such as, for example, wire communication links, wireless communication links, fiber optic cables, and the like. 
     In the depicted example, server  104  and server  106  connect to network  102 , along with storage  108  and edge devices  110 . Server  104  and server  106  are server computers with high-speed connections to network  102 . In addition, server  104  and server  106  are application programming interface servers that provide task management services for edge devices  110 . For example, server  104  and server  106  may manage performance of tasks by edge devices  110  using dynamic task migration across clusters of edge devices based on attributes of subtasks comprising the tasks. The tasks may be any type of task capable of being performed by edge devices  110 . Edge devices  110  represent a plurality of different types of edge devices in an edge computing framework and may include, for example, network computers, network devices, smart devices, and the like. Also, it should be noted that server  104  and server  106  may each represent multiple computing nodes in one or more cloud environments. 
     Client  112 , client  114 , and client  116  also connect to network  102 . Clients  112 ,  114 , and  116  are clients of server  104  and server  106 . In this example, clients  112 ,  114 , and  116  are shown as desktop or personal computers with wire communication links to network  102 . However, it should be noted that clients  112 ,  114 , and  116  are examples only and may represent other types of data processing systems, such as, for example, laptop computers, handheld computers, mobile phones, gaming devices, and the like, with wire or wireless communication links to network  102 . Users of clients  112 ,  114 , and  116  may utilize clients  112 ,  114 , and  116  to request performance of different types of tasks by server  104  and server  106 . 
     Storage  108  is a network storage device capable of storing any type of data in a structured format or an unstructured format. In addition, storage  108  may represent a plurality of network storage devices comprising a set of data stores. Further, storage  108  may store identifiers and network addresses for a plurality of servers, identifiers and network addresses for a plurality of edge devices, edge device cluster metadata, task identifiers, task attributes, and the like. Furthermore, storage  108  may store other types of data, such as authentication or credential data that may include user names, passwords, and biometric data associated with system administrators and users, for example. 
     In addition, it should be noted that network data processing system  100  may include any number of additional servers, edge devices, clients, storage devices, and other devices not shown. Program code located in network data processing system  100  may be stored on a computer readable storage medium and downloaded to a computer or other data processing device for use. For example, program code may be stored on a computer readable storage medium on server  104  and downloaded to edge devices  110  over network  102  for use on edge devices  110 . 
     In the depicted example, network data processing system  100  may be implemented as a number of different types of communication networks, such as, for example, an internet, an intranet, a wide area network, a metropolitan area network, a local area network, a telecommunications network, or any combination thereof.  FIG. 1  is intended as an example only, and not as an architectural limitation for the different illustrative embodiments. 
     As used herein, when used with reference to items, “a number of” means one or more of the items. For example, “a number of different types of communication networks” is one or more different types of communication networks. Similarly, “a set of,” when used with reference to items, means one or more of the items. 
     Further, the term “at least one of,” when used with a list of items, means different combinations of one or more of the listed items may be used, and only one of each item in the list may be needed. In other words, “at least one of” means any combination of items and number of items may be used from the list, but not all of the items in the list are required. The item may be a particular object, a thing, or a category. 
     For example, without limitation, “at least one of item A, item B, or item C” may include item A, item A and item B, or item B. This example may also include item A, item B, and item C or item B and item C. Of course, any combinations of these items may be present. In some illustrative examples, “at least one of” may be, for example, without limitation, two of item A; one of item B; and ten of item C; four of item B and seven of item C; or other suitable combinations. 
     With reference now to  FIG. 2 , a diagram of a data processing system is depicted in accordance with an illustrative embodiment. Data processing system  200  is an example of a computer, such as server  104  in  FIG. 1 , in which computer readable program code or instructions implementing task management processes of illustrative embodiments may be located. In this example, data processing system  200  includes communications fabric  202 , which provides communications between processor unit  204 , memory  206 , persistent storage  208 , communications unit  210 , input/output (I/O) unit  212 , and display  214 . 
     Processor unit  204  serves to execute instructions for software applications and programs that may be loaded into memory  206 . Processor unit  204  may be a set of one or more hardware processor devices or may be a multi-core processor, depending on the particular implementation. 
     Memory  206  and persistent storage  208  are examples of storage devices  216 . As used herein, a computer readable storage device or a computer readable storage medium is any piece of hardware that is capable of storing information, such as, for example, without limitation, data, computer readable program code in functional form, and/or other suitable information either on a transient basis or a persistent basis. Further, a computer readable storage device or a computer readable storage medium excludes a propagation medium, such as transitory signals. Memory  206 , in these examples, may be, for example, a random-access memory, or any other suitable volatile or non-volatile storage device, such as a flash memory. Persistent storage  208  may take various forms, depending on the particular implementation. For example, persistent storage  208  may contain one or more devices. For example, persistent storage  208  may be a disk drive, a solid-state drive, a rewritable optical disk, a rewritable magnetic tape, or some combination of the above. The media used by persistent storage  208  may be removable. For example, a removable hard drive may be used for persistent storage  208 . 
     Communications unit  210 , in this example, provides for communication with other computers, data processing systems, and devices via a network, such as network  102  in  FIG. 1 . Communications unit  210  may provide communications through the use of both physical and wireless communications links. The physical communications link may utilize, for example, a wire, cable, universal serial bus, or any other physical technology to establish a physical communications link for data processing system  200 . The wireless communications link may utilize, for example, shortwave, high frequency, ultrahigh 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), LTE Advanced, fifth-generation (5G), or any other wireless communication technology or standard to establish a wireless communications link for data processing system  200 . 
     Input/output unit  212  allows for the input and output of data with other devices that may be connected to data processing system  200 . For example, input/output unit  212  may provide a connection for user input through a keypad, a keyboard, a mouse, a microphone, and/or some other suitable input device. Display  214  provides a mechanism to display information to a user and may include touch screen capabilities to allow the user to make on-screen selections through user interfaces or input data, for example. 
     Instructions for the operating system, applications, and/or programs may be located in storage devices  216 , which are in communication with processor unit  204  through communications fabric  202 . In this illustrative example, the instructions are in a functional form on persistent storage  208 . These instructions may be loaded into memory  206  for running by processor unit  204 . The processes of the different embodiments may be performed by processor unit  204  using computer-implemented instructions, which may be located in a memory, such as memory  206 . These program instructions are referred to as program code, computer usable program code, or computer readable program code that may be read and run by a processor in processor unit  204 . The program instructions, in the different embodiments, may be embodied on different physical computer readable storage devices, such as memory  206  or persistent storage  208 . 
     Program code  218  is located in a functional form on computer readable media  220  that is selectively removable and may be loaded onto or transferred to data processing system  200  for running by processor unit  204 . Program code  218  and computer readable media  220  form computer program product  222 . In one example, computer readable media  220  may be computer readable storage media  224  or computer readable signal media  226 . 
     In these illustrative examples, computer readable storage media  224  is a physical or tangible storage device used to store program code  218  rather than a medium that propagates or transmits program code  218 . Computer readable storage media  224  may include, for example, an optical or magnetic disc that is inserted or placed into a drive or other device that is part of persistent storage  208  for transfer onto a storage device, such as a hard drive, that is part of persistent storage  208 . Computer readable storage media  224  also may take the form of a persistent storage, such as a hard drive, a thumb drive, or a flash memory that is connected to data processing system  200 . 
     Alternatively, program code  218  may be transferred to data processing system  200  using computer readable signal media  226 . Computer readable signal media  226  may be, for example, a propagated data signal containing program code  218 . For example, computer readable signal media  226  may be an electromagnetic signal, an optical signal, or any other suitable type of signal. These signals may be transmitted over communication links, such as wireless communication links, an optical fiber cable, a coaxial cable, a wire, or any other suitable type of communications link. 
     Further, as used herein, “computer readable media  220 ” can be singular or plural. For example, program code  218  can be located in computer readable media  220  in the form of a single storage device or system. In another example, program code  218  can be located in computer readable media  220  that is distributed in multiple data processing systems. In other words, some instructions in program code  218  can be located in one data processing system while other instructions in program code  218  can be located in one or more other data processing systems. For example, a portion of program code  218  can be located in computer readable media  220  in a server computer while another portion of program code  218  can be located in computer readable media  220  located in a set of client computers. 
     The different components illustrated for data processing system  200  are not meant to provide architectural limitations to the manner in which different embodiments can be implemented. In some illustrative examples, one or more of the components may be incorporated in or otherwise form a portion of, another component. For example, memory  206 , or portions thereof, may be incorporated in processor unit  204  in some illustrative examples. The different illustrative embodiments can be implemented in a data processing system including components in addition to or in place of those illustrated for data processing system  200 . Other components shown in  FIG. 2  can be varied from the illustrative examples shown. The different embodiments can be implemented using any hardware device or system capable of running program code  218 . 
     In another example, a bus system may be used to implement communications fabric  202  and may be comprised of one or more buses, such as a system bus or an input/output bus. Of course, the bus system may be implemented using any suitable type of architecture that provides for a transfer of data between different components or devices attached to the bus system. 
     It is 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, illustrative embodiments 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, such as, for example, 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 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. 
     The characteristics may include, for example, on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. On-demand self-service allows a cloud consumer to unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. Broad network access provides for capabilities that are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms, such as, for example, mobile phones, laptops, and personal digital assistants. Resource pooling allows the provider&#39;s computing resources to be 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, such as, for example, country, state, or data center. Rapid elasticity provides for capabilities that 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 allows cloud systems to automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service, such as, for example, storage, processing, bandwidth, and active user accounts. Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service. 
     Service models may include, for example, Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Software as a Service is the capability provided to the consumer to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface, such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. Platform as a Service is the capability provided to the consumer to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. Infrastructure as a Service is the capability provided to the consumer 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, such as, for example, host firewalls. 
     Deployment models may include, for example, a private cloud, community cloud, public cloud, and hybrid cloud. A private cloud is a cloud infrastructure operated solely for an organization. The private cloud may be managed by the organization or a third party and may exist on-premises or off-premises. A community cloud is a cloud infrastructure shared by several organizations and supports a specific community that has shared concerns, such as, for example, mission, security requirements, policy, and compliance considerations. The community cloud may be managed by the organizations or a third party and may exist on-premises or off-premises. A public cloud is a cloud infrastructure made available to the general public or a large industry group and is owned by an organization selling cloud services. A hybrid cloud is a cloud infrastructure composed of two or more clouds, such as, for example, private, community, and public clouds, which remain as unique entities, but are bound together by standardized or proprietary technology that enables data and application portability, such as, for example, 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 comprising a network of interconnected nodes. 
     With reference now to  FIG. 3 , a diagram illustrating a cloud computing environment is depicted in which illustrative embodiments may be implemented. In this illustrative example, cloud computing environment  300  includes a set of one or more cloud computing nodes  310  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant or smart phone  320 A, desktop computer  320 B, laptop computer  320 C, and/or automobile computer system  320 N, may communicate. Cloud computing nodes  310  may be, for example, server  104  and server  106  in  FIG. 1 . Local computing devices  320 A- 320 N may include, for example, edge devices  110  and clients  112 - 116  in  FIG. 1 . 
     Cloud computing nodes  310  may communicate with one another and may be grouped physically or virtually into one or more networks, such as private, community, public, or hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment  300  to offer infrastructure, platforms, and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device, such as local computing devices  320 A- 320 N. It is understood that the types of local computing devices  320 A- 320 N are intended to be illustrative only and that cloud computing nodes  310  and cloud computing environment  300  can communicate with any type of computerized device over any type of network and/or network addressable connection using a web browser, for example. 
     With reference now to  FIG. 4 , a diagram illustrating abstraction model layers is depicted in accordance with an illustrative embodiment. The set of functional abstraction layers shown in this illustrative example may be provided by a cloud computing environment, such as cloud computing environment  300  in  FIG. 3 . It should be understood in advance that the components, layers, and functions shown in  FIG. 4  are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided. 
     Abstraction layers of a cloud computing environment  400  include hardware and software layer  402 , virtualization layer  404 , management layer  406 , and workloads layer  408 . Hardware and software layer  402  includes the hardware and software components of the cloud computing environment. The hardware components may include, for example, mainframes  410 , RISC (Reduced Instruction Set Computer) architecture-based servers  412 , servers  414 , blade servers  416 , storage devices  418 , and networks and networking components  420 . In some illustrative embodiments, software components may include, for example, network application server software  422  and database software  424 . 
     Virtualization layer  404  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  426 ; virtual storage  428 ; virtual networks  430 , including virtual private networks; virtual applications and operating systems  432 ; and virtual clients  434 . 
     In one example, management layer  406  may provide the functions described below. Resource provisioning  436  provides dynamic procurement of computing resources and other resources, which are utilized to perform tasks within the cloud computing environment. Metering and pricing  438  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  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 required service levels are met. Service level agreement (SLA) planning and fulfillment  444  provides pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  408  provides examples of functionality for which the cloud computing environment may be utilized. Example workloads and functions, which may be provided by workload layer  408 , may include mapping and navigation  446 , software development and lifecycle management  448 , virtual classroom education delivery  450 , data analytics processing  452 , transaction processing  454 , and edge device task management  456 . 
     The increase in the number of Internet of Things devices at the edge of a network is producing a massive amount of data to be computed at data centers, pushing network bandwidth requirements to their limits. Despite improvements in network technology, data centers cannot guarantee acceptable transfer rates and response times, which may be a critical requirement for many applications. Furthermore, Internet of Things devices at the edge constantly consume data coming from the cloud, forcing entities, such as, for example, enterprises, companies, organizations, institutions, agencies, and the like, to build content delivery networks to decentralize data and service provisioning, leveraging physical proximity to the end user. 
     Edge computing moves the computation away from data centers towards the edge of the network, exploiting Internet of Things devices, such as, for example, smart devices (e.g., smart phones, smart televisions, smart watches, smart glasses, smart vehicles, smart appliances, smart sensors, and the like), mobile phones, network gateways and devices, and the like, to perform tasks and provide services on behalf of the cloud. By moving tasks and services to the edge, it is possible to provide better response times and bandwidth availability. 
     With the development and spread of cloud computing, Internet of Things, and business models, edge computing becomes the next emerging technology to provide greater computational capabilities. Assisted by artificial intelligence edge controllers, edge computing has now been widely adopted in many industries involving dedicated programming and controlling of edge devices. An increasing number of different types of tasks or workloads will run on these edge devices. Considering cost factors, some edge devices are expensive and have limited functions, and may be shared among different tasks. Time sharing of edge devices improves resource utilization and decreases cost. Time sharing is the allocation of a computing resource among multiple tasks. 
     A variety of different tasks may run concurrently (i.e., at the same time) on a single edge computing framework comprised of a plurality of edge devices. In addition, collaboration, interaction, or conflict (e.g., task preemption on the same edge device) may exist among these different types of tasks. Further, priority and resource consumption of each task may be different. The edge computing platform needs to ensure that important tasks are properly prioritized and run smoothly, while scheduling resources reasonably and improving utilization of the edge computing platform. While giving higher priority to completing important tasks, under limited resource conditions, illustrative embodiments enable reasonable scheduling and use of edge devices to ensure that lower priority tasks continue to run, minimizing possible impact on task performance. In other words, illustrative embodiments enable edge device time sharing across clusters of edge devices via dynamic task migration. 
     Thus, illustrative embodiments increase edge layer capabilities by providing dynamic task migration based on attributes, such as priority and tags, corresponding to respective tasks. Furthermore, illustrative embodiments enhance the efficiency of the entire edge layer via a novel dynamic task migration process. Moreover, illustrative embodiments enable faster response times for edge device task performance, improving user experience. 
     Therefore, illustrative embodiments provide one or more technical solutions that overcome a technical problem with migrating tasks across clusters of edge devices. As a result, these one or more technical solutions provide a technical effect and practical application in the field of edge computing by increasing task performance and decreasing response time using dynamic task migration based on task attributes. 
     With reference now to  FIG. 5 , a diagram illustrating an example of a task management system is depicted in accordance with an illustrative embodiment. Task management system  500  may be implemented in a network of data processing systems, such as network data processing system  100  in  FIG. 1 , or a cloud computing environment, such as cloud computing environment  300  in  FIG. 3 . Task management system  500  is a system of hardware and software components for time sharing across clusters of edge devices using dynamic task migration based on task attributes. 
     In this example, task management system  500  includes cloud layer  502 , edge layer  504 , and edge devices  506 . Cloud layer  502  may be, for example, cloud computing environment  300  in  FIG. 3 . Cloud layer  502  includes data store  508 , application programming interface server  510 , and edge controller  512 . Data store  508  may be, for example, storage  108  in  FIG. 1 , and includes, for example, edge device task management data. Application programming interface server  510  may be, for example, server  104  in  FIG. 1 , data processing system  200  in  FIG. 2 , or a cloud computing node in cloud computing nodes  310  in  FIG. 3 . Application programming interface server  510  receives user requests from client devices via a network to perform tasks. Application programming interface server  510  interacts with and manages edge devices  506  to perform the requested tasks. Edge controller  512  is responsible for connecting to all network gateways and edge devices  506  in the edge computing framework. In addition, edge controller  512  collects and collates data from edge devices  506 , transmits data to and accepts instructions from application programming interface server  510  to execute across all or clusters of edge devices  506 . 
     Edge layer  504  is responsible for connecting devices locally. In addition, edge layer  504  manages data collection and connection to application programming interface server  510 . Edge layer  504  is also responsible for handling outages and storing and forwarding data. Edge layer  504  includes synchronization service  514 , metadata store  516 , and edge agent  518 . Synchronization service  514  is responsible for synchronizing data to application programming interface server  510 . Metadata store  516  contains metadata defining different clusters of edge devices included in edge devices  506 . Metadata store  516  may retrieve the edge device cluster metadata from data store  508 . Edge agent  518  communicates with edge devices  506  using cluster definer  520 , task sender  522 , and task result receiver  524 . Cluster definer  520  defines the different clusters of edge devices using information in metadata store  516 . Cluster definer  520  also refines clusters of edge devices in response to migration of tasks between edge devices. Task sender  522  assigns and sends tasks to respective edge devices based on corresponding task attributes, such as priority and tags. Further, task sender  522  cancels and migrates tasks on edge devices based on a task cancel and migration plan or reinvokes tasks on edge devices based on a task pending plan. Task result receiver  524  receives task status and results of running tasks on edge devices  506 . 
     Edge devices  506  may be, for example, edge devices  110  in  FIG. 1 , and may include any type and combination of edge devices. In this example, edge devices  506  include edge device “A”  526 , edge device “B”  528 , edge device “C”  530 , edge device “D”  532 , and edge device “E”  534 . However, it should be noted that edge devices  506  may include any number of edge devices. Also, edge device A  526 , edge device B  528 , edge device C  530 , edge device D  532 , and edge device E  534  include proxy  536 , proxy  538 , proxy  540 , proxy  542 , and proxy  544 , respectively. Proxies  536 - 544  provide communication between edge devices  506 . In addition, edge device A  526 , edge device B  528 , edge device C  530 , edge device D  532 , and edge device E  534  include container  546 , container  548 , container  550 , container  552 , and container  554 , respectively. Containers  546 - 554  run the tasks sent to corresponding edge devices  506 . 
     With reference now to  FIG. 6 , a diagram illustrating an example of a first task with subtasks is depicted in accordance with an illustrative embodiment. First task with subtasks  600  may be implemented in a task management system, such as, for example, task management system  500  in  FIG. 5 . 
     In this example, first task with subtasks  600  is task_ 1   602  comprised of subtask_ 1 _ 1   604 , subtask_ 1 _ 2   606 , and subtask_ 1 _ 3   608 . However, it should be noted that task_ 1   602  is meant as an example only and not as a limitation on illustrative embodiments. In other words, task_ 1   602  may include any number of subtasks. 
     Task definition  610  of task_ 1   602  shows subtask identifiers with corresponding next subtask identifiers. In this example, subtask_ 1 _ 1  has a corresponding next subtask of subtask_ 1 _ 2  and subtask_ 1 _ 2  has a corresponding next subtask of subtask_ 1 _ 3 . Subtask attributes  612  of task_ 1   602  shows subtask identifiers with corresponding tags and priorities. In this example, subtask_ 1 _ 1  has corresponding tag_ 1  and priority_ 3 , subtask_ 1 _ 2  has corresponding tag_ 2 , tag_ 4 , and priority_ 2 , and subtask_ 1 _ 3  has corresponding tag_ 3  and priority_ 3 . A tag indicates which particular edge device the corresponding subtask is to be run on. For example, tag_ 1  may indicate that the corresponding subtask is to run on edge device A, tag_ 2  may indicate that the corresponding subtask is to run on edge device B, tag_ 3  may indicate that the corresponding subtask is to run on edge device C, tag_ 4  may indicate that the corresponding subtask is to run on edge device D, and tag_ 5  may indicate that the corresponding subtask is to run on edge device E. The priority indicates the relative importance of running a particular subtask. For example, a higher priority subtask takes priority to run over a lower priority subtask. 
     With reference now to  FIG. 7 , a diagram illustrating an example of a selecting edge device cluster for first task process is depicted in accordance with an illustrative embodiment. Selecting edge device cluster for first task process  700  may be implemented in a task management system, such as, for example, task management system  500  in  FIG. 5 . 
     In this example, edge agent  702 , such as, edge agent  518  in  FIG. 5 , utilizes a cluster definer, such as cluster definer  520  in  FIG. 5 , to define cluster_ 1  comprised of edge devices  704  for task_ 1   706 . Edge devices  704  include edge device A, edge device B, and edge device C, such as, for example, edge device A  526 , edge device B  528 , and edge device C  530  in  FIG. 5 . Task_ 1   706  includes subtask_ 1 _ 1 , subtask_ 1 _ 2 , and subtask_ 1 _ 3 , such as, for example, task_ 1   602  including subtask_ 1 _ 1   604 , subtask_ 1 _ 2   606 , and subtask_ 1 _ 3   608  in  FIG. 6 . Edge agent  702  utilizes a task sender, such as, for example, task sender  522  in  FIG. 5 , to send subtask_ 1 _ 1 , subtask_ 1 _ 2 , and subtask_ 1 _ 3  to edge device A, edge device B, and edge device C, respectively, based on corresponding tags included in subtask attributes, such as subtask attributes  612  in  FIG. 6 , and cluster metadata from the cluster definer. 
     Edge device attributes table  708  includes edge device identifier  710 , tag  712 , CPU usage  714 , current subtask  716 , current subtask status  718 , and current task  720 . Edge device attributes table  708  shows attributes of edge devices  704  prior to task_ 1   706  being sent to edge devices  704 . Edge device cluster table  722  includes task identifier  724 , cluster identifier  726 , subtask identifier  728 , edge device identifier  730 , and coordinator proxy  732 . Edge device cluster table  722  shows selection of edge devices  704  (i.e., edge devices A, B, and C) as cluster_ 1  for task_ 1   706  comprised of subtask_ 1 _ 1 , subtask_ 1 _ 2 , and subtask_ 1 _ 3 . Edge device cluster table  722  also shows that the proxy on edge device A, such as, for example, proxy  536  on edge device A  526 , is designated as a coordinator proxy for cluster_ 1 . The coordinator proxy controls the network traffic (e.g., task results and task status) between edge devices  704  of cluster_ 1  based on cluster metadata. 
     With reference now to  FIG. 8 , a diagram illustrating an example of a passing subtask results and task status process is depicted in accordance with an illustrative embodiment. Passing subtask results and task status process  800  may be implemented in a task management system, such as, for example, task management system  500  in  FIG. 5 . 
     In this example, passing subtask results and task status process  800  utilizes subtask results passing table  802  and task status table  804 . Subtask results passing table  802  includes edge device identifier  806 , subtask identifier  808 , task identifier  810 , cluster identifier  812 , next subtask identifier  814 , next edge device identifier  816 , and is coordinator  818 . Subtask results passing table  802  indicates that the coordinator proxy on edge device A receives results of each subtask and that the result of a respective subtask is sent to the next subtask (i.e., the next proxy on the next edge device) based on cluster metadata. Task status table  804  includes edge device identifier  820 , subtask identifier  822 , subtask status  824 , cluster identifier  826 , task identifier  828 , and task status  830 . Task status table  804  indicates the current status of task_ 1  is running on cluster_ 1  comprised of edge devices A, B, and C. In addition, task status table  804  indicates the current status of each subtask of task_ 1  on a corresponding edge device. For example, subtask_ 1 _ 1  is finished running on edge device A, subtask_ 1 _ 2  is running on edge device B, and subtask_ 1 _ 3  is pending on edge device C. 
     With reference now to  FIG. 9 , a diagram illustrating an example of a second task with subtasks is depicted in accordance with an illustrative embodiment. Second task with subtasks  900  may be implemented in a task management system, such as, for example, task management system  500  in  FIG. 5 . 
     In this example, second task with subtasks  900  is task_ 2   902  comprised of subtask_ 2 _ 1   904  and subtask_ 2 _ 2   906 . However, it should be noted that task_ 2   902  is meant as an example only and not as a limitation on illustrative embodiments. In other words, task_ 2   902  may include any number of subtasks. 
     Task definition  908  of task_ 2   902  shows a subtask identifier with corresponding next subtask identifier. In this example, subtask_ 2 _ 1  has a corresponding next subtask of subtask_ 2 _ 2 . Subtask attributes  910  of task_ 2   902  shows subtask identifiers with corresponding tags and priorities. In this example, subtask_ 2 _ 1  has a corresponding tag_ 2  and priority_ 1  and subtask_ 2 _ 2  has a corresponding tag_ 3  and priority_ 3 . The tag indicates which particular edge device the corresponding subtask is to be run on. For example, tag_ 2  indicates that subtask_ 2 _ 1  is to run on edge device B and tag_ 5  indicates that subtask_ 2 _ 2  is to run on edge device E. The priority indicates the relative importance of a particular subtask to run. For example, priority_ 1  indicates that subtask_ 2 _ 1  is a high priority subtask that takes priority to run over a lower priority subtask. 
     With reference now to  FIG. 10 , a diagram illustrating an example of an edge device attributes table is depicted in accordance with an illustrative embodiment. Edge device attributes table  1000  may be implemented in a task management system, such as, for example, task management system  500  in  FIG. 5 . In this example, edge device attributes table  1000  includes edge device identifier  1002 , tag  1004 , CPU usage  1006 , current subtask  1008 , current subtask status  1010 , and current task  1012 . Edge device attributes table  1000  is similar to edge device attributes table  708  in  FIG. 7 . However, edge device attributes table  1000  shows attributes of edge devices A-E after subtask  1 _ 1 , subtask  1 _ 2 , and subtask  1 _ 3  of task_ 1  were sent to edge devices A, B, and C, respectively, and prior to subtasks  2 _ 1  and  2 _ 2  being sent to selected edge devices. 
     With reference now to  FIG. 11 , a diagram illustrating an example of a subtask assignment process is depicted in accordance with an illustrative embodiment. Subtask assignment process  1100  may be implemented in a task management system, such as, for example, task management system  500  in  FIG. 5 . 
     In this example, edge agent  1102  utilizes a cluster definer, such as cluster definer  520  in  FIG. 5 , to define cluster_ 1  comprised of edge devices A, D, and C of edge devices  1104  for task_ 1   1106  and cluster_ 2  comprised of edge devices B and E of edge devices  1104  for task_ 2   1108 . In addition, it should be noted that edge device A is running subtask_ 1 _ 1  of task_ 1   1106 , subtask_ 1 _ 2  is pending on edge devices B, and subtask_ 1 _ 3  is pending on edge devices C, when a user request is received to run task_ 2   1108 . Because subtask_ 2 _ 1  of task_ 2   1108  needs to run on edge device B of edge devices  1104  based on subtask attributes of tag and priority, such as, for example, tag_ 2  and priority_ 1  of subtask attributes  910  in  FIG. 9 , corresponding to subtask_ 2 _ 1 , a task sender, such as, for example, task sender  522  in  FIG. 5 , of edge agent  1102  assigns subtask_ 1 _ 2  of task_ 1   1106  to be run on edge device D of edge devices  1104  based on subtask attributes of tags and priority, such as, for example, tag_ 2 , tag_ 4 , and priority_ 2  of subtask attributes  612  in  FIG. 6 , corresponding to subtask_ 1 _ 2 . Further, the task sender assigns subtask_ 2 _ 2  to be run on edge device E of edge devices  1104  based on subtask attributes of tag and priority, such as, for example, tag_ 5  and priority_ 3  of subtask attributes  910  in  FIG. 9 , corresponding to subtask_ 2 _ 2 . 
     Edge device cluster table  1110  includes task identifier  1112 , cluster identifier  1114 , subtask identifier  1116 , edge device identifier  1118 , and coordinator proxy  1120 . Edge device cluster table  1110  is similar to edge device cluster table  722  in  FIG. 7  except edge device cluster table  1110  now includes information regarding cluster_ 2  corresponding to task_ 2   1108 . In addition, edge device cluster table  1110  indicates that edge device D is now included in cluster_ 1  instead of edge device B. Edge device cluster table  1110  also shows that the proxy on edge device E, such as, for example, proxy  544  on edge device E  534 , is designated as a coordinator proxy for cluster_ 2 . 
     With reference now to  FIG. 12 , a diagram illustrating an example of a sending subtasks to edge devices process is depicted in accordance with an illustrative embodiment. Sending subtasks to edge devices process  1200  may be implemented in a task management system, such as, for example, task management system  500  in  FIG. 5 . 
     In this example, edge agent  1202  utilizes a task sender, such as, for example, task sender  522  in  FIG. 5 , to send tasks to edge devices  1204 . The tasks in this example are task_ 1   1206  comprised of subtask_ 1 _ 1 , subtask_ 1 _ 2 , and subtask_ 1 _ 3  and task_ 2   1208  comprised of subtask_ 2 _ 1  and subtask_ 2 _ 2 . The task sender utilizes the information in subtask sending table  1210  to send respective subtasks to appropriate edge devices of edge devices  1204 . 
     In this example, subtask sending table  1210  includes edge device identifier  1212 , subtask identifier  1214 , task identifier  1216 , cluster identifier  1218 , next subtask identifier  1220 , next edge device identifier  1222 , and is coordinator  1224 . Because edge devices  1204  include edge device D, which is capable of running subtask_ 1 _ 2 , the task sender will send subtask_ 1 _ 2  to be run on edge device D instead of edge device B. As a result, subtask_ 1 _ 1  will run on edge device A, subtask_ 2 _ 1  will run on edge device B, subtask_ 1 _ 3  will run on edge device C, subtask_ 1 _ 2  will run on edge device D, and subtask_ 2 _ 2  will run on edge device E. 
     With reference now to  FIG. 13 , a diagram illustrating an example of a pending plan process is depicted in accordance with an illustrative embodiment. Pending plan process  1300  may be implemented in a task management system, such as, for example, task management system  500  in  FIG. 5 . 
     In this example, edge agent  1302  utilizes a cluster definer, such as cluster definer  520  in  FIG. 5 , to define cluster_ 1  comprised of edge devices A, B, and C of edge devices  1304  for task_ 1   1306  and cluster_ 2  comprised of edge devices B and E of edge devices  1304  for task_ 2   1308 . In addition, it should be noted that edge device A is running subtask_ 1 _ 1  of task_ 1   1306  when a user request is received to run task_ 2   1308 . Because subtask_ 2 _ 1  of task_ 2   1308  needs to run on edge device B of edge devices  1304  based on its tag_ 2  and priority_ 1  attributes and edge devices  1304  do not include an edge device that is capable of running subtask  1 _ 2  in this example, a task sender, such as, for example, task sender  522  in  FIG. 5 , of edge agent  1302  creates a pending plan (i.e., pending plan table  1322 ) for subtask_ 1 _ 2  to run on edge device B when subtask_ 2 _ 1  finishes running based on subtask_ 1 _ 2  having a lower priority attribute (i.e., priority_ 2 ). 
     Edge device cluster table  1310  includes task identifier  1312 , cluster identifier  1314 , subtask identifier  1316 , edge device identifier  1318 , and coordinator proxy  1320 . Edge device cluster table  1310  is similar to edge device cluster table  1110  in  FIG. 11  except edge device cluster table  1310  indicates that subtask_ 1 _ 2  is pending on edge device B and edge device B is included in both cluster_ 1  and cluster_ 2 . Pending plan table  1322  includes task identifier  1324 , cluster identifier  1326 , subtask identifier  1328 , edge device identifier  1330 , dependent subtask identifier  1332 , and dependent task identifier  1334 . 
     With reference now to  FIG. 14 , a diagram illustrating an example of a cancel and migrate plan process is depicted in accordance with an illustrative embodiment. Cancel and migrate plan process  1400  may be implemented in a task management system, such as, for example, task management system  500  in  FIG. 5 . 
     In this example, edge agent  1402  utilizes a cluster definer, such as cluster definer  520  in  FIG. 5 , to define cluster_ 1  comprised of edge devices A, D, and C of edge devices  1404  for task_ 1   1406  and cluster_ 2  comprised of edge devices B and E of edge devices  1404  for task_ 2   1408 . In addition, it should be noted that edge device A is running subtask_ 1 _ 1  of task_ 1   1406  when a user request is received to run task_ 2   1408 . Because subtask_ 2 _ 1  of task_ 2   1408  needs to run on edge device B of edge devices  1404  based on its tag_ 2  and priority_ 1  attributes and edge devices  1304  include edge device D, which is capable of running subtask_ 1 _ 2  in this example, a task sender, such as, for example, task sender  522  in  FIG. 5 , of edge agent  1302  creates a cancel and migration plan (i.e., cancel and migration plan table  1422 ) for subtask_ 1 _ 2  to cancel subtask_ 1 _ 2  on edge device B and migrate subtask_ 1 _ 2  to edge device D based on subtask_ 1 _ 2  having a lower priority_ 2  attribute. 
     Edge device cluster table  1410  includes task identifier  1412 , cluster identifier  1414 , subtask identifier  1416 , edge device identifier  1418 , and coordinator proxy  1420 . Edge device cluster table  1410  is similar to edge device cluster table  1110  in  FIG. 11 . Cancel and migration plan table  1422  includes task identifier  1424 , cluster identifier  1426 , subtask identifier  1428 , cancel edge device identifier  1430 , and migrate edge device identifier  1432 . 
     With reference now to  FIGS. 15A-15B , a flowchart illustrating a process for edge time sharing across clusters via dynamic task migration is shown in accordance with an illustrative embodiment. The process shown in  FIGS. 15A-15B  may be implemented in a computer system, such as, for example, network data processing system  100  in  FIG. 1 , cloud computing environment  300  in  FIG. 3 , or task management system  500  in  FIG. 5 . 
     The process begins when the computer system receives a first task comprised of a first plurality of subtasks to run (step  1502 ). In response to receiving the first task, the computer system selects a first cluster of edge devices from a plurality of edge devices included in an edge computing framework to run respective subtasks of the first plurality of subtasks corresponding to the first task based on attributes of each respective subtask (step  1504 ). Then, the computer system sends each respective subtask of the first plurality of subtasks to a corresponding edge device in the first cluster of edge devices for running (step  1506 ). 
     Afterward, the computer system, using a proxy component included in each of the first cluster of edge devices, sends a result of running a subtask on its corresponding edge device to other edge devices included in the first cluster of edge devices based on cluster metadata corresponding to the first cluster of edge devices (step  1508 ). In addition, the computer system sends a status of the first task to a designated coordinator proxy of the first cluster of edge devices (step  1510 ). Further, the computer system synchronizes the status of the first task with a task result receiver using the designated coordinator proxy of the first cluster of edge devices (step  1512 ). 
     Subsequently, the computer system receives a second task comprised of a second plurality of subtasks to run while the first task is still running on the first cluster of edge devices (step  1514 ). In response to receiving the second task, the computer system selects a second cluster of edge devices from the plurality of edge devices to run respective subtasks of the second plurality of subtasks corresponding to the second task that includes a designated edge device of the first cluster of edge devices to run a higher priority subtask of the second plurality of subtasks based on attributes of the higher priority subtask (step  1516 ). 
     The computer system makes a determination as to whether a subtask cancel and migrate plan exists (step  1518 ). If the computer system determines that a subtask cancel and migrate plan does exist, yes output of step  1518 , then the computer system cancels a lower priority subtask of the first plurality of subtasks from the designated edge device of the first cluster of edge devices designated to run the higher priority subtask of the second plurality of subtasks based on the subtask cancel and migrate plan (step  1520 ). Afterwards, the computer system migrates the lower priority subtask of the first plurality of subtasks canceled from the designated edge device of the first cluster of edge devices to another edge device in the plurality of edge devices that is not included in either of the first cluster of edge devices or the second cluster of edge devices for running based on the subtask cancel and migrate plan (step  1522 ). In addition, the computer system sends the higher priority subtask of the second plurality of subtasks to the designated edge device of the first cluster of edge devices for running (step  1524 ). Thereafter, the process terminates. 
     Returning again to step  1518 , if the computer system determines that a subtask cancel and migrate plan does not exist, no output of step  1518 , then the computer system suspends the lower priority subtask of the first plurality of subtasks on the designated edge device of the first cluster of edge devices designated to run the higher priority subtask of the second plurality of subtasks based on a subtask pending plan (step  1526 ). Further, the compute system sends the higher priority subtask of the second plurality of subtasks to the designated edge device of the first cluster of edge devices for running (step  1528 ). Subsequently, the computer system invokes the lower priority subtask of the first plurality of subtasks suspended on the designated edge device of the first cluster of edge devices to run when the higher priority subtask of the second plurality of subtasks finishes running on the designated edge device (step  1530 ). Thereafter, the process terminates. 
     Thus, illustrative embodiments of the present invention provide a computer-implemented method, computer system, and computer program product for time sharing across clusters of edge devices using dynamic task migration based on attributes of subtasks. 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 disclosed herein.