Computing task allocation method based on end-cloud fusion, computer equipment and medium

Abstract

The invention provides a computing task allocation method based on end-cloud fusion, computer equipment and a medium. The method comprises the following steps: acquiring a target resource pool of end cloud fusion; in a target resource pool, target cloud resources, target local resources and target associated resources matched with the target computing task are determined, the target cloud resources are from a cloud server, the target local resources are from a first user terminal triggering the target computing task, and the target associated resources are from a second user terminal associated with the first user terminal; dividing the target calculation task into a plurality of sub-calculation tasks; and distributing the plurality of sub-computing tasks to a cloud server, a first user terminal and a second user terminal so as to perform data processing by using the target cloud resource, the target local resource and the target associated resource respectively. According to the method, the cloud resources required by the target computing task can be reduced, so that the cost required by the computing task is reduced.

Description

Technical Field

[0001] The present application relates to the field of end-cloud fusion processing technology, and specifically to a computing task allocation method, computer equipment, and medium based on end-cloud fusion. Background Art

[0002] In related technologies, complex computing tasks can be handled through end-to-end data processing, leveraging cloud server resources to quickly complete the computation. However, this end-to-end approach often requires excessive cloud resources, significantly increasing the cost of the computation. Summary of the Invention

[0003] The embodiments of the present application provide a computing task allocation method, computer equipment, and medium based on end-cloud integration, which aim to reduce the cloud resources required for the target computing task, thereby reducing the cost required for the computing task.

[0004] In a first aspect, an embodiment of the present application provides a method for allocating computing tasks based on end-cloud integration, the method comprising: Obtain the target resource pool for end-cloud integration; In the target resource pool, target cloud resources, target local resources, and target associated resources that match the target computing task are determined, wherein the target cloud resources are from a cloud server, the target local resources are from a first user terminal that triggers the target computing task, and the target associated resources are from a second user terminal associated with the first user terminal; Splitting the target computing task into a plurality of sub-computing tasks; Distribute the plurality of sub-computing tasks to the cloud server, the first user terminal, and the second user terminal, so as to respectively utilize the target cloud resources, the target local resources, and the target associated resources for data processing.

[0005] In some embodiments, after distributing the plurality of computing subtasks to the cloud server, the first user terminal, and the second user terminal, the method further includes: Receiving sub-computation results fed back by the cloud server, the first user terminal, and the second user terminal respectively; Based on the multiple sub-computation results, a calculation result of the target computing task is determined.

[0006] In some embodiments, obtaining a target resource pool for end-cloud integration includes: Obtain a user terminal set associated with the first user terminal that triggers the target computing task; Determining, from the user terminal set, a plurality of second user terminals in an idle state; Computing resources are aggregated and processed on the cloud server, the first user terminal, and multiple second user terminals to obtain the target resource pool.

[0007] In some embodiments, determining, in the set of user terminals, a plurality of second user terminals in an idle state includes: determining a load rate of each user terminal in the user terminal set; In the user terminal set, a user terminal whose load rate is less than a load rate threshold and has no computing tasks is used as the second user terminal.

[0008] In some embodiments, the computing task allocation method based on end-cloud integration further includes: Obtaining a current load rate of the first user terminal; The load rate threshold is determined based on a current load rate of the first user terminal.

[0009] In some embodiments, obtaining a set of user terminals associated with the first user terminal that triggers the target computing task includes: Determine the intranet environment where the first user terminal that triggers the target computing task is located; The user terminals other than the first user terminal in the intranet environment are aggregated to obtain the user terminal set.

[0010] In some embodiments, distributing the plurality of sub-computing tasks to the cloud server, the first user terminal, and the second user terminal includes: Determining a first difference between a current load rate of the first user terminal and a target load rate; determining a second difference between a current load rate of each second user terminal and the target load rate; determining, based on a ratio between the first gap value and each of the second gap values, a task allocation ratio between the first user terminal and the corresponding second user terminal; According to the task allocation ratio, the plurality of sub-computing tasks are distributed to the cloud server, the first user terminal, and the second user terminal.

[0011] In some embodiments, determining the task allocation ratio between the first user terminal and the corresponding second user terminal based on the ratio between the first gap value and each second gap value includes: Obtaining a delay difference between a current communication delay of the first user terminal and a current communication delay of each of the second user terminals; Based on each of the delay differences, a ratio between the first difference value and the corresponding second difference value is corrected to obtain a task allocation ratio between the first user terminal and the corresponding second user terminal.

[0012] In a second aspect, an embodiment of the present application provides a computing task allocation device based on end-cloud fusion, the computing task allocation device based on end-cloud fusion comprising: The acquisition module is used to obtain the target resource pool for end-cloud integration; a determination module, configured to determine, in the target resource pool, target cloud resources, target local resources, and target associated resources that match the target computing task, wherein the target cloud resources are from a cloud server, the target local resources are from a first user terminal that triggers the target computing task, and the target associated resources are from a second user terminal associated with the first user terminal; A splitting module, configured to split the target computing task into a plurality of sub-computing tasks; A distribution module is used to distribute the multiple sub-computing tasks to the cloud server, the first user terminal and the second user terminal, so as to respectively utilize the target cloud resources, the target local resources and the target associated resources for data processing.

[0013] In a third aspect, an embodiment of the present application provides a computer device comprising a processor and a memory, wherein a computer program is stored in the memory, and the computer program is configured to be executed by the processor to implement the computing task allocation method based on end-cloud integration as described in any one of the above items.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and the computer program is configured to be executed by a processor to implement the computing task allocation method based on end-cloud integration as described in any one of the above items.

[0015] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program or instructions, which are executed by a processor to implement the computing task allocation method based on end-cloud integration as described in any of the above items.

[0016] Beneficial effects of the embodiments of the present application: In an embodiment of the present application, the target computing task is divided into multiple sub-computing tasks, which are then distributed to the cloud server, the first user terminal, and the second user terminal, so as to comprehensively utilize the target cloud resources, the target local resources, and the target associated resources for data processing. Due to the addition of target associated resources, the cloud resources required for the target computing task can be reduced, thereby reducing the cost required for the computing task. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 This is a flow chart of an embodiment of a method for allocating computing tasks based on end-cloud integration provided by an embodiment of the present application; Figure 2 This is a flow chart of another embodiment of a method for allocating computing tasks based on end-cloud integration provided by an embodiment of the present application; Figure 3 This is a flow chart of another embodiment of a method for allocating computing tasks based on end-cloud integration provided in an embodiment of the present application; Figure 4 This is a schematic structural diagram of an embodiment of a computing task allocation device based on end-cloud integration provided by an embodiment of the present application; Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0020] In the description of this application, "plurality" means two or more, unless otherwise specifically defined. Furthermore, in the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features being described. Thus, features defined as "first" or "second" may explicitly or implicitly include one or more of the described features.

[0021] In the first aspect, the embodiment of the present application provides a method for allocating computing tasks based on end-cloud integration. Figure 1 , Figure 1 The following is a flow chart of an embodiment of a method for allocating computing tasks based on end-cloud integration. Figure 1 In the embodiment, the computing task allocation method based on end-cloud integration may include: 101. Obtain the target resource pool for end-cloud integration.

[0022] In the embodiments of the present application, the target resource pool for end-cloud integration refers to a resource pool that aggregates the computing resources of terminal devices and cloud servers. Computing resources may include, for example, CPU (Central Processing Unit) computing power and GPU (Graphics Processing Unit) computing power. A terminal device refers to a user device through which a user can trigger computing tasks. For example, in the interior design industry, computing tasks may include generating virtual reality three-dimensional images related to interior design. Cloud servers, unlike terminal devices, are cloud-based devices.

[0023] 102. In the target resource pool, determine the target cloud resources, target local resources, and target associated resources that match the target computing task, where the target cloud resources come from the cloud server, the target local resources come from the first user terminal that triggers the target computing task, and the target associated resources come from the second user terminal associated with the first user terminal.

[0024] In an embodiment of the present application, the target resource pool summarizes at least three types of computing resources, including: cloud resources, local resources, and associated resources. Cloud resources come from the cloud server, local resources come from the first user terminal that triggers the target computing task, and associated resources come from the second user terminal associated with the first user terminal. It can be seen that the target resource pool summarizes the computing resources of the cloud server, the computing resources of the first user terminal, and the computing resources of the second user terminal. Therefore, in the target resource pool, the target cloud resources, target local resources, and target associated resources that match the target computing task can be determined.

[0025] 103. Split the target computing task into multiple sub-computing tasks.

[0026] In the embodiments of the present application, the target computing task is divisible. Therefore, the target computing task can be divided into multiple sub-computing tasks, allowing data processing to be performed in parallel across the multiple sub-computing tasks, thereby improving computing efficiency. For example, if the target computing task is an image processing task (e.g., generating a virtual reality 3D image related to interior design), the image can be processed in blocks to generate multiple sub-computing tasks. The target computing task segmentation strategy can be pre-set based on actual needs and is not limited here.

[0027] 104. Distribute the multiple sub-computing tasks to the cloud server, the first user terminal, and the second user terminal, so as to respectively utilize the target cloud resources, the target local resources, and the target associated resources for data processing.

[0028] In an embodiment of the present application, since multiple sub-computing tasks need to be processed in parallel, the multiple sub-computing tasks can be distributed to the cloud server, the first user terminal, and the second user terminal to utilize target cloud resources, target local resources, and target associated resources to process data in parallel. Each of the cloud server, the first user terminal, and the second user terminal can be distributed with at least one sub-computing task, and the cloud server, the first user terminal, and the second user terminal can be distributed with different sub-computing tasks.

[0029] It can be seen that in the above embodiments of the present application, by dividing the target computing task into multiple sub-computing tasks, and then distributing them to the cloud server, the first user terminal and the second user terminal, the target cloud resources, the target local resources and the target associated resources are comprehensively utilized for data processing. Due to the addition of target associated resources, the cloud resources required for the target computing task can be reduced, thereby reducing the cost required for the computing task.

[0030] In some embodiments of the present application, after distributing multiple sub-computing tasks to a cloud server, a first user terminal, and a second user terminal to respectively utilize target cloud resources, target local resources, and target associated resources for data processing, it may also include: respectively receiving sub-computing results fed back by the cloud server, the first user terminal, and the second user terminal, wherein each sub-computing result is the data processing result of the corresponding distributed sub-computing task; determining the calculation result of the target computing task based on multiple sub-computing results, for example, the multiple sub-computing results can be combined according to the combination strategy corresponding to the segmentation strategy of the target computing task to obtain the calculation result of the target computing task.

[0031] In addition, after obtaining the calculation result of the target calculation task, the calculation result can also be fed back to the first user terminal that triggers the target calculation task, so that the first user terminal outputs the calculation result.

[0032] In some embodiments of the present application, Figure 2 As shown, in Figure 1 Based on the illustrated embodiment, obtaining the target resource pool for end-cloud fusion may include: 201. Obtain a user terminal set associated with a first user terminal that triggers a target computing task.

[0033] In an embodiment of the present application, a user terminal set includes multiple user terminals, and each user terminal in the user terminal set is associated with a first user terminal. This association relationship representation can use the computing resources of the user terminals in the user terminal set to complete the target computing task triggered by the first user terminal. Taking the interior design industry as an example, the teaching classroom of the interior design industry may include multiple user terminals, and each user terminal can be used by one student to study, practice, and take exams on knowledge related to the interior design industry. Therefore, among the multiple user terminals in the same teaching classroom of the interior design industry, the user terminal that triggers the target computing task can be used as the first terminal, and the other user terminals can be used as user terminals in the user terminal set associated with the first user terminal, so as to realize the sharing of computing resources among multiple user terminals in the same teaching classroom.

[0034] In some embodiments of the present application, obtaining a set of user terminals associated with the first user terminal that triggers the target computing task may include: determining the intranet environment where the first user terminal that triggers the target computing task is located; summarizing the other user terminals in the intranet environment except the first user terminal to obtain a set of user terminals. It is understandable that multiple user terminals that can share computing resources are usually in the same intranet environment. Taking the interior design industry as an example, multiple user terminals in the teaching classroom of the interior design industry are usually in the same intranet environment, so that multiple user terminals can directly transmit data in the intranet environment without going through the external network, so as to improve the speed and convenience of data transmission. Therefore, other user terminals except the first user terminal in the same intranet environment can be summarized to obtain a set of user terminals associated with the first user terminal.

[0035] 202. Determine, in the user terminal set, a plurality of second user terminals in an idle state.

[0036] In the embodiment of the present application, since the computing resources of the second user terminal need to be used, it is necessary to filter out the second user terminal in the idle state from the user terminal set to avoid affecting the use of the user terminal in the non-idle state.

[0037] In some embodiments of the present application, determining multiple second user terminals in an idle state in a user terminal set may include: determining the load rate of each user terminal in the user terminal set, where the load rate may include, for example, at least one of a CPU load rate and a GPU load rate; and determining, in the user terminal set, a user terminal with a load rate less than a load rate threshold and no computing tasks as a second user terminal. It is understood that if the load rate of a user terminal is less than the load rate threshold and no computing tasks are present, it indicates that sharing computing resources will not significantly affect the normal use of the user terminal, and therefore the user terminal can be designated as the second user terminal.

[0038] In some embodiments of the present application, a method for determining the load rate threshold is described. Specifically, the computing task allocation method based on end-cloud integration also includes: obtaining the current load rate of the first user terminal; determining the load rate threshold based on the current load rate of the first user terminal, for example, the current load rate of the first user terminal can be directly used as the load rate threshold, or any value less than the current load rate of the first user terminal can be used as the load rate threshold to avoid the load rate of the second user terminal being too high relative to the load rate of the first user terminal after the sub-computing task is distributed to the second user terminal. It can be seen that the present application determines the load rate threshold based on the current load rate of the first user terminal, so that the load rate of the second user terminal and the load rate of the first user terminal can be kept relatively balanced.

[0039] 203. Aggregate computing resources for the cloud server, the first user terminal, and multiple second user terminals to obtain a target resource pool.

[0040] In an embodiment of the present application, since the cloud server is pre-set, after determining multiple second user terminals, the computing resources of the cloud server, the first user terminal and the multiple second user terminals can be aggregated to obtain a target resource pool.

[0041] It can be seen that in the above-mentioned embodiment of the present application, by obtaining the user terminal set associated with the first user terminal that triggers the target computing task, and then determining multiple second user terminals in an idle state, and then performing computing resource aggregation processing, a target resource pool of computing resources including a cloud server, a first user terminal and multiple second user terminals is obtained.

[0042] In some embodiments of the present application, Figure 3 As shown, in Figures 1 to 2 Based on any of the embodiments shown, distributing multiple sub-computing tasks to the cloud server, the first user terminal, and the second user terminal may include: 301. Determine a first difference between a current load rate of a first user terminal and a target load rate.

[0043] In an embodiment of the present application, the target load rate is a preset load rate target value, and the target load rate is greater than the current load rate of the first user terminal. For example, the target load rate may be 100%, 90%, etc. The first gap value may be the difference between the target load rate and the current load rate of the first user terminal.

[0044] 302. Determine a second difference between a current load rate and a target load rate of each second user terminal.

[0045] In an embodiment of the present application, the second gap value may be a difference between the target load rate and the current load rate of the corresponding second user terminal.

[0046] 303. Determine a task allocation ratio between the first user terminal and the corresponding second user terminal based on a ratio between the first gap value and each second gap value.

[0047] In an embodiment of the present application, since the current load rate of the first user terminal and the current load rate of each second user terminal may differ from the target load rate, a task allocation ratio between the first user terminal and the corresponding second user terminal may be set accordingly to achieve load balancing between the first user terminal and each second user terminal, so that the user of the first user terminal and the user of each second user terminal have a similar user terminal usage experience, thereby ensuring consistency in the user terminal usage experience. The task allocation ratio can be, for example, an allocation ratio between the number of sub-computing tasks or an allocation ratio between the data amounts of the sub-computing tasks.

[0048] In some embodiments of the present application, the ratio between the first gap value and each second gap value can be directly used as the task allocation ratio between the first user terminal and the corresponding second user terminal. The corresponding second user terminal refers to a second user terminal for which the gap between the current load rate and the target load rate is the second gap value.

[0049] In some embodiments of the present application, the task allocation ratio between the first user terminal and the corresponding second user terminal can also be further determined based on the ratio between the first gap value and each second gap value. Specifically, determining the task allocation ratio between the first user terminal and the corresponding second user terminal based on the ratio between the first gap value and each second gap value can include: obtaining the delay difference between the current communication delay of the first user terminal and the current communication delay of each second user terminal, wherein the current communication delay of the first user terminal refers to the current communication delay between the first user terminal and the computing task allocation device based on end-cloud fusion, and the current communication delay of the second user terminal refers to the current communication delay between the second user terminal and the computing task allocation device based on end-cloud fusion, and the delay difference can specifically be a delay difference; based on each delay gap, correcting the ratio between the first gap value and the corresponding second gap value to obtain the task allocation ratio between the first user terminal and the corresponding second user terminal.

[0050] In some embodiments of the present application, an example is given of a method for correcting the ratio between the first gap value and the corresponding second gap value. For example, when the delay gap is a positive value, the ratio between the first gap value and the corresponding second gap value can be reduced, so that the proportion of the first user terminal in the task allocation ratio between the first user terminal and the corresponding second user terminal is also reduced. In this way, relative to the corresponding second user terminal, the first user terminal can complete the distributed sub-computing task faster to make up for the delay difference between the current communication delay of the first user terminal and the current communication delay of the corresponding second user terminal, so that the overall delay of the first user terminal and the overall delay of the corresponding second user terminal tend to be consistent, so that the calculation result of the target computing task can be obtained faster. For another example, when the delay gap is a negative value, the ratio between the first gap value and the corresponding second gap value can be increased, so that the proportion of the first user terminal in the task allocation ratio between the first user terminal and the corresponding second user terminal also increases. In this way, relative to the first user terminal, the corresponding second user terminal can complete the distributed sub-computing task faster to make up for the delay gap between the current communication delay of the first user terminal and the current communication delay of the corresponding second user terminal, so that the overall delay of the corresponding second user terminal tends to be consistent with the overall delay of the first user terminal, so that the calculation result of the target calculation task can be obtained faster.

[0051] Furthermore, in the step of reducing the ratio between the first gap value and the corresponding second gap value, the magnitude of the reduction in the ratio may be determined based on the absolute value of the delay difference, for example, the magnitude of the reduction in the ratio may be positively correlated with the absolute value of the delay difference. Furthermore, in the step of increasing the ratio between the first gap value and the corresponding second gap value, the magnitude of the increase in the ratio may be determined based on the absolute value of the delay difference, for example, the magnitude of the increase in the ratio may be positively correlated with the absolute value of the delay difference.

[0052] 304. Distribute the multiple sub-computing tasks to the cloud server, the first user terminal, and the second user terminal according to the task distribution ratio.

[0053] In an embodiment of the present application, since the task allocation ratio can be the allocation ratio between the number of sub-computing tasks or the allocation ratio between the data amounts of the sub-computing tasks, after distributing multiple sub-computing tasks to the cloud server, the first user terminal and the second user terminal according to the task allocation ratio, the ratio between the number of sub-computing tasks distributed to the first user terminal and the number of sub-computing tasks distributed to each second user terminal can be equal to the corresponding task allocation ratio, or the ratio between the data amount of the sub-computing tasks distributed to the first user terminal and the data amount of the sub-computing tasks distributed to each second user terminal can be equal to or close to the corresponding task allocation ratio. In this way, load balancing and overall delay consistency between the first user terminal and each second user terminal can be achieved. Among them, the calculation method of the data amount of the sub-computing task can be set based on actual needs and is not limited here.

[0054] In some embodiments of the present application, in the step of distributing multiple sub-computing tasks to the cloud server, the first user terminal, and the second user terminal, the ratio between the sum of the number of sub-computing tasks distributed to the first user terminal and each second user terminal and the number of sub-computing tasks distributed to the cloud server can be a preset ratio, or the ratio between the sum of the data volume of the sub-computing tasks distributed to the first user terminal and each second user terminal and the data volume of the sub-computing tasks distributed to the cloud server can be a preset ratio. In this way, the preset ratio can be used to quantitatively reduce the cloud resources required for the target computing task, thereby quantitatively calculating the cost required for the computing task, making the control of the reduction in cloud resources required for the target computing task and the cost control more accurate.

[0055] In the second aspect, based on the computing task allocation method based on end-cloud fusion of the above embodiment, the embodiment of the present application provides a computing task allocation device based on end-cloud fusion. The computing task allocation device based on end-cloud fusion is used to execute the steps in any embodiment of the computing task allocation method based on end-cloud fusion. Specifically, referring to Figure 4 , the computing task allocation device 400 based on end-cloud integration may include: Acquisition module 401, used to acquire the target resource pool of end-cloud integration; Determination module 402 is used to determine target cloud resources, target local resources, and target associated resources that match the target computing task in the target resource pool, wherein the target cloud resources are from the cloud server, the target local resources are from the first user terminal that triggers the target computing task, and the target associated resources are from the second user terminal associated with the first user terminal; A splitting module 403 is used to split the target computing task into multiple sub-computing tasks; The distribution module 404 is used to distribute multiple sub-computing tasks to the cloud server, the first user terminal and the second user terminal, so as to respectively utilize the target cloud resources, the target local resources and the target associated resources for data processing.

[0056] In a third aspect, embodiments of the present application provide a computer device that integrates any of the end-cloud convergence-based computing task allocation devices provided in the embodiments of the present application. The computer device includes a processor and a memory, wherein the memory stores a computer program configured to be executed by the processor to implement the end-cloud convergence-based computing task allocation method described in any of the above embodiments, for example: Obtain a target resource pool for end-cloud integration; in the target resource pool, determine target cloud resources, target local resources, and target associated resources that match the target computing task, wherein the target cloud resources come from the cloud server, the target local resources come from the first user terminal that triggers the target computing task, and the target associated resources come from the second user terminal associated with the first user terminal; split the target computing task into multiple sub-computing tasks; distribute the multiple sub-computing tasks to the cloud server, the first user terminal, and the second user terminal, so as to respectively utilize the target cloud resources, the target local resources, and the target associated resources for data processing.

[0057] In a fourth aspect, an embodiment of the present application provides a computer device that integrates any device for allocating computing tasks based on end-cloud fusion provided in an embodiment of the present application. Figure 5 , which shows a schematic diagram of the structure of the computer device involved in the embodiment of the present application, specifically: The computer device may include one or more processing core processors 501, one or more computer readable storage medium storage units 502, a power supply 503 and an input unit 504. Those skilled in the art will understand that Figure 5 The computer device structure shown in the figure does not constitute a limitation on the computer device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently. Processor 501 is the control center of the computer device. It connects the various components of the computer device using various interfaces and circuits. By running or executing software programs and / or modules stored in storage unit 502 and accessing data stored in storage unit 502, it performs various functions of the computer device and processes data, thereby providing overall monitoring of the computer device. Optionally, processor 501 may include one or more processing cores. Preferably, processor 501 may integrate an application processor and a modem processor, with the application processor primarily processing the operating system, user interface, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into processor 501.

[0058] The storage unit 502 can be used to store software programs and modules. The processor 501 executes various functional applications and data processing by running the software programs and modules stored in the storage unit 502. The storage unit 502 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as sound playback or image playback); the data storage area may store data generated based on the use of the computer device. Furthermore, the storage unit 502 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state memory device. Accordingly, the storage unit 502 may also include a memory controller to provide the processor 501 with access to the storage unit 502.

[0059] The computer device also includes a power supply 503 for supplying power to various components. Preferably, the power supply 503 can be logically connected to the processor 501 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The power supply 503 can also include one or more DC or AC power supplies, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components.

[0060] The computer device may further include an input unit 504 , which may be configured to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0061] Although not shown, the computer device may further include a display unit, etc., which will not be described in detail here. Specifically, in the embodiment of the present application, the processor 501 in the computer device will load the executable files corresponding to the processes of one or more application programs into the storage unit 502 according to the following instructions, and the processor 501 will run the application programs stored in the storage unit 502 to implement various functions, such as: Obtain a target resource pool for end-cloud integration; in the target resource pool, determine target cloud resources, target local resources, and target associated resources that match the target computing task, wherein the target cloud resources come from the cloud server, the target local resources come from the first user terminal that triggers the target computing task, and the target associated resources come from the second user terminal associated with the first user terminal; split the target computing task into multiple sub-computing tasks; distribute the multiple sub-computing tasks to the cloud server, the first user terminal, and the second user terminal, so as to respectively utilize the target cloud resources, the target local resources, and the target associated resources for data processing.

[0062] In a fifth aspect, embodiments of the present application provide a computer-readable storage medium, which may include: a read-only memory (ROM), a random access memory (RAM), a disk, or an optical disk. The computer-readable storage medium stores a computer program, which is configured to be executed by a processor to implement the computing task allocation method based on end-cloud integration as described in any of the above items, for example: Obtain a target resource pool for end-cloud integration; in the target resource pool, determine target cloud resources, target local resources, and target associated resources that match the target computing task, wherein the target cloud resources come from the cloud server, the target local resources come from the first user terminal that triggers the target computing task, and the target associated resources come from the second user terminal associated with the first user terminal; split the target computing task into multiple sub-computing tasks; distribute the multiple sub-computing tasks to the cloud server, the first user terminal, and the second user terminal, so as to respectively utilize the target cloud resources, the target local resources, and the target associated resources for data processing.

[0063] In a sixth aspect, embodiments of the present application provide a computer program product or computer program, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to execute the computing task allocation method based on end-cloud integration as described in any one of the above items, for example: Obtain a target resource pool for end-cloud integration; in the target resource pool, determine target cloud resources, target local resources, and target associated resources that match the target computing task, wherein the target cloud resources come from the cloud server, the target local resources come from the first user terminal that triggers the target computing task, and the target associated resources come from the second user terminal associated with the first user terminal; split the target computing task into multiple sub-computing tasks; distribute the multiple sub-computing tasks to the cloud server, the first user terminal, and the second user terminal, so as to respectively utilize the target cloud resources, the target local resources, and the target associated resources for data processing.

[0064] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, based on the ideas of the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A computing task allocation method based on end-cloud integration, characterized in that: The computing task allocation method based on end-cloud integration includes: Obtain the target resource pool for end-cloud integration; In the target resource pool, target cloud resources, target local resources, and target associated resources that match the target computing task are determined, wherein the target cloud resources are from a cloud server, the target local resources are from a first user terminal that triggers the target computing task, and the target associated resources are from a second user terminal associated with the first user terminal; Splitting the target computing task into a plurality of sub-computing tasks; Distribute the plurality of sub-computing tasks to the cloud server, the first user terminal, and the second user terminal, so as to respectively utilize the target cloud resources, the target local resources, and the target associated resources for data processing.

2. The computing task allocation method based on end-cloud integration according to claim 1, characterized in that: After distributing the plurality of sub-computing tasks to the cloud server, the first user terminal, and the second user terminal, the method further includes: Receiving sub-computation results fed back by the cloud server, the first user terminal, and the second user terminal respectively; Based on the multiple sub-computation results, a calculation result of the target computing task is determined.

3. The computing task allocation method based on end-cloud integration according to claim 1, characterized in that: The obtaining of the target resource pool for end-cloud integration includes: Obtain a user terminal set associated with the first user terminal that triggers the target computing task; Determining, from the user terminal set, a plurality of second user terminals in an idle state; Computing resources are aggregated and processed on the cloud server, the first user terminal, and multiple second user terminals to obtain the target resource pool.

4. The computing task allocation method based on end-cloud integration according to claim 3 is characterized in that: The determining, in the set of user terminals, a plurality of second user terminals in an idle state includes: determining a load rate of each user terminal in the user terminal set; In the user terminal set, a user terminal whose load rate is less than a load rate threshold and has no computing tasks is used as the second user terminal.

5. The computing task allocation method based on end-cloud integration according to claim 4 is characterized in that: The computing task allocation method based on end-cloud integration also includes: Obtaining a current load rate of the first user terminal; The load rate threshold is determined based on a current load rate of the first user terminal.

6. The computing task allocation method based on end-cloud integration according to claim 3 is characterized in that: The acquiring of a user terminal set associated with the first user terminal that triggers the target computing task includes: Determine the intranet environment where the first user terminal that triggers the target computing task is located; The user terminals other than the first user terminal in the intranet environment are aggregated to obtain the user terminal set.

7. The computing task allocation method based on end-cloud integration according to claim 1, characterized in that: The distributing the plurality of sub-computing tasks to the cloud server, the first user terminal, and the second user terminal includes: Determining a first difference between a current load rate of the first user terminal and a target load rate; determining a second difference between a current load rate of each second user terminal and the target load rate; determining, based on a ratio between the first gap value and each of the second gap values, a task allocation ratio between the first user terminal and the corresponding second user terminal; According to the task allocation ratio, the plurality of sub-computing tasks are distributed to the cloud server, the first user terminal, and the second user terminal.

8. The computing task allocation method based on end-cloud integration according to claim 7 is characterized in that: The determining, based on a ratio between the first gap value and each of the second gap values, a task allocation ratio between the first user terminal and the corresponding second user terminal includes: Obtaining a delay difference between a current communication delay of the first user terminal and a current communication delay of each of the second user terminals; Based on each of the delay differences, a ratio between the first difference value and the corresponding second difference value is corrected to obtain a task allocation ratio between the first user terminal and the corresponding second user terminal.

9. A computer device, characterized in that: The computer device includes a processor and a memory, wherein a computer program is stored in the memory, and the computer program is configured to be executed by the processor to implement the computing task allocation method based on end-cloud integration according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which is configured to be executed by a processor to implement the computing task allocation method based on end-cloud integration according to any one of claims 1 to 8.

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