End-cloud fusion-based computing task allocation method, computer device and medium

Abstract

The application provides a computing task allocation method based on end-cloud fusion, a computer device and a medium. The method comprises the following steps: obtaining a target resource pool based on end-cloud fusion; determining a target cloud resource, a target local resource and a target associated resource matched with a target computing task in the target resource pool, wherein the target cloud resource comes from a cloud server, the target local resource comes from a first user terminal triggering the target computing task, and the target associated resource comes from a second user terminal associated with the first user terminal; dividing the target computing task into a plurality of sub-computing tasks; and distributing the plurality of sub-computing tasks to the cloud server, the first user terminal and the 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. The application can reduce the cloud resource required by the target computing task, thereby reducing the cost required by the computing task.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of end-cloud fusion processing, in particular to a computing task allocation method based on end-cloud fusion, a computer device and a medium. BACKGROUND

[0002] In related technologies, when facing a complex computing task, an end-cloud fusion manner can be used for data processing to quickly complete the computing by using resources of a cloud server end. However, the cloud resources required by this end-cloud fusion manner are usually excessive, which greatly increases the cost required by the computing task. SUMMARY

[0003] Embodiments of the present application provide a computing task allocation method based on end-cloud fusion, a computer device and a medium, aiming to reduce the cloud resources required by a target computing task, thereby reducing the cost required by the computing task.

[0004] In a first aspect, embodiments of the present application provide a computing task allocation method based on end-cloud fusion, which comprises:

[0005] obtaining a target resource pool of end-cloud fusion;

[0006] determining, in the target resource pool, a target cloud resource, a target local resource and a target associated resource matched with a target computing task, wherein the target cloud resource is from a cloud server, the target local resource is from a first user terminal triggering the target computing task, and the target associated resource is from a second user terminal associated with the first user terminal;

[0007] dividing the target computing task into a plurality of sub-computing tasks;

[0008] distributing the plurality of sub-computing tasks to the cloud server, the first user terminal and the second user terminal, so as to respectively use the target cloud resource, the target local resource and the target associated resource for data processing.

[0009] In some embodiments, after the plurality of sub-computing tasks are distributed to the cloud server, the first user terminal and the second user terminal, the method further comprises:

[0010] respectively receiving sub-computing results fed back by the cloud server, the first user terminal and the second user terminal;

[0011] determining a computing result of the target computing task based on the plurality of sub-computing results.

[0012] In some embodiments, the obtaining of the target resource pool of end-cloud fusion comprises:

[0013] obtaining a user terminal set associated with a first user terminal triggering the target computing task;

[0014] determining, in the user terminal set, a plurality of second user terminals in an idle state;

[0015] performing computing resource aggregation processing on the cloud server, the first user terminal, and the plurality of second user terminals to obtain the target resource pool.

[0016] In some embodiments, the determining, in the user terminal set, a plurality of second user terminals in an idle state comprises:

[0017] determining a load rate of each user terminal in the user terminal set;

[0018] in the user terminal set, taking a user terminal with a load rate less than a load rate threshold and without a computing task as the second user terminal.

[0019] In some embodiments, the end-cloud fusion-based computing task allocation method further comprises:

[0020] obtaining a current load rate of the first user terminal;

[0021] determining the load rate threshold based on the current load rate of the first user terminal.

[0022] In some embodiments, the obtaining a user terminal set associated with a first user terminal triggering the target computing task comprises:

[0023] determining an intranet environment in which the first user terminal triggering the target computing task is located;

[0024] aggregating other user terminals in the intranet environment except the first user terminal to obtain the user terminal set.

[0025] In some embodiments, the distributing a plurality of sub-computing tasks to the cloud server, the first user terminal, and the second user terminals comprises:

[0026] determining a first difference value between a current load rate of the first user terminal and a target load rate;

[0027] determining a second difference value between a current load rate of each second user terminal and the target load rate;

[0028] determining a task allocation ratio between the first user terminal and the corresponding second user terminal based on a ratio between the first difference value and each second difference value;

[0029] 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.

[0030] In some embodiments, the task allocation ratio between the first user terminal and the corresponding second user terminal is determined based on a ratio between the first difference value and each second difference value, comprising:

[0031] Obtaining a delay difference between a current communication delay of the first user terminal and a current communication delay of each second user terminal;

[0032] Based on each delay difference, the ratio between the first difference value and the corresponding second difference value is corrected to obtain the task allocation ratio between the first user terminal and the corresponding second user terminal.

[0033] In a second aspect, embodiments of the present application provide a computing task allocation device based on end-to-cloud fusion, comprising:

[0034] An acquisition module is configured to acquire a target resource pool based on end-to-cloud fusion;

[0035] A determination module is configured to determine, in the target resource pool, a target cloud resource, a target local resource and a target associated resource matched with a target computing task, wherein the target cloud resource is from a cloud server, the target local resource is from a first user terminal triggering the target computing task, and the target associated resource is from a second user terminal associated with the first user terminal;

[0036] A segmentation module is configured to segment the target computing task into a plurality of sub-computing tasks;

[0037] A distribution module is configured to 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 resource, the target local resource and the target associated resource for data processing.

[0038] In a third aspect, embodiments of the present application provide a computer device, comprising a processor and a memory, wherein the memory stores a computer program configured to be executed by the processor to implement the computing task allocation method based on end-to-cloud fusion as described in any one of the above aspects.

[0039] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium storing a computer program configured to be executed by a processor to implement the end-cloud fusion based computing task allocation method according to any one of the preceding aspects.

[0040] In a fifth aspect, an embodiment of the present application provides a computer program product comprising a computer program or instructions, which are executed by a processor to implement the end-cloud fusion based computing task allocation method according to any one of the preceding aspects.

[0041] Advantages of embodiments of the present application:

[0042] In the embodiments of the present application, the target computing task is divided into a plurality of sub-computing tasks, and then distributed to the cloud server, the first user terminal and the second user terminal, so as to comprehensively utilize the target cloud resource, the target local resource and the target associated resource for data processing. Since the target associated resource is added, the cloud resource required by the target computing task can be reduced, and thus the cost required by the computing task can be reduced. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort based on these drawings.

[0044] Figure 1 is an embodiment flow diagram of the end-cloud fusion based computing task allocation method provided by an embodiment of the present application;

[0045] Figure 2 is another embodiment flow diagram of the end-cloud fusion based computing task allocation method provided by an embodiment of the present application;

[0046] Figure 3 is still another embodiment flow diagram of the end-cloud fusion based computing task allocation method provided by an embodiment of the present application;

[0047] Figure 4 is an embodiment structure diagram of the end-cloud fusion based computing task allocation apparatus provided by an embodiment of the present application;

[0048] Figure 5 is an embodiment structure diagram of the computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0049] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0050] In the description of the present application, the meaning of "multiple" is two or more than two, unless otherwise explicitly and specifically limited. In addition, in the description of the present application, the terms "first", "second" are only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features limited by "first", "second" can explicitly or implicitly include one or more of the features.

[0051] In a first aspect, embodiments of the present application provide a computing task allocation method based on end-to-cloud fusion. Specifically, referring to Figure 1 , Figure 1 An embodiment flowchart of the computing task allocation method based on end-to-cloud fusion. In Figure 1 , the computing task allocation method based on end-to-cloud fusion can include:

[0052] 101, obtaining an end-to-cloud fusion target resource pool.

[0053] In the embodiments of the present application, the end-to-cloud fusion target resource pool refers to a resource pool that aggregates computing resources of terminal devices and computing resources of cloud servers. The computing resources may, for example, include CPU (Central Processing Unit) computing power, GPU (Graphics Processing Unit) computing power, etc. The terminal device refers to a user device, and the user can trigger a computing task through the terminal device. Taking the interior design industry as an example, the computing task can include the generation task of a virtual reality three-dimensional picture related to interior design. The cloud server is different from the terminal device, and the cloud server is a cloud device.

[0054] 102, determining target cloud resources, target local resources, and target associated resources matched with a target computing task in the target resource pool, wherein the target cloud resources come from the cloud server, the target local resources come from a first user terminal triggering the target computing task, and the target associated resources come from a second user terminal associated with the first user terminal.

[0055] In the embodiment of the present application, at least three types of computing resources are summarized in the target resource pool, including: cloud resources, local resources, and associated resources. The cloud resources come from the cloud server, the local resources come from the first user terminal triggering the target computing task, and the associated resources come from the second user terminal associated with the first user terminal. As can be seen, 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, the target cloud resource, the target local resource, and the target associated resource matched with the target computing task can be determined in the target resource pool.

[0056] 103. The target computing task is segmented into a plurality of sub-computing tasks.

[0057] In the embodiment of the present application, the target computing task is a divisible computing task, so the target computing task can be segmented into a plurality of sub-computing tasks to perform data processing in parallel through the plurality of sub-computing tasks, thereby improving the computing efficiency. Taking the target computing task as an image processing task (for example, a virtual reality three-dimensional picture generation task related to interior design) as an example, the image can be processed in blocks to obtain a plurality of sub-computing tasks. The segmentation strategy of the target computing task can be pre-set based on actual needs, which is not limited here.

[0058] 104. The plurality of sub-computing tasks are distributed to the cloud server, the first user terminal, and the second user terminal to perform data processing using the target cloud resource, the target local resource, and the target associated resource, respectively.

[0059] In the embodiment of the present application, since the plurality of sub-computing tasks need to be processed in parallel, the plurality of sub-computing tasks can be distributed to the cloud server, the first user terminal, and the second user terminal to perform data processing in parallel using the target cloud resource, the target local resource, and the target associated resource. Any one of the cloud server, the first user terminal, and the second user terminal can be assigned at least one sub-computing task, and the sub-computing tasks assigned to the cloud server, the first user terminal, and the second user terminal are different.

[0060] As can be seen in the above embodiment of the present application, by segmenting the target computing task into a plurality of sub-computing tasks and then distributing them to the cloud server, the first user terminal, and the second user terminal to perform data processing using the target cloud resource, the target local resource, and the target associated resource, respectively, the addition of the target associated resource can reduce the cloud resources required for the target computing task, thereby reducing the cost required for the computing task.

[0061] In some embodiments of the present application, after the plurality of sub-computing tasks are distributed to the cloud server, the first user terminal and the second user terminal to perform data processing by using the target cloud resource, the target local resource and the target associated resource respectively, the method can further include: receiving the sub-computing results fed back by the cloud server, the first user terminal and the second user terminal respectively, wherein each sub-computing result is a data processing result of the corresponding sub-computing task; determining the computing result of the target computing task based on the plurality of sub-computing results, for example, the plurality of sub-computing results can be combined according to the combination strategy corresponding to the segmentation strategy of the target computing task, and the computing result of the target computing task can be obtained.

[0062] In addition, after obtaining the computing result of the target computing task, the computing result can be fed back to the first user terminal triggering the target computing task, so that the first user terminal outputs the computing result.

[0063] In some embodiments of the present application, as shown in Figure 2 based on the embodiment shown in Figure 1 based on the embodiment shown in

[0064] 201, obtaining a user terminal set associated with the first user terminal triggering the target computing task.

[0065] In an embodiment of the present application, the user terminal set includes a plurality of user terminals, and each user terminal in the user terminal set is associated with the first user terminal. This association relationship 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. For example, in the teaching classroom of the interior design industry, a plurality of user terminals can be included, each user terminal can be used by a student to learn, practice and take exams related to the knowledge of the interior design industry, and therefore, in the plurality of user terminals in the same teaching classroom of the interior design industry, the user terminal triggering the target computing task can be used as the first terminal, and the other user terminals can be used as the user terminals in the user terminal set associated with the first user terminal, so as to share the computing resources among the plurality of user terminals in the same teaching classroom.

[0066] In some embodiments of the present application, obtaining the user terminal set associated with the first user terminal triggering the target computing task can include: determining an intranet environment in which the first user terminal triggering the target computing task is located; and aggregating other user terminals in the intranet environment except the first user terminal to obtain the user terminal set. It can be understood that a plurality of user terminals that can share computing resources are usually in the same intranet environment. For example, in the interior design industry, a plurality of user terminals in a teaching classroom of the interior design industry are usually in the same intranet environment, so that the plurality of user terminals can directly transmit data in the intranet environment without going through the extranet, thereby improving the speed and convenience of data transmission. Therefore, the user terminal set associated with the first user terminal can be obtained by aggregating other user terminals in the same intranet environment except the first user terminal.

[0067] 202、In the user terminal set, a plurality of second user terminals in an idle state are determined.

[0068] In embodiments of the present application, since the computing resources of the second user terminal are needed, it is necessary to screen the second user terminal in an idle state from the user terminal set to avoid affecting the use of the user terminal in a non-idle state.

[0069] In some embodiments of the present application, in the user terminal set, a plurality of second user terminals in an idle state are determined, which can include: determining the load rate of each user terminal in the user terminal set, wherein the load rate can include at least one of a CPU load rate and a GPU load rate; and in the user terminal set, the user terminal with a load rate less than a load rate threshold and without a computing task is taken as a second user terminal. It can be understood that if the load rate of the user terminal is less than the load rate threshold and there is no computing task, it indicates that the sharing of computing resources will not excessively affect the normal use of the user terminal, and therefore the user terminal can be taken as a second user terminal.

[0070] In some embodiments of the present application, the determination method of the load rate threshold is described. Specifically, the end-cloud fusion computing task allocation method further includes: obtaining the current load rate of the first user terminal; and 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 taken as the load rate threshold, or any value less than the current load rate of the first user terminal can be taken 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, by determining the load rate threshold based on the current load rate of the first user terminal, the load rate of the second user terminal and the load rate of the first user terminal can be kept relatively balanced.

[0071] 203、performing a computing resource aggregation processing on the cloud server, the first user terminal and the plurality of second user terminals to obtain a target resource pool.

[0072] In the embodiments of the present application, since the cloud server is pre-set, after the plurality of second user terminals are determined, the computing resources of the cloud server, the first user terminal and the plurality of second user terminals can be aggregated to obtain the target resource pool.

[0073] It can be seen that in the above embodiments of the present application, by obtaining the user terminal set associated with the first user terminal triggering the target computing task, and then determining the plurality of second user terminals in the idle state, the computing resource aggregation processing is performed to obtain the target resource pool including the computing resources of the cloud server, the first user terminal and the plurality of second user terminals.

[0074] In some embodiments of the present application, as shown in Figure 3 on the basis of any of the embodiments shown in Figures 1 to 2 the plurality of sub-computing tasks can be distributed to the cloud server, the first user terminal and the second user terminals, which can include:

[0075] 301、determine a first gap value between the current load rate of the first user terminal and a target load rate.

[0076] In the embodiments of the present application, the target load rate is a pre-set 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 can take values of 100%, 90%, etc. The first gap value can be the difference between the target load rate and the current load rate of the first user terminal.

[0077] 302、determine a second gap value between the current load rate of each second user terminal and the target load rate.

[0078] In the embodiments of the present application, the second gap value can be the difference between the target load rate and the current load rate of the corresponding second user terminal.

[0079] 303、based on the ratio between the first gap value and each second gap value, determine the task allocation ratio between the first user terminal and the corresponding second user terminal.

[0080] In the embodiments of the present application, since the first difference value and each second difference value can be different, the task allocation ratio between the first user terminal and the corresponding second user terminal can be set accordingly, so that load balancing can be achieved between the first user terminal and each second user terminal, and users of the first user terminal and users of each second user terminal can have similar user terminal experience, ensuring the consistency of user terminal 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 amount of sub-computing tasks.

[0081] In some embodiments of the present application, the ratio between the first difference value and each second difference 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 whose difference value between the current load rate and the target load rate is the second difference value.

[0082] 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 determined based on the ratio between the first difference value and each second difference 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 difference value and each second difference value can include: obtaining a 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-to-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-to-cloud fusion, and the delay difference can be a delay difference value; based on each delay difference, the ratio between the first difference value and the corresponding second difference value is corrected to obtain the task allocation ratio between the first user terminal and the corresponding second user terminal.

[0083] In some embodiments of the present application, the way of modifying the ratio between the first gap value and the corresponding second gap value is exemplified. 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, the first user terminal can complete the sub-computing tasks distributed to it faster relative to the corresponding second user terminal, so as 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 first user terminal and the overall delay of the corresponding second user terminal tend to be consistent, so that the computing 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 is also increased. In this way, the corresponding second user terminal can complete the sub-computing tasks distributed to it faster relative to the first user terminal, so as 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 and the overall delay of the first user terminal tend to be consistent, so that the computing result of the target computing task can be obtained faster.

[0084] In addition, in the step of reducing the ratio between the first gap value and the corresponding second gap value, the reduction amplitude of the ratio can be determined based on the absolute value of the delay gap, for example, the reduction amplitude of the ratio can be positively correlated with the absolute value of the delay gap. And in the step of increasing the ratio between the first gap value and the corresponding second gap value, the increase amplitude of the ratio can be determined based on the absolute value of the delay gap, for example, the increase amplitude of the ratio can be positively correlated with the absolute value of the delay gap.

[0085] 304、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.

[0086] In the embodiments of the present application, since the task allocation ratio can be an allocation ratio between the number of sub-computing tasks or an allocation ratio between the data amount of sub-computing tasks, after the multiple sub-computing tasks are distributed 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 sub-computing tasks distributed to the first user terminal and the data amount of 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 between the first user terminal and each second user terminal and consistent overall delay can be achieved. The calculation method of the data amount of sub-computing tasks can be set based on actual needs, which is not limited here.

[0087] In some embodiments of the present application, in the step of distributing the 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 amount of sub-computing tasks distributed to the first user terminal and each second user terminal and the data amount of 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 by the target computing task, so as to quantitatively calculate the cost required by the target computing task, so that the control of the reduction amount of cloud resources required by the target computing task and the cost control are more accurate.

[0088] In a second aspect, based on the above-mentioned computing task allocation method based on end-to-cloud fusion, the embodiments of the present application provide a computing task allocation device based on end-to-cloud fusion. The computing task allocation device based on end-to-cloud fusion is used to execute the steps in any of the embodiments of the above-mentioned computing task allocation method based on end-to-cloud fusion. Specifically, referring to Figure 4 The computing task allocation device based on end-to-cloud fusion 400 can include:

[0089] The acquisition module 401 is configured to acquire a target resource pool based on end-to-cloud fusion.

[0090] The determination module 402 is configured to determine, in the target resource pool, a target cloud resource, a target local resource and a target associated resource matched with the target computing task, wherein the target cloud resource comes from a cloud server, the target local resource comes from a first user terminal triggering the target computing task, and the target associated resource comes from a second user terminal associated with the first user terminal.

[0091] The segmentation module 403 is configured to segment the target computing task into a plurality of sub-computing tasks.

[0092] The distribution module 404 is configured to distribute the plurality of sub-computing tasks to the cloud server, the first user terminal and the 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.

[0093] In a third aspect, an embodiment of the present application provides a computer device integrating any of the computing task allocation apparatuses based on end-cloud fusion provided by the embodiments of the present application. The computer device includes a processor and a memory, and the memory stores a computer program configured to be executed by the processor to implement the computing task allocation method based on end-cloud fusion as described in any of the above embodiments, for example:

[0094] obtaining a target resource pool based on end-cloud fusion; determining, in the target resource pool, a target cloud resource, a target local resource and a target associated resource matched with the target computing task, wherein the target cloud resource is from a cloud server, the target local resource is from a first user terminal triggering the target computing task, and the target associated resource is from a second user terminal associated with the first user terminal; segmenting the target computing task into a plurality of sub-computing tasks; and distributing the plurality of sub-computing tasks to the cloud server, the first user terminal and the 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.

[0095] In a fourth aspect, an embodiment of the present application provides a computer device integrating any of the computing task allocation apparatuses based on end-cloud fusion provided by the embodiments of the present application. As shown in Figure 5 , which shows a structural schematic diagram of the computer device related to the embodiments of the present application. Specifically, the computer device includes:

[0096] The computer device can include a processor 501 with one or more processing cores, a storage unit 502 with one or more computer readable storage media, a power supply 503 and an input unit 504, etc. Those skilled in the art can understand that the computer device structure shown in the Figure 5 above does not constitute a limitation on the computer device, and can include more or fewer components than those shown, or combine certain components, or different component arrangements. Among them:

[0097] The processor 501 is a control center of the computer device, connects each part of the computer device by various interfaces and lines, executes various functions and processes data of the computer device by running or executing software programs and / or modules stored in the storage unit 502 and calling data stored in the storage unit 502, and thus monitors the computer device as a whole. Optionally, the processor 501 can include one or more processing cores; preferably, the processor 501 can integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface and an application program, and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor can also not be integrated into the processor 501.

[0098] The storage unit 502 can be used to store software programs and modules, and the processor 501 executes various functions and data processing by running the software programs and modules stored in the storage unit 502. The storage unit 502 can mainly include a storage program area and a storage data area, wherein the storage program area can store an operating system, at least one application program (such as a sound playing function, an image playing function, etc.) required by a function, etc.; and the storage data area can store data created according to use of the computer device, etc. In addition, the storage unit 502 can include a high-speed random access memory, and can also include a nonvolatile memory, for example, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state memory device. Accordingly, the storage unit 502 can also include a memory controller to provide access for the processor 501 to the storage unit 502.

[0099] The computer device further includes a power supply 503 for supplying power to each component, and preferably, the power supply 503 can be logically connected to the processor 501 through a power management system, so as to realize functions such as management of charging, discharging and power consumption management through the power management system. The power supply 503 can also include one or more than one direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power state indicator and any other components.

[0100] The computer device can further include an input unit 504, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0101] Although not shown, the computer device can also include a display unit and the like, which will not be described here. Specifically in the embodiments of the present application, the processor 501 in the computer device will load an executable file corresponding to a process of one or more than one application program into the storage unit 502 according to the following instructions, and run the application program stored in the storage unit 502 by the processor 501, so as to realize various functions, for example:

[0102] obtaining a target resource pool of end-cloud fusion; in the target resource pool, determining a target cloud resource, a target local resource, and a target associated resource matched with the target computing task, wherein the target cloud resource is from a cloud server, the target local resource is from a first user terminal triggering the target computing task, and the target associated resource is from a second user terminal associated with the first user terminal; segmenting the target computing task into a plurality of sub-computing tasks; and distributing the plurality of sub-computing tasks to the cloud server, the first user terminal, and the 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.

[0103] In a fifth aspect, an embodiment of the present application provides a computer readable storage medium, which can include a read only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc. The computer readable storage medium stores a computer program configured to be executed by a processor to implement the end-cloud fusion based computing task allocation method according to any one of the preceding aspects, for example:

[0104] obtaining a target resource pool of end-cloud fusion; in the target resource pool, determining a target cloud resource, a target local resource, and a target associated resource matched with the target computing task, wherein the target cloud resource is from a cloud server, the target local resource is from a first user terminal triggering the target computing task, and the target associated resource is from a second user terminal associated with the first user terminal; segmenting the target computing task into a plurality of sub-computing tasks; and distributing the plurality of sub-computing tasks to the cloud server, the first user terminal, and the 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.

[0105] In a sixth aspect, an embodiment of the present application provides a computer program product or a computer program, which includes 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, so that the computer device performs to implement the end-cloud fusion based computing task allocation method according to any one of the preceding aspects, for example:

[0106] Obtaining a target resource pool of cloud fusion; in the target resource pool, determining a target cloud resource, a target local resource, and a target associated resource matched with the target computing task, wherein the target cloud resource is from a cloud server, the target local resource is from a first user terminal triggering the target computing task, and the target associated resource is from a second user terminal associated with the first user terminal; dividing the target computing task into a plurality of sub-computing tasks; and distributing 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 resource, the target local resource, and the target associated resource to perform data processing.

[0107] The above has described the embodiments of the present application in detail, and the principles and implementation manners of the present application have been described by applying specific examples; the above embodiment descriptions are only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manners and application ranges will have changes; in summary, the content of the present description should not be understood as a limitation of the present application.

Claims

1. A method for computing task allocation based on end-cloud fusion, characterized in that, The end-cloud fusion-based computing task allocation method comprises: obtaining a target resource pool for end-cloud fusion; in the target resource pool, determining a target cloud resource, a target local resource and a target associated resource matched with a target computing task, wherein the target cloud resource is from a cloud server, the target local resource is from a first user terminal triggering the target computing task, and the target associated resource is from a second user terminal associated with the first user terminal; segmenting the target computing task into a plurality of sub-computing tasks; distributing the plurality of sub-computing tasks to the cloud server, the first user terminal and the second user terminal to perform data processing by using the target cloud resource, the target local resource and the target associated resource respectively; the distributing the plurality of sub-computing tasks to the cloud server, the first user terminal and the second user terminal comprises: determining a first difference value between a current load rate of the first user terminal and a target load rate; determining a second difference value between a current load rate of each second user terminal and the target load rate; determining a task allocation ratio between the first user terminal and the corresponding second user terminal based on a ratio between the first difference value and each second difference value; distributing the plurality of sub-computing tasks to the cloud server, the first user terminal and the second user terminal according to the task allocation ratio; the determining the task allocation ratio between the first user terminal and the corresponding second user terminal based on the ratio between the first difference value and each second difference value comprises: obtaining a delay difference between a current communication delay of the first user terminal and a current communication delay of each second user terminal; based on each delay difference, correcting the ratio between the first difference value and the corresponding second difference value to obtain the task allocation ratio between the first user terminal and the corresponding second user terminal. 2.The end-cloud fusion based computing task allocation method of claim 1, wherein, after the distributing the plurality of sub-computing tasks to the cloud server, the first user terminal and the second user terminal, further comprising: respectively receiving sub-computing results fed back by the cloud server, the first user terminal and the second user terminal; based on the plurality of sub-computing results, determining a computing result of the target computing task. 3.The end-cloud fusion based computing task allocation method of claim 1, wherein, the obtaining the target resource pool for end-cloud fusion comprises: obtaining a user terminal set associated with a first user terminal triggering the target computing task; in the user terminal set, determining a plurality of second user terminals in an idle state; performing computing resource aggregation processing on the cloud server, the first user terminal and the plurality of second user terminals to obtain the target resource pool.

4. The end-cloud fusion based computing task allocation method of claim 3, wherein, the determining the plurality of second user terminals in an idle state in the user terminal set comprises: determining a load rate of each user terminal in the user terminal set; in the user terminal set, taking a user terminal with a load rate less than a load rate threshold and without a computing task as the second user terminal.

5. The end-cloud fusion based computing task allocation method of claim 4, wherein, The end-cloud fusion-based computing task allocation method further includes: obtaining a current load rate of the first user terminal; determining the load rate threshold based on the current load rate of the first user terminal. 6.The end-cloud fusion based computing task allocation method of claim 3, wherein, The obtaining includes: determining an intranet environment in which the first user terminal triggering the target computing task is located; summarizing other user terminals in the intranet environment except the first user terminal to obtain the user terminal set.

7. A computer device, comprising: The computer device includes a processor and a memory, and the memory stores a computer program configured to be executed by the processor to implement the end-cloud fusion-based computing task allocation method of any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program configured to be executed by a processor to implement the end-cloud fusion-based computing task allocation method of any one of claims 1 to 6.

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