Computing power task execution method, computing power scheduling equipment and storage medium

Abstract

The invention discloses a computing power task execution method, computing power scheduling equipment and a storage medium, and relates to the technical field of data processing.The method comprises the steps that task attribute information of a target computing power task is obtained, and a matched historical computing power task and a historical computing power node set for executing the historical computing power task are obtained according to the task attribute information; determining association degree information between the historical computing power task and the target computing power task; determining a target computing power node set corresponding to the target computing power task according to the association degree information and the historical computing power node set; and scheduling the computing power nodes in the target computing power node set to execute the target computing power task. The objective of the invention is to improve the efficiency of computing power scheduling while ensuring that the target computing power task requirement is met, so as to meet the efficient computing power task execution requirement.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to a computing power task execution method, a computing power scheduling device, and a storage medium. Background Art

[0002] During the computing power leasing process, it is generally necessary to first construct a computing power node cluster, and then select computing power nodes (such as virtual machines or cloud containers, etc.) that meet the computing task requirements for scheduling and executing corresponding computing power tasks.

[0003] In the related art, when selecting computing power nodes, it is generally necessary to traverse each computing power node in the calculation to screen out a set of nodes that meet the requirements of the computing task. However, such a screening method has low efficiency, affects the efficiency of computing power scheduling, and is difficult to meet the requirements of efficient computing power task execution. Summary of the Invention

[0004] The main purpose of this application is to provide a computing power task execution method, a computing power scheduling device, and a storage medium, aiming to improve the efficiency of computing power scheduling to meet the requirements of efficient computing power task execution.

[0005] To achieve the above object, this application proposes a computing power task execution method, and the method includes: Obtain the task attribute information of the target computing power task, and obtain the matching historical computing power task and the set of historical computing power nodes that execute the historical computing power task according to the task attribute information; Determine the correlation information between the historical computing power task and the target computing power task; Determine the set of target computing power nodes corresponding to the target computing power task according to the correlation information and the set of historical computing power nodes; Schedule the computing power nodes in the set of target computing power nodes to execute the target computing power task.

[0006] In an embodiment, the step of determining the correlation information between the historical computing power task and the target computing power task includes: Determine the first status information representing the difference in data source locations according to the location data of the data sources corresponding to the historical computing power task and the target computing power task respectively; Determine the second status information representing the task difference according to the execution requirements of multiple subtasks included in the historical computing power task and the target computing power task respectively; Determine the third status information representing the difference in computing power types according to the computing power types corresponding to the historical computing power task and the target computing power task respectively; Wherein, the correlation information includes the first status information, the second status information, and the third status information.

[0007] In one embodiment, the step of determining the target computing power node set corresponding to the target computing power task according to the correlation information and the historical computing power node set includes: When the third status information includes that the computing power type of the historical computing power task is the same as that of the target computing power task, determine the similarity between the historical computing power task and the target computing power task according to the first status information and the second status information; When the similarity is greater than a first preset threshold, determine the historical computing power node set as the target computing power node set; When the similarity is greater than a second preset threshold and less than or equal to the first preset threshold, adjust the historical computing power node set according to the first status information and the second status information to obtain the target computing power node set; When the similarity is less than or equal to the second preset threshold, traverse all computing power nodes in the computing power node cluster, and use the node set that meets the preset computing power scheduling policy as the target computing power node set; Wherein, the second preset threshold is less than the first preset threshold.

[0008] In one embodiment, the first status information includes a first deviation value representing the difference in data source locations, the second status information includes a second deviation value representing the difference in computing power processing capabilities of task requirements, and the step of determining the similarity between the historical computing power task and the target computing power task according to the first status information and the second status information includes: According to the priority information between the data source location and the computing power processing capability corresponding to the target computing power task; Determine the first weight corresponding to the first deviation value and the second weight corresponding to the second deviation value according to the priority information; Calculate the similarity according to the first deviation value and the corresponding first weight, and the second deviation value and the corresponding second weight.

[0009] In one embodiment, the step of determining the target computing power node set corresponding to the target computing power task according to the correlation information and the historical computing power node set includes: When the third status information includes that the computing power type of the historical computing power task is different from that of the target computing power task, determine the similarity between the historical computing power task and the target computing power task according to the first status information and the second status information; When the similarity is greater than a first preset threshold, adjust the historical computing power node set according to the first status information, the second status information, and the third status information to obtain the target computing power node set; When the similarity is less than the first preset threshold, all computing power nodes in the computing power node cluster are traversed, and the set of nodes that meet the preset computing power scheduling policy is used as the target computing power node set.

[0010] In one embodiment, the step of determining the first status information representing the difference in data source locations according to the location data of the data sources respectively corresponding to the historical computing power task and the target computing power task includes: Determine the first central location of all data sources corresponding to the historical computing power task, and determine the second central location of all data sources corresponding to the target computing power task; Determine a first deviation value representing the difference in data source locations according to the distance between the first central location and the second central location, and the first status information includes the first deviation value.

[0011] In one embodiment, the execution requirements include the total number of subtasks of the task and the proportion of the number of subtasks allowed to be executed in parallel in the total number of tasks. The step of determining the second status information representing the task difference according to the execution requirements of multiple subtasks respectively included in the historical computing power task and the target computing power task includes: Determine a first difference between the total number of tasks corresponding to the historical computing power task and the total number of tasks corresponding to the target computing power task, and determine a second difference between the proportion corresponding to the historical computing power task and the proportion corresponding to the target computing power task; Determine a second deviation value representing the difference in computing power processing capabilities of the task requirements according to the first difference and the second difference, and the second status information includes the second deviation value.

[0012] In one embodiment, the task attribute information includes the task application scenario and the billing method. The step of obtaining a matching historical computing power task according to the task attribute information includes: Determine that the computing power task that meets the preset conditions and is executed before the current moment is the historical computing power task; The preset conditions include that the corresponding task application scenario and billing method are the same as those of the target computing power task.

[0013] In addition, to achieve the above object, the present application also proposes a computing power scheduling device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. The computer program is configured to implement the steps of the computing power task execution method as described above.

[0014] In addition, to achieve the above object, the present application further provides a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the computing power task execution method described above are implemented.

[0015] One or more technical solutions proposed by the present application have at least the following technical effects: Based on the task attribute information of the target computing power task, the set of historical computing power nodes used when the matching historical computing power task is executed is obtained, and then based on the correlation information between the historical computing power task and the target computing power task and the set of historical computing power nodes, the set of target computing power nodes for executing the target computing power task is determined. Based on this method, the analysis process of the set of target computing power nodes for currently executing the target computing power task can effectively reduce the process of traversing and screening all nodes in the computing power node cluster one by one, which can ensure that the requirements of the target computing power task are met while improving the efficiency of computing power scheduling to meet the requirements of efficient computing power task execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0017] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a schematic flowchart provided for Embodiment 1 of the computing power task execution method of the present application; Figure 2 It is a schematic diagram of the device structure of the hardware operating environment involved in the computing power task execution method in the embodiments of the present application.

[0019] The realization of the object, functional features, and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0021] To better understand the technical solutions of the present application, the following will be described in detail in combination with the accompanying drawings of the specification and the specific embodiments.

[0022] The main solution of the embodiment of the present application is: obtain the task attribute information of the target computing power task, obtain the matching historical computing power tasks and the set of historical computing power nodes that execute the historical computing power tasks according to the task attribute information; determine the correlation information between the historical computing power tasks and the target computing power task; determine the set of target computing power nodes corresponding to the target computing power task according to the correlation information and the set of historical computing power nodes; schedule the computing power nodes in the set of target computing power nodes to execute the target computing power task.

[0023] In this embodiment, for the convenience of description, the following will be described with a computing power scheduling device as the execution subject.

[0024] In the prior art, when selecting computing power nodes, it is generally necessary to traverse and calculate each computing power node in the computing power node cluster to screen out the set of nodes that meet the requirements of the computing task. However, such a screening method has low efficiency, affects the efficiency of computing power scheduling, and is difficult to meet the requirements of efficient execution of computing power tasks.

[0025] The present application provides the above solution. First, based on the task attribute information of the target computing power task, obtain the set of historical computing power nodes used when the matching historical computing power tasks are executed, and then determine the set of target computing power nodes for executing the target computing power task based on the correlation information between the historical computing power tasks and the target computing power task and the set of historical computing power nodes. Based on this method, the analysis process of the set of target computing power nodes for currently executing the target computing power task can effectively reduce the process of traversing and screening all nodes in the computing power node cluster one by one, can ensure that the requirements of the target computing power task are met while improving the efficiency of computing power scheduling, so as to meet the requirements of efficient execution of computing power tasks.

[0026] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, a computing power scheduling device, etc. that can implement the above functions. The following takes the computing power scheduling device as an example to illustrate this embodiment and the following embodiments.

[0027] Based on this, the embodiment of the present application provides a method for executing a computing power task, referring to Figure 1 , Figure 1 which is a schematic flowchart of the first embodiment of the method for executing a computing power task of the present application.

[0028] In this embodiment, the method for executing a computing power task includes steps S10 to S40: Step S10, obtain the task attribute information of the target computing power task, and obtain the matching historical computing power tasks and the set of historical computing power nodes that execute the historical computing power tasks according to the task attribute information; The target computing power task is the computing task executed by the currently required computing power nodes. The task attribute information may include at least one of the following: application scenario, computing power requirement information of the required computing power (such as computing power type, required computing power performance, performance mode, etc.), data source type, and so on.

[0029] The number of historical computing power tasks matched here can be one or more than one.

[0030] The historical computing power node set may include at least one of virtual machines, cloud containers, etc. In this embodiment, the computing power unit used by the nodes in the historical computing power node set is a GPU unit.

[0031] Compare the historical task attribute information corresponding to all the computing power tasks executed before the current moment with the task attribute information of the target computing power task, and determine the computing power task corresponding to the historical task attribute information that matches the task attribute information of the target computing power task as the historical computing power task. In this embodiment, the task attribute information includes the task application scenario and the billing method, and determine the computing power tasks that meet the preset conditions executed before the current moment as the historical computing power tasks; the preset conditions include that the corresponding task application scenario and billing method are consistent with the task application scenario and billing method of the target computing power task. Among them, the task application scenario may include one of the following: artificial intelligence (AI) large model training task, image generation task, video generation task, three-dimensional content creation task, etc.; the billing method may include hourly calculation or billing according to the processing volume, and so on.

[0032] Step S20, determine the correlation information between the historical computing power task and the target computing power task; The correlation information represents the similarity degree between the computing power usage states when the historical computing power task and the target computing power task are executed.

[0033] The correlation information can be obtained by analyzing the state parameters related to the computing power usage processes corresponding to the historical computing power task and the target computing power task (such as at least one of the location data of the data source, the execution requirements of subtasks, the computing power type, etc.), or can be obtained by analyzing the task description tags corresponding to the target computing power task and the task description tags corresponding to the historical computing power task (the task description tags here can be selected by the computing power requester from multiple preset tags).

[0034] Among them, when there are more than one historical computing power tasks, determine the correlation information between each historical computing power task and the target computing power task respectively.

[0035] Step S30, determine the target computing power node set corresponding to the target computing power task according to the correlation information and the historical computing power node set; In one implementation, when the correlation information meets the preset similarity condition, the historical computing power node set is determined as the target computing power node set. When the correlation information does not meet the preset similarity condition, the target computing power node set can be obtained by adjusting the historical computing power node set according to the correlation information.

[0036] In another implementation, when there are more than one historical computing power tasks, the historical computing power task with the highest similarity to the target computing power task can be determined as the target task from the more than one historical computing power tasks according to the more than one correlation information, and the historical computing power node set corresponding to the target task is used as the target computing power node set.

[0037] In yet another implementation, when there are more than one historical computing power tasks, the evaluation scores corresponding to each historical computing power task in different service metrics can be obtained, the metric with the highest priority in different service metrics for the target computing power task is obtained as the target metric, the historical computing power tasks with the evaluation scores corresponding to the target metric being greater than or equal to the preset score are used as reference tasks, and the target computing power node set is determined according to the correlation information corresponding to the reference tasks and the historical computing power node set. When there are more than one reference tasks, the reference task with the highest similarity to the target computing power task can be determined as the target task according to the correlation information, and the historical computing power node set corresponding to the target task is used as the target computing power node set.

[0038] Step S40, schedule the computing power nodes in the target computing power node set to execute the target computing power task.

[0039] During the execution of the target computing power task, all the computing power nodes in the target computing power node set can be called simultaneously, or the corresponding computing power nodes can be called in segments or according to the requirements of different subtasks in the target computing power task to execute the target computing power task.

[0040] This embodiment provides a method for executing a computing power task. First, based on the task attribute information of the target computing power task, the historical computing power node set used when the matching historical computing power task is executed is obtained. Then, based on the correlation information between the historical computing power task and the target computing power task and the historical computing power node set, the target computing power node set for executing the target computing power task is determined. Based on this method, the analysis process of the target computing power node set for currently executing the target computing power task can effectively reduce the process of traversing and screening all nodes in the computing power node cluster one by one, and can improve the efficiency of computing power scheduling while ensuring that the requirements of the target computing power task are met, so as to meet the requirements of efficient execution of computing power tasks.

[0041] In a feasible implementation manner, the step of determining the association degree information between the historical computing power task and the target computing power task includes: determining first status information representing the difference in data source positions according to the position data of the data sources respectively corresponding to the historical computing power task and the target computing power task; determining second status information representing the task difference according to the execution requirements of multiple subtasks respectively included in the historical computing power task and the target computing power task; determining third status information representing the difference in computing power types according to the computing power types respectively corresponding to the historical computing power task and the target computing power task; wherein, the association degree information includes the first status information, the second status information, and the third status information.

[0042] The greater the difference comprehensively represented by the first status information, the second status information, and the third status information, the smaller the similarity between the corresponding historical computing power task and the target computing power task.

[0043] The first status information may include whether the data source positions are the same or the position deviation between the data source positions. The second status information may include whether the execution requirements are the same or the requirement deviation between the execution requirements. The third status information includes whether the computing power types are the same or the type deviation situation between the computing power types.

[0044] Wherein, the data source is the data source of the data required during the execution of the computing power task. The data source corresponding to one computing power task can be one or more, and multiple data sources can be in the same position or different positions.

[0045] In this embodiment, determine the first central position of all data sources corresponding to the historical computing power task, and determine the second central position of all data sources corresponding to the target computing power task; determine a first deviation value representing the difference in data source positions according to the distance between the first central position and the second central position. The first status information includes the first deviation value. When there are multiple data sources corresponding to the historical computing power task or the target computing power task, the positions of multiple data sources can be connected pairwise by lines, and the largest enclosed area can be determined as the data source area among the multiple enclosed areas formed by the connection of multiple lines. The position where the center of gravity of the data source area is located can be used as the corresponding central position. Wherein, the distance between the first central position and the second central position can be used as the first deviation value. Here, the first deviation value can accurately reflect the difference in the data transmission duration corresponding to the historical computing power task and the target computing power task during the execution process. In some other implementation manners, the third central position where the hardware resources corresponding to the historical computing power node set are located can also be determined, the first distance between the first central position and the third central position and the second distance between the second central position and the third central position are determined, and the distance difference between the first distance and the second distance is used as the first deviation value.

[0046] In this embodiment, the execution requirements include the total number of subtasks of the task and the proportion of the number of subtasks that allow parallel execution in the total number of tasks. The step of determining the second status information representing the task difference according to the execution requirements of the multiple subtasks included in the historical computing power task and the target computing power task respectively includes: determining a first difference between the total number of tasks corresponding to the historical computing power task and the total number of tasks corresponding to the target computing power task, and determining a second difference between the proportion corresponding to the historical computing power task and the proportion corresponding to the target computing power task; determining a second deviation value representing the difference in computing power processing capabilities of the task requirements according to the first difference and the second difference, and the second status information includes the second deviation value. Among the multiple subtasks included in each computing power task, the subtasks that do not need to be processed in a set order are allowed to be executed in parallel, and the subtasks that need to be processed in a set order are not allowed to be executed in parallel. The total number of tasks can reflect the total number of threads required to be used by the computing power nodes scheduled when executing the corresponding computing power task. The first difference can accurately reflect the difference in the total scale of the subtasks to be processed by the historical computing power task and the target computing power task, and the second difference can accurately reflect the difference in the distribution rules of the multiple subtasks in the historical computing power task and the target computing power task in the computing power nodes. Based on this, the second deviation value determined by combining the first difference and the second difference can accurately reflect the difference between the computing power processing capabilities of the task requirements.

[0047] In this embodiment, when the computing power types corresponding to the historical computing power task and the target computing power task are both of one type (such as both intelligent computing power or both general computing power), it is determined that the computing power types corresponding to the historical computing power task and the target computing power task are the same when they are the same. When the computing power types corresponding to the historical computing power task and the target computing power task are more than one type (for example, including intelligent computing power and general computing power, etc.), the number of subtasks corresponding to each computing power type can be counted. When the types of computing power types corresponding to the historical computing power task and the target computing power task are the same, the quantity difference between the number of subtasks of the same computing power type in the historical computing power task and the target computing power task is determined. In the case where the data difference corresponding to each computing power type is less than a preset threshold, it is determined that the computing power types corresponding to the historical computing power task and the target computing power task are the same.

[0048] In this embodiment, the first status information, the second status information, and the third status information can accurately reflect the similarity degree between the target computing power task and the historical computing power task. Based on this, the determined set of target computing power nodes can ensure that while improving the scheduling efficiency, the computing power capabilities provided by the set of target computing power nodes can accurately meet the requirements of the target computing power task.

[0049] In a feasible implementation manner, the step of determining the target computing power node set corresponding to the target computing power task according to the association degree information and the historical computing power node set includes: when the third state information includes that the computing power type of the historical computing power task is the same as the computing power type of the target computing power task, determining the similarity between the historical computing power task and the target computing power task according to the first state information and the second state information; when the similarity is greater than a first preset threshold, determining the historical computing power node set as the target computing power node set; when the similarity is greater than a second preset threshold and less than or equal to the first preset threshold, adjusting the historical computing power node set according to the first state information and the second state information to obtain the target computing power node set; when the similarity is less than or equal to the second preset threshold, traversing all computing power nodes in the computing power node cluster, and taking the node set that meets the preset computing power scheduling policy as the target computing power node set; wherein, the second preset threshold is less than the first preset threshold.

[0050] Here, after screening out the above reference tasks from more than one historical computing power task, the similarity between the reference task and the target computing power task can be determined according to the first state information and the second state information corresponding to the reference task. When the similarity is greater than the first preset threshold, determining the historical computing power node set corresponding to the reference task as the target computing power node set; when the similarity is greater than the second preset threshold and less than or equal to the first preset threshold, adjusting the historical computing power node set corresponding to the reference task according to the first state information and the second state information corresponding to the reference task to obtain the target computing power node set; when the similarity is less than or equal to the second preset threshold, traversing all computing power nodes in the computing power node cluster, and taking the node set that meets the preset computing power scheduling policy as the target computing power node set.

[0051] Among them, in the process of adjusting the historical computing power node set according to the first status information and the second status information, the addition, reduction, or replacement of computing power nodes can be performed in the historical computing power node set. For example, when the first deviation value is greater than the first preset threshold, the first number of computing power nodes with the farthest distance between the location of the corresponding hardware resource and the second central location in the historical computing power node set can be replaced with the first computing power nodes of the same other computing power type. The capacity deviation between the computing power processing capacity of the first computing power node and that of the replaced computing power node is less than the preset deviation, and the distance between the location of the hardware resource corresponding to the first computing power node and the second central location is less than the preset distance. Among them, the first number can be determined according to the deviation amount between the first deviation value and the first preset threshold; when the second deviation value is greater than the second preset threshold and the computing power processing capacity of the target computing power task requirement corresponding to the second deviation value is greater than the computing power processing capacity of the historical computing power task requirement, the second number of computing power nodes with the largest computing power processing capacity in the node set where the distance between the location of the corresponding hardware resource and the second central location is greater than the preset distance in the historical computing power node set can be deleted. The second number can be determined according to the deviation amount between the second deviation value and the second preset threshold; when the second deviation value is greater than the second preset threshold and the computing power processing capacity of the target computing power task requirement corresponding to the second deviation value is less than the computing power processing capacity of the historical computing power task requirement, the third number of second computing power nodes can be added to the historical computing power node set. The distance between the location of the hardware resource corresponding to the second computing power node and the second central location is less than the preset distance and the computing power type is the same as the original computing power type in the historical computing power node set.

[0052] The preset computing power scheduling strategy here can be a pre-set fixed strategy or a strategy determined according to user needs.

[0053] In this embodiment, through the above method, it is beneficial to improve the computing power scheduling efficiency, ensure the efficient execution of tasks, and ensure that the target computing power task requirements can be accurately met.

[0054] In a feasible implementation manner, the first status information includes a first deviation value representing the difference in data source location, and the second status information includes a second deviation value representing the difference in computing power processing capacity of task requirements. The first deviation value and the second deviation value are determined in the manner mentioned in the above embodiment and will not be elaborated here. The step of determining the similarity between the historical computing power task and the target computing power task according to the first status information and the second status information includes: according to the priority information between the data source location and the computing power processing capacity corresponding to the target computing power task; determining the first weight corresponding to the first deviation value and the second weight corresponding to the second deviation value according to the priority information; calculating the similarity according to the first deviation value and the corresponding first weight, and the second deviation value and the corresponding second weight.

[0055] The priority information here can be set by the user based on requirements, or determined according to the relationship between the number of subtasks corresponding to different computing power types in more than one computing power type required by the target computing power task. For example, when the proportion of the number of subtasks corresponding to general computing power is greater than the proportion of the number of subtasks corresponding to intelligent computing power, the priority information is that the priority of the data source location is higher than the priority of the computing power processing ability; when the proportion of the number of subtasks corresponding to general computing power is less than the proportion of the number of subtasks corresponding to intelligent computing power, the priority information is that the priority of the data source location is lower than the priority of the computing power processing ability.

[0056] When the priority information is that the priority of the data source location is higher than the priority of the computing power processing ability, the first weight is greater than the second weight; when the priority information is that the priority of the data source location is lower than the priority of the computing power processing ability, the first weight is less than the second weight.

[0057] Among them, the similarity can be obtained by weighted average calculation of the first deviation value and the second deviation value based on the first weight and the second weight.

[0058] In this embodiment, it is beneficial to further improve the accuracy of the subsequently determined target computing power node set, thereby further ensuring the effective balance between the high efficiency of computing power task execution and the accuracy of demand matching.

[0059] In a feasible implementation manner, the step of determining the target computing power node set corresponding to the target computing power task according to the association degree information and the historical computing power node set includes: In the case where the third status information includes that the computing power type of the historical computing power task is inconsistent with the computing power type of the target computing power task, determine the similarity between the historical computing power task and the target computing power task according to the first status information and the second status information; in the case where the similarity is greater than the first preset threshold, adjust the historical computing power node set according to the first status information, the second status information, and the third status information to obtain the target computing power node set; in the case where the similarity is less than the first preset threshold, traverse all the computing power nodes in the computing power node cluster, and use the node set that meets the preset computing power scheduling strategy as the target computing power node set.

[0060] The same expressions as those in the above implementation manner here refer to the same concepts, and for the same parts in the corresponding solutions, they can be analogously implemented in the manner mentioned in the above implementation manner, and will not be elaborated here.

[0061] In the process of obtaining the target computing power node set by adjusting the historical computing power node set according to the first status information, the second status information, and the third status information, similar to the process of adjusting the historical computing power node set according to the first status information and the second status information, it is necessary to ensure that the computing power type corresponding to the adjusted historical computing power node set is consistent with the computing power type required by the target computing power task.

[0062] In this embodiment, through the above method, the efficient execution of the computing power task and the accuracy of demand matching can be further effectively balanced.

[0063] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the computing power task execution method of this application. Based on this technical concept, more forms of simple transformation are within the protection scope of this application.

[0064] Refer to Figure 2 , this application provides a computing power scheduling device 1, which may include: at least one processor 1001; and a memory 1002, an input / output (I / O) interface 1003, etc. that are communicatively connected to the at least one processor 1001; wherein, the memory 1002 stores instructions executable by the at least one processor 1001, and the instructions are executed by the at least one processor 1001 to enable the at least one processor 1001 to execute the computing power task execution method in the first embodiment above.

[0065] Next, refer to Figure 2 , which shows a schematic structural diagram of the computing power scheduling device 1 suitable for implementing the embodiments of this application. The computing power scheduling device 1 in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, etc. and fixed terminals such as digital TVs, desktop computers, etc. Figure 2 The shown computing power scheduling device 1 is only an example and should not bring any limitation to the functions and usage scope of the embodiments of this application.

[0066] Such as Figure 2As shown in the figure, the computing power scheduling device 1 may include a processor 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the programs stored in the memory 1002. Here, the programs in the memory 1002 can be programs in a read-only memory (ROM: Read Only Memory) or programs loaded from a storage device into a random access memory (RAM: Random Access Memory). In the RAM, various programs and data required for the operation of the computing power scheduling device 1 are also stored. The processor 1001, the memory 1002 (ROM and RAM), the input / output (I / O) interface 1003, etc. are connected to each other through a communication bus 1007. Generally, the following systems can be connected to the I / O interface 1003: an input device 1004 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1005 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; and a communication device 1006, etc. The communication device 1006 can allow the computing power scheduling device 1 to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows the computing power scheduling device 1 with various devices, it should be understood that it is not required to implement or have all the shown devices. Instead, more or fewer devices can be implemented or had.

[0067] In particular, according to the embodiments disclosed in the present application, the method flow described in the above embodiments can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device, or installed from the memory 1002. When the computer program is executed by the processor 1001, the above functions defined in the computing power task execution method of the embodiments disclosed in the present application are executed.

[0068] The computing power scheduling device provided by the present application adopts the computing power task execution method in the above embodiments, and can solve the technical problem of how to ensure that the target computing power task requirements are met while improving the efficiency of computing power scheduling to meet the requirements of efficient computing power task execution. Compared with the prior art, the beneficial effects of the computing power scheduling device provided by the present application are the same as those of the computing power task execution method provided by the above embodiments, and other technical features in the computing power scheduling device are the same as those disclosed in the method of the previous embodiment, and will not be elaborated here.

[0069] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the computing power task execution method in the above embodiments.

[0070] The computer-readable storage medium provided by the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program can be used by or combined with an instruction execution system, device, or component. The program code contained on the computer-readable storage medium can be transmitted by any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0071] The above computer-readable storage medium may be included in the computing power scheduling device; or it may exist alone and not be assembled into the computing power scheduling device.

[0072] The above computer-readable storage medium carries one or more programs. When the one or more programs are executed by the computing power scheduling device, the computing power scheduling device is caused to execute the following processes: obtaining task attribute information of a target computing power task, obtaining a matching historical computing power task and a set of historical computing power nodes that execute the historical computing power task according to the task attribute information; determining association degree information between the historical computing power task and the target computing power task; determining a set of target computing power nodes corresponding to the target computing power task according to the association degree information and the set of historical computing power nodes; and scheduling the computing power nodes in the set of target computing power nodes to execute the target computing power task.

[0073] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN: Local Area Network) or a wide area network (WAN: Wide Area Network), or it can be connected to an external computer (for example, by connecting through the Internet using an Internet service provider).

[0074] The readable storage medium provided by this application is a computer-readable storage medium. The computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for performing the above-mentioned computing power task execution method, and can solve the technical problem of how to ensure that the target computing power task requirements are met while improving the efficiency of computing power scheduling to meet the requirements of efficient computing power task execution. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by this application are the same as those of the computing power task execution method provided by the above-mentioned embodiments, and will not be elaborated here.

[0075] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0076] It should be understood that each part disclosed in this application can be implemented by hardware, software, firmware, or a combination thereof. The modules described in the embodiments of this application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation to the unit itself in some cases. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0077] The above are only some embodiments of this application, and thus do not limit the patent scope of this application. Any equivalent structural transformation made under the technical concept of this application by using the content of the specification and drawings of this application, or direct / indirect application in other related technical fields, is included in the patent protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A computing power task execution method, characterized in that, The method described above includes: Obtaining the task attribute information of the target computing power task, and obtaining the matching historical computing power task and the set of historical computing power nodes that execute the historical computing power task according to the task attribute information; Determining the correlation information between the historical computing power task and the target computing power task; Determining the set of target computing power nodes corresponding to the target computing power task according to the correlation information and the set of historical computing power nodes; Scheduling the computing power nodes in the set of target computing power nodes to execute the target computing power task.

2. The computing power task execution method according to claim 1, wherein The step of determining the correlation information between the historical computing power task and the target computing power task includes: Determining the first state information representing the difference in data source locations according to the location data of the data sources corresponding to the historical computing power task and the target computing power task respectively; Determining the second state information representing the task difference according to the execution requirements of multiple subtasks included in the historical computing power task and the target computing power task respectively; Determining the third state information representing the difference in computing power types according to the computing power types corresponding to the historical computing power task and the target computing power task respectively; Wherein, the correlation information includes the first state information, the second state information, and the third state information.

3. The computing power task execution method according to claim 2, wherein The step of determining the set of target computing power nodes corresponding to the target computing power task according to the correlation information and the set of historical computing power nodes includes: When the third state information includes that the computing power type of the historical computing power task is the same as that of the target computing power task, determining the similarity between the historical computing power task and the target computing power task according to the first state information and the second state information; When the similarity is greater than the first preset threshold, determining the set of historical computing power nodes as the set of target computing power nodes; When the similarity is greater than the second preset threshold and less than or equal to the first preset threshold, adjusting the set of historical computing power nodes according to the first state information and the second state information to obtain the set of target computing power nodes; When the similarity is less than or equal to the second preset threshold, traversing all the computing power nodes in the computing power node cluster, and using the set of nodes that meet the preset computing power scheduling policy as the set of target computing power nodes; Wherein, the second preset threshold is less than the first preset threshold.

4. The computing power task execution method according to claim 3, wherein The first state information includes a first deviation value representing the difference in data source locations, and the second state information includes a second deviation value representing the difference in computing power processing capabilities of task requirements. The step of determining the similarity between the historical computing power task and the target computing power task according to the first state information and the second state information includes: According to the priority information between the data source location and the computing power processing capability corresponding to the target computing power task; Determining the first weight corresponding to the first deviation value and the second weight corresponding to the second deviation value according to the priority information; Calculating the similarity according to the first deviation value and the corresponding first weight, and the second deviation value and the corresponding second weight.

5. The computing power task execution method according to claim 2, wherein The step of determining the target computing power node set corresponding to the target computing power task according to the relevance information and the historical computing power node set includes: When the third status information indicates that the computing power type of the historical computing power task is inconsistent with the computing power type of the target computing power task, determine the similarity between the historical computing power task and the target computing power task according to the first status information and the second status information; When the similarity is greater than a first preset threshold, adjust the historical computing power node set according to the first status information, the second status information, and the third status information to obtain the target computing power node set; When the similarity is less than the first preset threshold, traverse all computing power nodes in the computing power node cluster, and use the node set that meets the preset computing power scheduling policy as the target computing power node set.

6. The computing power task execution method according to claim 2, characterized in that, The step of determining the first status information indicating the difference in data source locations according to the location data of the data sources corresponding to the historical computing power task and the target computing power task respectively includes: Determine the first central location of all data sources corresponding to the historical computing power task, and determine the second central location of all data sources corresponding to the target computing power task; Determine a first deviation value indicating the difference in data source locations according to the distance between the first central location and the second central location, and the first status information includes the first deviation value.

7. The computing power task execution method according to claim 2, wherein, The execution requirements include the total number of subtasks of the subtask and the proportion of the number of subtasks that allow parallel execution in the total number of tasks. The step of determining the second status information indicating the task difference according to the execution requirements of multiple subtasks included in the historical computing power task and the target computing power task respectively includes: Determine a first difference between the total number of tasks corresponding to the historical computing power task and the total number of tasks corresponding to the target computing power task, and determine a second difference between the proportion corresponding to the historical computing power task and the proportion corresponding to the target computing power task; Determine a second deviation value indicating the difference in computing power processing capabilities of the task requirements according to the first difference and the second difference, and the second status information includes the second deviation value.

8. The computing power task execution method according to any one of claims 1 to 7, characterized in that The task attribute information includes the task application scenario and the billing method. The step of obtaining a matching historical computing power task according to the task attribute information includes: Determine that the computing power task that meets the preset conditions and is executed before the current moment is the historical computing power task; The preset conditions include that the corresponding task application scenario and billing method are consistent with the task application scenario and billing method of the target computing power task.

9. A computing power scheduling device, characterized in that, The computing power scheduling device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. The computer program is configured to implement the steps of the computing power task execution method according to any one of claims 1 to 8.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the computing power task execution method according to any one of claims 1 to 8 are implemented.

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