Resource scheduling method, device, electronic device and storage medium

Abstract

The present application discloses a resource scheduling method, device, electronic device and storage medium, which relates to the field of communication technology, and is used to solve the problem of unfair allocation of edge cloud resources when edge computing is introduced into the 5G deterministic network in the existing technology, including: allocating initial network resources to the IoT device, the number of which is at least one; determining the task delay corresponding to the IoT device according to the initial network resources; determining the resource adjustment priority corresponding to the IoT device according to the task delay and the device local delay of the IoT device; adjusting the initial network resources according to the resource adjustment priority corresponding to the IoT device, and obtaining the final network resources allocated to the IoT device. The present application is used for fair resource scheduling of 5G deterministic networks.

Description

Technical Field

[0001] The present application relates to the field of communications, and in particular to a resource scheduling method, device, electronic device and storage medium. Background Art

[0002] Deterministic Networking (DetNet) is a technology for the Industrial Internet. The fifth generation mobile communication technology (5G) deterministic network is considered by the industry to be a revolutionary and fundamental productivity deterministic network. The 5G deterministic network introduces edge computing, which provides multi-access network connection capabilities at the edge of the network at the same time. By deploying close to users, it improves network provision efficiency, reduces latency, ensures user experience, improves service quality, and is more deterministic.

[0003] Most of the existing scheduling methods for 5G deterministic networks focus on optimizing the overall latency, aiming to optimize the overall average latency and minimize the overall latency, but do not study the fairness of device latency. That is, only the overall average latency is considered without considering the latency of a single device, resulting in unfair allocation of edge cloud resources and poor user experience quality. Summary of the invention

[0004] The present application provides a resource scheduling method, device, electronic device and storage medium, which can solve the problem of unfair edge cloud resource allocation when 5G deterministic networks introduce edge computing in the prior art.

[0005] In order to achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, the present application provides a resource scheduling method, the method comprising: allocating initial network resources to an IoT device, the number of which is at least one. Determining a task delay corresponding to the IoT device based on the initial network resources. Determining a resource adjustment priority corresponding to the IoT device based on the task delay and the device local delay of the IoT device. Adjusting the initial network resources based on the resource adjustment priority corresponding to the IoT device to obtain a final network resource allocated to the IoT device.

[0007] Based on the above technical solution, this application can accurately minimize the task delay of each IoT device in the IoT in the 5G deterministic network scenario, considering the fine-grained offloading scenario, so that each IoT device can achieve its own maximum delay effect benefit, thereby realizing the fair scheduling of IoT device tasks in edge cloud server resources and obtaining the optimal offloading solution for IoT devices, reducing the difference between the maximum task completion time and the minimum task completion time in the IoT, and thus ensuring the fair scheduling of network resources. Compared with the existing technology, it focuses more on the fairness of each IoT device during mobile offloading, and considers the delay optimization of a single device under the optimized average delay.

[0008] In a possible implementation, allocating initial network resources to the IoT device specifically includes: determining the priority of the subtasks corresponding to the critical path according to the critical path of the IoT device task, wherein the number of the subtasks is at least one, allocating network resources to the subtasks according to the priorities of the subtasks, and determining the sum of the network resources allocated to the subtasks as the initial network resources.

[0009] In a possible implementation, the resource adjustment priority corresponding to the IoT device is determined according to the task delay and the device local delay of the IoT device, specifically including: determining a priority reference ratio according to the task delay and the device local delay of the IoT device. Determining the resource adjustment priority of the IoT device according to the priority reference ratio.

[0010] In a possible implementation, the initial network resources are adjusted according to the resource adjustment priority corresponding to the IoT device to obtain the final network resources allocated to the IoT device, specifically including: constructing a benefit function according to the task delay, device local delay and preset reference delay of the IoT device. Corresponding to the adjustment of the initial network resources, the benefit function is iteratively calculated;. When the IoT device satisfies the Nash equilibrium state, the optimal resource scheduling strategy is determined; wherein the optimal resource scheduling strategy is the resource scheduling strategy corresponding to the maximum benefit function. The network resources corresponding to the IoT device allocated in the optimal resource scheduling strategy are determined as the final network resources.

[0011] In one possible implementation, the profit function satisfies the following formula:

[0012]

[0013] Among them, C is the value of the benefit function, T0 is the local device delay of the IoT device, T1 is the task delay, and T r is the preset reference delay.

[0014] In a second aspect, the present application provides a resource scheduling device, which includes: a processing unit. The processing unit is used to allocate initial network resources to an Internet of Things device, and the number of Internet of Things devices is at least one. The processing unit is also used to determine the task delay corresponding to the Internet of Things device based on the initial network resources. The processing unit is also used to determine the resource adjustment priority corresponding to the Internet of Things device based on the task delay and the device local delay of the Internet of Things device. The processing unit is also used to adjust the initial network resources according to the resource adjustment priority corresponding to the Internet of Things device, and obtain the final network resources allocated to the Internet of Things device.

[0015] In a possible implementation, the processing unit is further configured to determine the priority of the subtasks corresponding to the critical path according to the critical path of the IoT device task, wherein the number of the subtasks is at least one. The processing unit is further configured to allocate network resources to the subtasks according to the priorities of the subtasks, and determine the sum of the network resources allocated to the subtasks as the initial network resources.

[0016] In a possible implementation, the processing unit is further configured to determine a priority reference ratio according to the task delay and the device local delay of the IoT device. The processing unit is further configured to determine a resource adjustment priority of the IoT device according to the priority reference ratio.

[0017] In a possible implementation, the processing unit is further used to construct a benefit function according to the task delay, the local delay of the device and the preset reference delay of the IoT device. The processing unit is further used to iteratively calculate the benefit function corresponding to the adjustment of the initial network resources. The processing unit is further used to determine the optimal resource scheduling strategy when the IoT device satisfies the Nash equilibrium state; wherein the optimal resource scheduling strategy is the resource scheduling strategy corresponding to the maximum benefit function. The processing unit is further used to determine the network resources corresponding to the IoT device allocated in the optimal resource scheduling strategy as the final network resources.

[0018] In one possible implementation, the profit function satisfies the following formula:

[0019]

[0020] Among them, C is the value of the benefit function, T0 is the local device delay of the IoT device, T1 is the task delay, and T r is the preset reference delay.

[0021] In addition, the technical effects of the resource scheduling device described in the second aspect can refer to the technical effects of the resource scheduling method described in the first aspect above, and will not be repeated here.

[0022] In a third aspect, the present application provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by an electronic device of the present application, enable the electronic device to execute a resource scheduling method as described in the first aspect and any possible implementation of the first aspect.

[0023] In a fourth aspect, the present application provides an electronic device, comprising: a processor and a memory; wherein the memory is used to store one or more programs, and the one or more programs include computer execution instructions. When the electronic device is running, the processor executes the computer execution instructions stored in the memory to enable the electronic device to perform the resource scheduling method described in the first aspect and any possible implementation of the first aspect.

[0024] In a fifth aspect, the present application provides a computer program product comprising instructions, which, when executed on a computer, enables the electronic device of the present application to execute the resource scheduling method as described in the first aspect and any possible implementation manner of the first aspect.

[0025] In a sixth aspect, the present application provides a chip system, which is applied to a resource scheduling device; the chip system includes one or more interface circuits and one or more processors. The interface circuit and the processor are interconnected through a line; the interface circuit is used to receive a signal from the memory of the resource scheduling device and send the signal to the processor, wherein the signal includes a computer instruction stored in the memory. When the processor executes the computer instruction, the resource scheduling device executes the resource scheduling method as described in the first aspect and any possible design thereof.

[0026] In this application, the name of the resource scheduling device does not limit the device or functional unit itself. In actual implementation, these devices or functional units may appear with other names. As long as the functions of each device or functional unit are similar to those of this application, they are within the scope of the claims of this application and their equivalent technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic diagram of a resource scheduling method provided in an embodiment of the present application;

[0028] Figure 2 A schematic diagram of the structure of a resource scheduling device provided in an embodiment of the present application;

[0029] Figure 3 A schematic diagram of the structure of another resource scheduling device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0031] In this article, the character " / " generally indicates that the objects before and after are in an "or" relationship. For example, A / B can be understood as A or B.

[0032] The terms "first" and "second" in the specification and claims of this application are used to distinguish different objects rather than to describe a specific order of objects. For example, a first edge service node and a second edge service node are used to distinguish different edge service nodes rather than to describe a characteristic order of edge service nodes.

[0033] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0034] In addition, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present concepts in a specific way.

[0035] Deterministic networking is a technology for the industrial Internet proposed by the Internet Engineering Task Force (IETF). The 5G deterministic network is considered by the industry to be a revolutionary and fundamental productivity deterministic network. The 5G deterministic network introduces edge computing, which provides multi-access network connection capabilities at the edge of the network at the same time. By deploying close to users, it improves network provision efficiency, reduces latency, ensures user experience, improves service quality, and is more deterministic.

[0036] 5G deterministic networks are currently mainly used to solve problems such as the transmission of computing-intensive and latency-sensitive tasks, and have the advantages of low latency and high reliability. Furthermore, the integration of deterministic networks and edge cloud computing mainly solves the problem of effective integration of communication and computing. Based on the communication connection of deterministic networks, it can achieve flexible matching and deterministic scheduling of integrated edge networks, improve network and computing efficiency and service quality. In addition, in the deterministic network edge cloud scenario, the integrated edge devices provide computing resources at the edge of the deterministic network to provide computing for resource-constrained IoT devices, and are more widely used.

[0037] Most of the existing scheduling methods for 5G deterministic networks focus on optimizing the overall latency, aiming to optimize the overall average latency and minimize the overall latency, but do not study the fairness of device latency. That is, only the overall average latency is considered without considering the latency of a single device, resulting in unfair allocation of edge cloud resources and poor user experience quality.

[0038] In response to the scheduling problem of 5G deterministic networks, a solution is disclosed in the prior art: by scheduling subtasks of IoT devices, it is chosen whether to upload the subtasks to the edge server or execute them locally. After all IoT devices are scheduled, the total delay of the IoT devices is calculated. The ultimate goal of the algorithm is to minimize the total delay of all devices.

[0039] The above existing solutions have the following disadvantages: the solution focuses on the optimization of the overall delay, aiming to optimize the overall average delay and minimize the overall delay, but does not study the fairness of device delay. That is, only the overall average delay is considered without considering the delay of a single device, resulting in unfair allocation of edge cloud resources and poor user experience quality.

[0040] In order to solve the problems existing in the above-mentioned existing solutions, the embodiments of the present application provide a resource scheduling method and device. Among them, the resource scheduling device first determines the priority of the subtask of the Internet of Things device according to the critical path of the device task of each Internet of Things device in the Internet of Things, and allocates initial network resources to the device accordingly. After that, the resource scheduling device determines the initial network resources, determines the task delay corresponding to the Internet of Things device, and constructs a profit function according to the task delay and the device local delay of the Internet of Things device. Furthermore, the resource scheduling device iteratively calculates the constructed profit function corresponding to the adjustment of the initial network resources. When all devices in the Internet of Things satisfy the Nash equilibrium state, the resource adjustment strategy when the profit function is maximized is determined as the optimal resource adjustment strategy. The network resources corresponding to the Internet of Things devices in this optimal resource adjustment strategy are the final network resources allocated to the Internet of Things devices. Therefore, in the 5G deterministic network scenario, the embodiment of the present application accurately minimizes the task delay of each IoT device in the IoT, considering the fine-grained unloading scenario, so that each IoT device can achieve its own maximum delay effect benefit, thereby realizing the fair scheduling of IoT device tasks in edge cloud server resources and obtaining the optimal unloading solution for IoT devices, reducing the difference between the maximum task completion time and the minimum task completion time in the IoT, and thus ensuring the fair scheduling of network resources. Compared with the prior art, it focuses more on the fairness of each IoT device during mobile unloading, and considers the delay optimization of a single device under the optimized average delay.

[0041] In the resource scheduling method provided in the present application, the execution subject is a resource scheduling device. The resource scheduling device can be an electronic device (such as a computer terminal, a server), a processor in an electronic device, a control module for resource scheduling in an electronic device, or a client for resource scheduling in an electronic device.

[0042] In order to solve the problem of unfair edge cloud resource allocation when 5G deterministic networks introduce edge computing in the existing technical solutions, this application provides a resource scheduling method. Figure 1 As shown, the resource scheduling method provided by this application includes the following steps:

[0043] For example, this embodiment is aimed at IoT devices that are sensitive to latency. Specifically, assume that there are N IoT devices, where N is a positive integer, and each IoT device includes a group of j subtasks V i =(1,2,3,...,j). There are M mobile edge servers in the 5G deterministic network, where M is a positive integer. The mobile edge server provides network resources for IoT devices, and the IoT device terminals unload and transmit data to the mobile edge cloud server.

[0044] S101. The resource scheduling device allocates initial network resources to the IoT device.

[0045] Among them, the number of IoT devices is at least one.

[0046] Exemplarily, the resource scheduling device may allocate initial network resources to the IoT device by specifically including the following steps S1011-S1013:

[0047] S1011. The resource scheduling device determines the priority of the subtask corresponding to the critical path according to the critical path of the IoT device task.

[0048] The number of the subtasks is at least one. It should be noted that determining the priority of the subtasks corresponding to the critical path according to the critical path of the device task of the IoT device is a prior art, and this application will not elaborate on it here.

[0049] S1012: The resource scheduling device allocates network resources to the subtask according to the priority of the subtask.

[0050] It is understandable that after allocating network resources to the subtasks according to their priorities, each subtask has a corresponding subtask delay.

[0051] S1013: The resource scheduling device determines the sum of the network resources allocated to the subtasks as the initial network resources.

[0052] It should be noted that the initial network resources here include all network resources scheduled by the resource scheduling device for the subtasks of the IoT device task, and the network resources may be adjusted in subsequent processes.

[0053] S102: The resource scheduling device determines the task delay corresponding to the IoT device according to the initial network resources.

[0054] Optionally, the resource scheduling device determines the task delays of the Internet of Things devices corresponding to these subtasks based on the subtask delay of each subtask.

[0055] For example, if the task of an IoT device includes 3 subtasks, namely subtask a, subtask b, and subtask c, their delays are 5ms, 10ms, and 8ms respectively, and subtask b can only be executed after subtask a is completed, and subtask c can only be executed after subtask b is completed. Then the task delay of the IoT device is 5+10+8=23ms. It should be noted that this is only an example of the situation where the task of the IoT device may contain subtasks and a possible calculation of the corresponding task delay. The task delay calculation of a specific IoT device can be determined according to the task type of the IoT in actual application and the subtask type included, and the embodiment of the present application does not limit this.

[0056] S103. The resource scheduling device determines the resource adjustment priority corresponding to the IoT device according to the task delay and the device local delay of the IoT device.

[0057] The local delay of the IoT device is the initial delay of the device when resources are allocated. Specifically, the size of the local delay depends on the IoT device itself.

[0058] Optionally, the resource scheduling device determines a priority reference ratio based on the task delay and the device local delay of the IoT device.

[0059] Specifically, the resource scheduling device calculates the difference between the device local delay and the task delay of the IoT device. Thereafter, the resource scheduling device calculates the ratio of the difference to the device local delay and determines the ratio as the priority reference ratio.

[0060] It should be noted that the larger the priority reference ratio of an IoT device, the lower the resource adjustment priority of the IoT device; the smaller the priority reference ratio of an IoT device, the higher the resource adjustment priority of the IoT device. This is because, the larger the priority reference ratio of an IoT device, the more it means that after the resource allocation in step S101, the latency of the IoT has been significantly improved, and the adjustment priority of the IoT device is lower at this time. Similarly, the smaller the priority reference ratio of an IoT device, the more it means that after the resource allocation in step S101, the latency of the IoT has not been significantly improved, and the adjustment priority of the IoT device is higher at this time.

[0061] S104: The resource scheduling device adjusts the initial network resources according to the resource adjustment priority corresponding to the IoT device to obtain the final network resources allocated to the IoT device.

[0062] Optionally, after determining the task delay of the IoT device, the resource scheduling device adjusts the initial network resources according to the resource adjustment priority corresponding to the IoT device, specifically including the following steps S1041-S1044:

[0063] S1041. The resource scheduling device constructs a profit function according to the task delay, local delay and preset reference delay of the IoT device.

[0064] Among them, the preset reference delay can be pre-set by the resource scheduling device, or it can be input into the resource scheduling device manually. It can be understood that the preset reference delay of the IoT device is the expected optimal delay of the IoT device initially judged by the resource scheduling device. However, due to the network fluctuations that may exist in the IoT in actual applications, namely resource scheduling and other problems, the actual optimal delay may change. The purpose of setting the preset reference delay is to provide the resource scheduling device with a reference when judging the optimal delay of the IoT device (that is, the equilibrium delay in the following text), so as to facilitate the subsequent iterative calculation of the benefit function.

[0065] Exemplarily, the preset reference delay may be set to 10 ms, which is not limited in the embodiments of the present application.

[0066] Exemplarily, the profit function satisfies the following formula:

[0067]

[0068] Among them, C is the value of the profit function, T0 is the initial delay of the IoT device, T1 is the task delay, T r is the preset reference delay.

[0069] S1042: The resource scheduling device iteratively calculates the benefit function in response to the adjustment made to the initial network resources.

[0070] It is understandable that the resource scheduling device regards each IoT device as a competitor and uses the idea of ​​game theory to coordinate the competition among all IoT devices. In the unloading scheme based on game theory between multiple IoT devices and edge cloud computing servers, in each iterative calculation, the IoT device with the highest resource adjustment priority is selected to make allocation decisions on the network resources of other IoT devices, and a local optimal decision is obtained. Until all IoT devices in the deterministic network system reach a Nash equilibrium state. In the Nash equilibrium state, each IoT device achieves its own maximum benefit, thereby realizing fair scheduling of IoT device tasks under the resources of the edge cloud server.

[0071] It is necessary to support that the method for all IoT devices in a deterministic network system to reach a Nash equilibrium state is a prior art and will not be described in detail in this application.

[0072] S1043: When the IoT device satisfies the Nash equilibrium state, the resource scheduling device determines the optimal resource scheduling strategy.

[0073] Optionally, the optimal resource scheduling strategy is the resource scheduling strategy corresponding to when the benefit function is maximized.

[0074] S1044. The resource scheduling device determines the network resources corresponding to the IoT devices in the optimal resource scheduling strategy as the final network resources.

[0075] Based on the above technical solution, in the embodiment of the present application, the resource scheduling device first determines the priority of the subtask of the IoT device according to the critical path of the device task of each IoT device in the IoT, and allocates initial network resources to the device accordingly. After that, the resource scheduling device determines the initial network resources, determines the task delay corresponding to the IoT device, and constructs a benefit function according to the task delay and the device local delay of the IoT device. Furthermore, the resource scheduling device iteratively calculates the constructed benefit function corresponding to the adjustment of the initial network resources. When all devices in the IoT satisfy the Nash equilibrium state, the resource adjustment strategy when the benefit function is maximized is determined as the optimal resource adjustment strategy. The network resources allocated to the IoT device in this optimal resource adjustment strategy are the final network resources allocated to the IoT device. Therefore, in the 5G deterministic network scenario, the embodiment of the present application accurately minimizes the task delay of each IoT device in the IoT, considering the fine-grained unloading scenario, so that each IoT device can achieve its own maximum delay effect benefit, thereby realizing the fair scheduling of IoT device tasks in edge cloud server resources and obtaining the optimal unloading solution for IoT devices, reducing the difference between the maximum task completion time and the minimum task completion time in the IoT, and thus ensuring the fair scheduling of network resources. Compared with the prior art, it focuses more on the fairness of each IoT device during mobile unloading, and considers the delay optimization of a single device under the optimized average delay.

[0076] The embodiment of the present application can divide the resource scheduling device into functional modules or functional units according to the above method example. For example, each functional module or functional unit can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of software functional modules or functional units. Among them, the division of modules or units in the embodiment of the present application is schematic, which is only a logical function division. There may be other division methods in actual implementation.

[0077] For example, Figure 2 2 is a schematic diagram of a possible structure of a resource scheduling device according to an embodiment of the present application. The resource scheduling device 200 includes: a processing unit 201 .

[0078] The processing unit 201 is used to allocate initial network resources to the IoT device.

[0079] The processing unit 201 is further configured to determine a task delay corresponding to the IoT device according to the initial network resources.

[0080] The processing unit 201 is further configured to determine a resource adjustment priority corresponding to the IoT device according to the task delay and the device local delay of the IoT device.

[0081] The processing unit 201 is further configured to adjust the initial network resources according to the resource adjustment priority corresponding to the IoT device, and obtain the final network resources allocated to the IoT device.

[0082] Optionally, the processing unit 201 is further configured to determine the priority of the subtask corresponding to the critical path according to the critical path of the IoT device task.

[0083] Optionally, the processing unit 201 is further configured to allocate network resources to the subtasks according to the priorities of the subtasks, and determine the sum of the network resources allocated to the subtasks as the initial network resources.

[0084] Optionally, the processing unit 201 is further used to determine a priority reference ratio according to the task delay and the device local delay of the Internet of Things device.

[0085] Optionally, the processing unit 201 is further configured to determine a resource adjustment priority of the IoT device according to a priority reference ratio.

[0086] Optionally, the processing unit 201 is further used to construct a profit function according to the task delay of the IoT device, the local delay of the device and the preset reference delay.

[0087] Optionally, the processing unit 201 is further configured to iteratively calculate the benefit function corresponding to the adjustment made to the initial network resources.

[0088] Optionally, the processing unit 201 is further configured to determine an optimal resource scheduling strategy when the IoT device satisfies a Nash equilibrium state.

[0089] Optionally, the processing unit 201 is further configured to determine the network resources allocated to the IoT device in the optimal resource scheduling strategy as the final network resources.

[0090] Optionally, the resource scheduling device 200 may further include a storage unit ( Figure 2 The storage unit stores a program or instruction. When the processing unit 201 executes the program or instruction, the resource scheduling device can execute the resource scheduling method described in the above method embodiment.

[0091] also, Figure 2 The technical effects of the resource scheduling device can refer to the technical effects of the resource scheduling method described in the above embodiment, which will not be repeated here.

[0092] For example, Figure 3 FIG. 2 is another possible structural diagram of the resource scheduling device involved in the above embodiment. Figure 3 As shown, the resource scheduling device 300 includes: a processor 302 .

[0093] The processor 302 is used to control and manage the actions of the resource scheduling device, for example, to execute the steps executed by the above-mentioned processing unit 201, and / or to execute other processes of the technical solution described in this document.

[0094] The processor 302 may be a processor that implements or executes various exemplary logic blocks, modules, and circuits described in conjunction with the contents of this application. The processor may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the contents disclosed in this application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0095] Optionally, the resource scheduling device 300 may further include a communication interface 303, a memory 301 and a bus 304. The communication interface 303 is used to support the communication between the resource scheduling device 300 and other network entities. The memory 301 is used to store program codes and data of the resource scheduling device.

[0096] Among them, the memory 301 can be a memory in the resource scheduling device, which can include a volatile memory, such as a random access memory; the memory can also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk or a solid-state drive; the memory can also include a combination of the above types of memory.

[0097] The bus 304 may be an Extended Industry Standard Architecture (EISA) bus, etc. The bus 304 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0098] Through the description of the above implementation methods, those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0099] An embodiment of the present application provides a computer program product including instructions. When the computer program product is run on an electronic device of the present application, the computer is enabled to execute the resource scheduling method described in the above method embodiment.

[0100] An embodiment of the present application also provides a computer-readable storage medium, in which instructions are stored. When a computer executes the instructions, the electronic device of the present application executes each step performed by the resource scheduling device in the method flow shown in the above method embodiment.

[0101] Among them, the computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, and a hard disk. Random Access Memory (RAM), Read-Only Memory (ROM), Erasable Programmable Read Only Memory (EPROM), registers, hard disks, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any other form of computer-readable storage medium in a suitable combination of the above, or numerical values ​​in the art. An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an Application Specific Integrated Circuit (ASIC). In the embodiments of the present application, a computer-readable storage medium may be any tangible medium that contains or stores a program, which may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0102] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A resource scheduling method, characterized in that: The method comprises: Determine the priority of the subtask corresponding to the critical path according to the critical path of the IoT device task; wherein the number of the subtask is at least one, and the number of the IoT device is at least one; Allocate network resources to the subtasks according to their priorities, and determine the sum of the network resources allocated to the subtasks as initial network resources; Determining a task delay corresponding to the IoT device according to the initial network resources; Determining a priority reference ratio according to the task delay and the device local delay of the IoT device; Determining the resource adjustment priority of the IoT device according to the priority reference ratio; A profit function is constructed according to the task delay of the IoT device, the local delay of the device and the preset reference delay; the profit function satisfies the following formula: Where C is the value of the profit function, T0 is the local delay of the IoT device, T1 is the task delay, T r is the preset reference delay; Iteratively calculating the revenue function corresponding to the adjustment made to the initial network resources; When the IoT device satisfies the Nash equilibrium state, determining an optimal resource scheduling strategy; wherein the optimal resource scheduling strategy is a resource scheduling strategy corresponding to when the benefit function is maximized; The network resources corresponding to the IoT device allocated in the optimal resource scheduling strategy are determined as the final network resources.

2. A resource scheduling device, characterized in that: The resource scheduling device comprises: a processing unit; The processing unit is used to allocate initial network resources to an Internet of Things device, where the number of the Internet of Things device is at least one; The processing unit is further used to determine the task delay corresponding to the Internet of Things device according to the initial network resources; The processing unit is further configured to determine a resource adjustment priority corresponding to the IoT device according to the task delay and the device local delay of the IoT device; The processing unit is further configured to adjust the initial network resources according to the resource adjustment priority corresponding to the IoT device, and obtain the final network resources allocated to the IoT device; The processing unit is further used to determine the priority of the subtask corresponding to the critical path according to the critical path of the IoT device task; wherein the number of the subtask is at least one; The processing unit is further configured to allocate network resources to the subtasks according to the priorities of the subtasks, and determine the sum of the network resources allocated to the subtasks as the initial network resources; The processing unit is further used to determine a priority reference ratio according to the task delay and the device local delay of the IoT device; The processing unit is further configured to determine the resource adjustment priority of the IoT device according to the priority reference ratio; The processing unit is further used to construct a profit function according to the task delay of the IoT device, the local delay of the device and the preset reference delay; The processing unit is further configured to iteratively calculate the benefit function corresponding to the adjustment made to the initial network resources; The processing unit is further used to determine an optimal resource scheduling strategy when the Internet of Things device satisfies a Nash equilibrium state; wherein the optimal resource scheduling strategy is a resource scheduling strategy corresponding to when the benefit function is maximized; The processing unit is further configured to determine the network resources correspondingly allocated to the IoT device in the optimal resource scheduling strategy as the final network resources; The profit function satisfies the following formula: Where C is the value of the profit function, T0 is the local delay of the IoT device, T1 is the task delay, T r is the preset reference delay.

3. An electronic device, characterized in that: include: A processor and a memory; wherein the memory is used to store computer-executable instructions, and when the electronic device is running, the processor executes the computer-executable instructions stored in the memory to enable the electronic device to execute the resource scheduling method described in claim 1.

4. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes instructions. When the instructions in the computer-readable storage medium are executed by an electronic device, the electronic device is enabled to execute the resource scheduling method as claimed in claim 1.

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