A computing power network resource allocation method and device, electronic equipment and medium

Abstract

The application provides a computing power network resource allocation method and device, electronic equipment and medium. The method comprises the following steps: obtaining the computing power network resource utilization rule of a target network, wherein the computing power network resource utilization rule of the target network is obtained by mapping the computing power network resource information of the target network and the service time of the user of the target network; obtaining the computing power network price model of the target network according to the computing power network resource utilization rule of the target network and the user distribution probability of the target network; constructing the target utility function according to the computing power network resource utilization rule of the target network and the computing power network price model of the target network; and obtaining the resource allocation result of the target network according to the target utility function. In the application, the utility function of the computing power network is defined by combining the computing power network resource utilization rule and the computing power network price model, and differential services are provided in different time periods and for different users, so that the joint resource allocation of computing power and bandwidth is realized, and the network performance is improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of computing power network, in particular to a computing power network resource allocation method and device, electronic equipment and medium. BACKGROUND

[0002] The current network generally uses Transmission Control Protocol (TCP) statistical multiplexing mode to allocate network resources, that is, different resources are allocated for service according to different polling schemes set according to business or user priority, so as to ensure communication quality and meet the needs of multiple parties.

[0003] However, the access rate of modern devices to the network is high and changes rapidly, the existing open-loop service mode does not have the ability to adjust in time, and often falls into a local congestion state, which cannot achieve overall optimization; and user needs are diverse and difficult to know, and the natural information asymmetry makes it difficult for network managers to capture demand state adjustment supply and demand configuration, resulting in low network device resource utilization. SUMMARY

[0004] Embodiments of the present application provide a computing power network resource allocation method, device, electronic equipment and medium to solve the problem that existing network resources cannot be better allocated.

[0005] To solve the above technical problems, the present application is implemented as follows:

[0006] In a first aspect, embodiments of the present application provide a computing power network resource allocation method, comprising:

[0007] Obtaining the computing power network resource utilization rule of the target network, wherein the computing power network resource utilization rule of the target network is obtained by mapping the computing power network resource information of the target network and the service time of the users of the target network;

[0008] Obtaining the computing power network price model of the target network according to the computing power network resource utilization rule of the target network;

[0009] Constructing a target utility function according to the computing power network resource utilization rule of the target network and the computing power network price model of the target network;

[0010] Obtaining the resource allocation result of the target network according to the target utility function.

[0011] Optionally, the computing power network resource information includes bandwidth resources and computing power resources;

[0012] The bandwidth resource includes at least one of a minimum bandwidth requirement and a maximum bandwidth requirement, and the computing power resource includes at least one of a minimum computing power price and a maximum computing power price; the service time of the user of the target network includes a service start time and a service end time of the user;

[0013] The computing power network resource utilization rule of the target network is that the minimum bandwidth requirement, the maximum bandwidth requirement, the minimum computing power price and the maximum computing power price are mapped to the service start time and the service end time of the user, and a constraint condition is set for the computing power network resource information and the service time of the user of the target network.

[0014] Optionally, the computing power network price model of the target network is determined according to the computing power network resource utilization rule of the target network and the user distribution probability of users with different priorities at different moments.

[0015] Optionally, the target utility function is the computing power network price model of the target network minus the product of the computing power network resource utilization rule of the target network and the user distribution probability of users with different priorities at different moments with a weight.

[0016] Optionally, the target utility function further includes user satisfaction.

[0017] The target utility function is the computing power network price model of the target network minus the product of the computing power network resource utilization rule of the target network and the user distribution probability of users with different priorities at different moments with a weight, plus a user satisfaction function.

[0018] Optionally, the resource allocation result of the target network is obtained according to the target utility function, including:

[0019] The corresponding bandwidth resource and computing power resource in the computing power network resource utilization rule of the target network and the computing power network price model of the target network at the maximum value of the target utility function are taken as the resource allocation result of the target network.

[0020] Optionally, the method further includes:

[0021] The target utility function and the resource allocation result of the target network are input into a target neural network, and the target utility function is iteratively optimized according to a pre-set constraint condition and an iteration condition.

[0022] When the pre-set iteration condition is met, the optimization is stopped, and an optimal resource allocation result of the target network is obtained.

[0023] In a second aspect, an embodiment of the present application provides a computing power network resource allocation device, including:

[0024] obtain a computing power network resource utilization rule of the target network, wherein the computing power network resource utilization rule of the target network is obtained by mapping computing power network resource information of the target network and service time of a user of the target network;

[0025] a first processing module configured to obtain a computing power network price model of the target network according to the computing power network resource utilization rule of the target network;

[0026] a second processing module configured to construct a target utility function according to the computing power network resource utilization rule of the target network and the computing power network price model of the target network;

[0027] a third processing module configured to obtain a resource allocation result of the target network according to the target utility function.

[0028] Optionally, the computing power network resource information includes bandwidth resource and computing power resource.

[0029] The bandwidth resource includes at least one of minimum bandwidth demand and maximum bandwidth demand, and the computing power resource includes at least one of minimum computing power price and maximum computing power price; the service time of the user of the target network includes service start time of the user and service end time of the user.

[0030] The computing power network resource utilization rule of the target network is that the minimum bandwidth demand, the maximum bandwidth demand, the minimum computing power price and the maximum computing power price are mapped with the service start time of the user and the service end time of the user, and a constraint condition is set for the computing power network resource information and the service time of the user of the target network.

[0031] Optionally, the computing power network price model of the target network is determined according to the computing power network resource utilization rule of the target network and the user distribution probability of users of different priorities at different time points.

[0032] Optionally, the target utility function is the computing power network price model of the target network minus the product of the computing power network resource utilization rule of the target network and the user distribution probability of users of different priorities at different time points with an accompanying weight.

[0033] Optionally, the target utility function further includes user satisfaction.

[0034] The target utility function is the computing power network price model of the target network minus the product of the computing power network resource utilization rule of the target network and the user distribution probability of users of different priorities at different time points with an accompanying weight, and plus a user satisfaction function.

[0035] Optionally, the third processing module comprises:

[0036] The processing sub-module is configured to take the corresponding bandwidth resource and computing power resource in the target network's computing power network resource utilization rule and the target network's computing power network price model at the maximum value of the target utility function as the resource allocation result of the target network.

[0037] Optionally, the method further comprises:

[0038] The optimization module is configured to input the target utility function and the resource allocation result of the target network into a target neural network, and iteratively optimize the target utility function according to a pre-set constraint condition and an iteration condition; and stop the optimization when the pre-set iteration condition is met, to obtain an optimal resource allocation result of the target network.

[0039] In a third aspect, an electronic device is provided, which includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, and the program or instructions, when executed by the processor, implement the steps in the computing power network resource allocation method according to any one of the first aspect.

[0040] In a fourth aspect, a readable storage medium is provided, which stores a program or instructions, and the program or instructions, when executed by a processor, implement the steps in the computing power network resource allocation method according to any one of the first aspect.

[0041] In the present application, by combining the computing power network resource utilization rule and the computing power network price model to define the utility function of the computing power network, differential services are provided for different time periods and different users, the joint resource allocation of computing power and bandwidth is realized, the network performance is improved, and the problems of the existing network devices, such as the lack of timely adjustment capability, the inability to achieve overall optimization, the diverse user needs and the difficulty in obtaining the user needs, and the low utilization rate of network device resources, are solved. BRIEF DESCRIPTION OF DRAWINGS

[0042] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are included to provide a description of the preferred embodiments and are not intended to limit the scope of the application. Moreover, the same reference numerals are used throughout the same figures. In the drawings:

[0043] Figure 1 is a flowchart of a computing power network resource allocation method provided by an embodiment of the present application;

[0044] Figure 2 is an algorithm flowchart of a computing power network resource allocation method provided by an embodiment of the present application;

[0045] Figure 3 is a structural schematic diagram of a computing power network resource allocation device provided by an embodiment of the present application.

[0046] Figure 4 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0048] For reference Figure 1 The embodiment of the present application provides a computing power network resource allocation method, which comprises the following steps:

[0049] Step 11: obtaining the computing power network resource utilization rule of the target network, wherein the computing power network resource utilization rule of the target network is obtained by mapping the computing power network resource information of the target network and the service time of the user of the target network.

[0050] In the embodiment of the present application, the computing power network resource information comprises bandwidth resource and computing power resource; the bandwidth resource comprises at least one of minimum bandwidth demand and maximum bandwidth demand, and the computing power resource comprises at least one of minimum computing power price and maximum computing power price; the service time of the user of the target network comprises service start time of the user and service end time of the user.

[0051] In the embodiment of the present application, the computing power network resource utilization rule R k is obtained by mapping the computing power network resource information of the target network and the service time of the user of the target network. k =(b min ,b max ,P min ,P max )@(T s ,T e ), wherein b min is minimum bandwidth demand, b max is maximum bandwidth demand, P min is minimum computing power price, P max is maximum computing power price, T s represents service start time of the user, and T erepresents the service end time of the user; wherein the service time of the user of the target network and the computing power network resource information can be set with a constraint condition according to actual needs, for example: the service start time T s of the user e needs to meet: ∪{T s ,T e}=24h; that is, the service time of the user is within 24 hours, and the service start time T s of the user e and the service end time T min of the user do not coincide;

[0052] The bandwidth and computing power multiplexing factor can also be defined according to actual needs to constrain the total amount of network computing power and bandwidth, for example: assuming α is the bandwidth and / or computing power multiplexing factor, in the case of a bandwidth-limited network, the optimization problem also needs to meet: ∪{b min ,αb max}=(B min ,B max ), wherein B min is the minimum bandwidth requirement of the entire network, and B max is the maximum bandwidth requirement of the entire network; for the case of a computing power-limited network, similarly, the optimization problem also needs to meet wherein, is the minimum computing power requirement of the entire network, is the maximum computing power requirement of the entire network, by defining the bandwidth and computing power multiplexing factor, the total amount of network computing power and bandwidth is constrained, the time complexity is further reduced, the joint resource allocation of computing power and bandwidth is realized, and the network performance is improved.

[0053] Step 12: obtaining a computing power network price model of the target network according to the computing power network resource utilization rule of the target network;

[0054] In the embodiment of the application, optionally, the computing power network price model of the target network is determined according to the computing power network resource utilization rule of the target network, and the computing power network price model C ik is represented as C ik (T i ,R k ), wherein R k is the computing power network resource utilization rule, and T i is any i moment, by providing differentiated services in different time periods and for different users, the joint resource allocation of computing power and bandwidth is realized, and the network performance is improved.

[0055] Step 13: constructing a target utility function according to the computing power network resource utilization rule of the target network and the computing power network price model of the target network;

[0056] In the embodiment of the application, the network resource can be allocated and optimized with the target utility function as the target under the condition that the computing power network resource utilization rule of the target network and the computing power network price model of the target network are obtained, and the target utility function is:

[0057]

[0058] wherein, β is a weight coefficient; p k is user satisfaction; U j is a business type, j is any business type, p ikj is the number distribution probability of different business types, is the bandwidth resource allocated to the target user; P k j is the computing power resource allocated to the target user; P TOTAL is the total amount of computing power; B is the total amount of bandwidth; the target utility function needs to meet the minimum computing power and bandwidth resource requirement, and the total resource cannot exceed the total amount of computing power and the total amount of bandwidth that the operator can provide; wherein, the weight coefficient β and the user satisfaction p k are related to the bandwidth and computing power resources allocated to the target user by the network, when the bandwidth and computing power resources allocated to the target user by the network are large, the user satisfaction also increases, and at the same time, the cost of the network increases, therefore, the target utility function can take into account the fairness among network users, and is no longer the traditional water injection optimization, the utility function of the computing power network is defined by taking into account the system cost and user satisfaction, the network performance is comprehensively improved, the utility function of the computing power network is defined by combining the computing power network resource utilization rule and the computing power network price model, differential services are provided for different time periods and different users, the joint resource allocation of computing power and bandwidth is realized, and the network performance is improved.

[0059] Step 14: obtaining the resource allocation result of the target network according to the target utility function.

[0060] In the embodiment of the application, the corresponding bandwidth resource and computing power resource in the computing power network resource utilization rule of the target network and the computing power network price model of the target network under the maximum value of the target utility function are taken as the resource allocation result of the target network.

[0061] In the embodiment of the present application, the utility function of the computing power network is defined by combining the computing power network resource utilization rule and the computing power network price model, differentiated services are provided in time periods and for different users, joint resource allocation of computing power and bandwidth is realized, network performance is improved, and the problems of low network device resource utilization rate caused by the inability to adjust in time, the inability to achieve overall optimization, the diversity of user demand and the difficulty to obtain the user demand are solved.

[0062] In the embodiment of the present application, optionally, the target utility function is the product of the computing power network price model of the target network, the computing power network resource utilization rule of the target network and the user distribution probability of users of different priorities at different time points with a weight.

[0063] In the embodiment of the present application, optionally, the target utility function further includes user satisfaction.

[0064] The target utility function is the product of the computing power network price model of the target network, the computing power network resource utilization rule of the target network and the user distribution probability of users of different priorities at different time points with a weight, and the user satisfaction function.

[0065] In the embodiment of the present application, the network resources can be allocated and optimized with the target utility function as the target under the condition of obtaining the computing power network resource utilization rule of the target network and the computing power network price model of the target network, but not limited to.

[0066]

[0067] Wherein, β is a weight coefficient; p k is user satisfaction; U j is a business type, p ikj is the number distribution probability of different business types, is the bandwidth resource allocated to the target user; is the computing power resource allocated to the target user; P TOTAL is the total amount of computing power; B is the total amount of bandwidth; the target utility function needs to meet the minimum computing power and bandwidth resource requirement, and the total resource cannot exceed the total amount of computing power and the total amount of bandwidth that the operator can provide; wherein, the weight coefficient β and the user satisfaction p k are related to the bandwidth and computing power resources allocated to the target user by the network, when the bandwidth and computing power resources allocated to the target user by the network are large, the user satisfaction also increases, and at the same time, the cost of the network increases, therefore, the target utility function can take into account the fairness among network users, provide differentiated services in time periods and for different users, realize joint resource allocation of computing power and bandwidth, and improve network performance.

[0068] Optionally, the embodiment of the application further comprises:

[0069] The target utility function and the resource allocation result of the target network are input into a target neural network, and the target utility function is iteratively optimized according to a preset constraint condition and an iteration condition;

[0070] The optimization is stopped when the preset iteration condition is met, and the optimal resource allocation result of the target network is obtained.

[0071] In the embodiment of the application, the problem of joint allocation of network resources is solved by using a derivative-free optimization method, and the derivative-free optimization network resource pre-allocation service deployment process is regarded as a 'black box', that is, a target neural network, the input is a resource allocation variable and a total amount of resources; the output is the target utility function and the resource allocation result of the target network, and for the derivative-free optimization process, optimization tools such as OpenBox, NOMAD and Hyperopt can be used but are not limited to.

[0072] In the embodiment of the application, the specific process of the derivative-free optimization network resource pre-allocation service is as follows Figure 2 :

[0073] In the embodiment of the application, the derivative-free optimization network resource pre-allocation service first defines a search space S∈R E +V and sets an optimization initial value [R0,C i,0 ] of a set of resource allocation results of the target network, and according to the constraint condition cons defined by the target utility function, starts the derivative-free optimization iteration for searching the optimal resource pre-allocation scheme; in each iteration, according to the network resource allocation scheme [R n ,C i,n+1 ] in the current iteration, the derivative-free optimization network resource pre-allocation algorithm is run once to obtain the target function value under the current resource configuration, that is, obj(n)=∑C in (T i ,R n )-βR n p inj (T i ,R n ,U j )+Σf(p n ), and according to the derivative-free optimization algorithm and the constraint condition actually selected, the resource allocation scheme of the next iteration is determined, and with the increase of the number of iterations, the optimization algorithm can converge to obtain a set of optimal resource allocation schemes to realize the maximum utility of the computing power network.

[0074] Please refer to Figure 3 , the embodiment of the application provides a computing power network resource allocation device, which comprises:

[0075] The acquisition module 31 is configured to acquire a computing power network resource utilization rule of a target network, wherein the computing power network resource utilization rule of the target network is obtained by mapping computing power network resource information of the target network and service time of a user of the target network;

[0076] The first processing module 32 is configured to obtain a computing power network price model of the target network according to the computing power network resource utilization rule of the target network.

[0077] The second processing module 33 is configured to construct a target utility function according to the computing power network resource utilization rule of the target network and the computing power network price model of the target network.

[0078] The third processing module 34 is configured to obtain a resource allocation result of the target network according to the target utility function.

[0079] In the embodiment of the application, the computing power network resource information includes bandwidth resource and computing power resource.

[0080] The bandwidth resource includes at least one of minimum bandwidth demand and maximum bandwidth demand, and the computing power resource includes at least one of minimum computing power price and maximum computing power price; the service time of the user of the target network includes service start time of the user and service end time of the user.

[0081] The computing power network resource utilization rule of the target network is that the minimum bandwidth demand, the maximum bandwidth demand, the minimum computing power price and the maximum computing power price are mapped with the service start time of the user and the service end time of the user, and a constraint condition is set for the computing power network resource information and the service time of the user of the target network.

[0082] In the embodiment of the application, the computing power network price model of the target network is determined according to the computing power network resource utilization rule of the target network and the user distribution probability of users with different priorities at different time.

[0083] In the embodiment of the application, the target utility function is the product of the computing power network price model of the target network, the computing power network resource utilization rule of the target network with an attached weight and the user distribution probability of users with different priorities at different time.

[0084] In the embodiment of the application, the target utility function further includes user satisfaction.

[0085] The target utility function is the computing power network price model of the target network minus the product of the computing power network resource utilization rule of the target network and the user distribution probability of different priority users at different moments with an accompanying weight, plus a user satisfaction function.

[0086] In the embodiment of the application, the third processing module comprises:

[0087] The processing sub-module is configured to take the corresponding bandwidth resource and computing power resource in the computing power network resource utilization rule of the target network and the computing power network price model of the target network at the maximum value of the target utility function as the resource allocation result of the target network.

[0088] In the embodiment of the application, the method further comprises:

[0089] The optimization module is configured to input the target utility function and the resource allocation result of the target network into a target neural network, and iteratively optimize the target utility function according to a pre-set constraint condition and an iteration condition; the optimization is stopped when the pre-set iteration condition is met, and the optimal resource allocation result of the target network is obtained.

[0090] The computing power network resource allocation device provided in the embodiment of the application can realize Figure 1 The method embodiment realizes each process and achieves the same technical effect, and thus repeated description is omitted here.

[0091] The embodiment of the application provides an electronic device 40, referring to Figure 4 As shown in Figure 4 The principle block diagram of the electronic device 40 in the embodiment of the application comprises a processor 41, a memory 42, and a program or instruction stored in the memory 42 and capable of running on the processor 41, and the program or instruction is executed by the processor to realize the steps in any one of the computing power network resource allocation methods of the application.

[0092] The embodiment of the application provides a readable storage medium, and the readable storage medium stores a program or instruction, and the program or instruction is executed by a processor to realize each process of the embodiment of the computing power network resource allocation method according to any one of the above, and the same technical effect can be achieved, and thus repeated description is omitted here.

[0093] Computer-readable media includes permanent and non-permanent, removable and non-removable media, which can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0094] It should be noted that in this paper, the term "includes", "contains" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or includes elements inherent to such process, method, article or device. Without more limitations, the element defined by the statement "includes a" does not exclude the presence of another identical element in the process, method, article or device including the element.

[0095] The above-mentioned embodiment number of the application is only for description, not representing the advantages and disadvantages of the embodiments.

[0096] Through the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment method can be realized by software plus the necessary general hardware platform, of course, it can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), including a plurality of instructions for making a service classification device (which can be a mobile phone, computer, server, air conditioner or network device, etc.) execute the method described in each embodiment of the present application.

[0097] The above-mentioned only is the preferred embodiment of the present application, it should be pointed out that, for those skilled in the art, without departing from the principle of the present application, can make a number of improvements and refinements, these improvements and refinements should be regarded as the protection scope of the present application.

Claims

1. A computing power network resource allocation method, characterized in that, The method comprises: obtaining the computing power network resource utilization rule of the target network, wherein the computing power network resource utilization rule of the target network is obtained by mapping the computing power network resource information of the target network and the service time of the user of the target network; obtaining the computing power network price model of the target network according to the computing power network resource utilization rule of the target network; constructing the target utility function according to the computing power network resource utilization rule of the target network and the computing power network price model of the target network; obtaining the resource allocation result of the target network according to the target utility function; the computing power network resource information comprises bandwidth resources and computing power resources; the bandwidth resources comprise at least one of minimum bandwidth demand and maximum bandwidth demand, and the computing power resources comprise at least one of minimum computing power price and maximum computing power price; the service time of the user of the target network comprises service start time and service end time of the user; the computing power network resource utilization rule of the target network is to map the minimum bandwidth demand, the maximum bandwidth demand, the minimum computing power price and the maximum computing power price to the service start time and the service end time of the user, and to set constraint conditions for the computing power network resource information and the service time of the user of the target network.

2. The computing power network resource allocation method according to claim 1, wherein the computing power network price model of the target network is determined according to the computing power network resource utilization rule of the target network and the user distribution probability of users with different priorities at different time points.

3. The computing power network resource allocation method according to claim 1, wherein the target utility function is the computing power network price model of the target network minus the product of the computing power network resource utilization rule of the target network and the user distribution probability of users with different priorities at different time points with an attached weight.

4. The computing power network resource allocation method according to claim 1, wherein the target utility function further comprises user satisfaction; the target utility function is the computing power network price model of the target network minus the product of the computing power network resource utilization rule of the target network and the user distribution probability of users with different priorities at different time points with an attached weight, plus a user satisfaction function.

5. The computing power network resource allocation method of claim 1, wherein, obtaining the resource allocation result of the target network according to the target utility function comprises: taking the corresponding bandwidth resources and computing power resources in the computing power network resource utilization rule of the target network and the computing power network price model of the target network at the maximum value of the target utility function as the resource allocation result of the target network.

6. The computing power network resource allocation method of claim 1, wherein, Further comprising: inputting the target utility function and the resource allocation result of the target network into a target neural network, and iteratively optimizing the target utility function according to a pre-set constraint condition and an iteration condition; stopping optimization when the pre-set iteration condition is met, and obtaining the optimal resource allocation result of the target network.

7. A computing power network resource allocation apparatus characterized in that, ​ An acquisition module is configured to acquire a computing power network resource utilization rule of a target network, wherein the computing power network resource utilization rule of the target network is obtained by mapping computing power network resource information of the target network and service time of a user of the target network; A first processing module is configured to obtain a computing power network price model of the target network according to the computing power network resource utilization rule of the target network; A second processing module is configured to construct a target utility function according to the computing power network resource utilization rule of the target network and the computing power network price model of the target network; A third processing module is configured to obtain a resource allocation result of the target network according to the target utility function. The computing power network resource information includes bandwidth resources and computing power resources. The bandwidth resources include at least one of minimum bandwidth demand and maximum bandwidth demand, and the computing power resources include at least one of a minimum computing power price and a maximum computing power price. The service time of the user of the target network includes service start time and service end time of the user.

8. An electronic device, comprising: The computing power network resource utilization rule of the target network is to map the minimum bandwidth demand, the maximum bandwidth demand, the minimum computing power price and the maximum computing power price to the service start time and the service end time of the user, and to set constraint conditions for the computing power network resource information and the service time of the user of the target network.

9. A readable storage medium characterized by: A processor, a memory and a program or instructions stored on the memory and executable on the processor are included, and the program or instructions are executed by the processor to implement the steps in the computing power network resource allocation method according to any one of claims 1 to 6. A readable storage medium stores a program or instructions, and the program or instructions are executed by a processor to implement the steps in the computing power network resource allocation method according to any one of claims 1 to 6.

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