Multi-domain general computing resource collaborative deployment optimization method and device, equipment and medium

Abstract

The invention belongs to the technical field of communication, and particularly relates to a multi-domain general computing resource collaborative deployment optimization method and device, equipment and a medium. The method comprises the following steps: determining the communication time delay of a service from an edge substation to a cloud, constructing a first target function with the maximum time delay redundancy as a target, and solving to obtain the shortest service communication time delay. And calculating a service quality function and a resource adequacy index based on the shortest service communication time delay. And when the resource adequacy does not meet the preset condition, constructing a second target function with the minimum general calculation resource investment cost as a target, and solving to obtain a multi-domain general calculation resource collaborative deployment optimal scheme. According to the scheme, a traditional service hierarchical transmission method is abandoned, the cloud-side service shortest communication time delay is calculated through the maximum redundancy time delay, the resource adequacy is evaluated, and a low-cost resource collaborative deployment scheme is given.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and specifically relates to a method, device, equipment and medium for collaborative deployment optimization of multi-domain computing resources. Background Art

[0002] The application of edge substation and cloud master station technology can effectively improve the intelligence level of the new distribution system and meet the complex and changing operation requirements. In the new distribution system, the edge substation can realize real-time processing and analysis of massive data. As a key node of the new distribution system, it undertakes tasks such as data collection, preprocessing and real-time control. The cloud master station can provide comprehensive data support for the operation and control of the new distribution system. Therefore, a close collaborative working relationship is formed between the cloud master station and the edge substation. The edge substation is responsible for collecting and processing real-time data, and uploading key data to the cloud master station for further analysis and mining. The cloud master station performs global optimization and scheduling based on the data uploaded by the edge substation to achieve precise control of the entire power system.

[0003] Although the current cloud-edge transmission system in the distribution network has many advantages, it also has significant disadvantages. Although edge computing can reduce the bandwidth pressure of the cloud master station, in some cases, the bandwidth from the edge substation to the cloud master station is limited and cannot meet the needs of high-concurrency multi-service. The traditional method generally adopts hierarchical transmission of services, which to a certain extent causes a waste of resources and fails to fully explore the limits of the original resource cluster. Summary of the invention

[0004] The purpose of the present invention is to provide a method, device, equipment and medium for optimizing the collaborative deployment of multi-domain computing resources to solve the problems in the prior art.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for optimizing the coordinated deployment of multi-domain computing resources, comprising the following steps: Determine the communication delay of the service from the edge substation to the cloud; Based on the communication delay, a first objective function is constructed with the maximum delay redundancy as the goal, and the communication and computing resources allocated to each edge substation are used as the first constraint condition of the first objective function; based on the first constraint condition, the first objective function is solved to obtain the shortest service communication delay under the maximum delay redundancy; Calculate the service quality function of the service transmitted from each edge substation to the cloud master station based on the shortest service communication delay; calculate the cloud-edge resource abundance index based on each of the service quality functions; When the cloud-edge resource abundance index does not meet the preset conditions, a collaborative deployment optimization model for computing resources is constructed based on the shortest service communication delay; wherein the collaborative deployment optimization model for computing resources includes a second objective function with the goal of minimizing the investment cost of computing resources, and a second constraint condition with the expected resource abundance as a constraint; the second objective function is solved based on the second constraint condition to obtain the optimal solution for the collaborative deployment of multi-domain computing resources.

[0006] Furthermore, the communication delay of the service from the edge substation to the cloud is determined, including: The communication delay of the service at the edge substation includes the unloading delay of the service at the edge substation, the transmission delay from the edge substation to the cloud master station, and the unloading delay of the service at the cloud master station.

[0007] Furthermore, the service unloading delay at the edge substation , Transmission delay from edge substation to cloud main station And the service unloading delay at the cloud master station , respectively expressed as follows:

[0008]

[0009]

[0010] in, It is at the edge station The amount of data that the service unloads locally; It is an edge substation The amount of data transmitted from the service to the cloud master station; It is the number of CPU cycles required to process a unit bit of data; The computing resources at the edge; Assign to edge substation for edge The percentage of computing power resources in the total computing power resources of the edge; is the cloud-edge transmission bandwidth; Assigned to edge substations The percentage of transmission bandwidth to cloud-edge transmission bandwidth; It is the computing power resource of the cloud; Distribute processing edge substations to the cloud The percentage of computing resources for transmission services to the total computing resources in the cloud.

[0011] Furthermore, based on the communication delay, a first objective function is constructed with the maximum delay redundancy as the goal, and the communication and computing resources allocated to each edge substation are used as the first constraint condition of the first objective function; Wherein, the first objective function is expressed as:

[0012] The first constraint is expressed as:

[0013]

[0014]

[0015]

[0016]

[0017]

[0018] in, They respectively represent the amount of data unloaded locally by the business, the percentage of edge substation computing resources in the total edge computing resources, the percentage of bandwidth allocated to edge substations in the total transmission bandwidth, and the percentage of computing resources allocated to edge substations in the cloud in the total cloud computing resources. is the number of edge substations at the edge; For services under computing resource constraints, The maximum delay for successful transmission to the cloud master station; Edge Substation Communication delay of the service; It is an edge substation The amount of business data; It is at the edge station The amount of data that the service unloads locally; Assign to edge substation for edge The percentage of computing power resources in the total computing power resources of the edge; Distribute processing edge substations to the cloud The percentage of computing resources for transmission services to the total computing resources in the cloud; Assigned to edge substations The percentage of the transmission bandwidth of edge to cloud transmission bandwidth.

[0019] Furthermore, the general computing resource collaborative deployment optimization model includes a second objective function with the goal of minimizing the general computing resource investment cost, and a second constraint condition with the expected resource abundance as a constraint; Wherein, the second objective function is expressed as:

[0020] The second constraint is expressed as:

[0021]

[0022]

[0023] in, They represent the number of bits of data that can be transmitted per second and the number of cycles of computer operation per second respectively; Indicates expected resource sufficiency; To calculate the resource investment cost.

[0024] Furthermore, the resource investment cost is calculated It is expressed as:

[0025] in, They are the communication resource deployment cost and computing resource deployment cost respectively; All are factors affecting communication resource costs; All of them are factors affecting computing resource costs; The upper limit of the bandwidth of the communication technology; The maximum computing power supported by a single device; , They are respectively the deployment cost of communication resources when the upper limit of the communication technology bandwidth is not exceeded and the deployment cost of communication resources when the upper limit of the communication technology bandwidth is exceeded; , They are respectively the computing resource deployment cost when the computing power support capability of a single device is not exceeded, and the computing resource deployment cost when the computing power support capability of a single device is exceeded; They respectively represent the number of bits of data that can be transmitted per second and the number of computer cycles per second.

[0026] Furthermore, in the step of solving the second objective function based on the second constraint condition, the CVX toolbox in MATLAB is used for solving.

[0027] In a second aspect, the present invention provides a multi-domain computing resource collaborative deployment optimization device, comprising: The delay determination module is used to determine the communication delay of the service from the edge substation to the cloud; A first optimization module is used to construct a first objective function based on the communication delay and with the delay redundancy as the goal, and use the communication and computing resources allocated to each edge substation as the first constraint of the first objective function; solve the first objective function based on the first constraint to obtain the shortest service communication delay under the maximum delay redundancy; A sufficiency calculation module is used to calculate the service quality function of the service transmitted from each edge substation to the cloud main station based on the shortest service communication delay; and calculate the cloud-edge resource sufficiency index based on each of the service quality functions; The second optimization module is used to construct a collaborative deployment optimization model of general computing resources based on the shortest business communication delay when the cloud-edge resource abundance index does not meet the preset conditions; wherein the collaborative deployment optimization model of general computing resources includes a second objective function with the goal of minimizing the investment cost of general computing resources, and a second constraint condition with the expected resource abundance as a constraint; the second objective function is solved based on the second constraint condition to obtain the optimal solution for the collaborative deployment of multi-domain general computing resources.

[0028] According to a third aspect of the present invention, an electronic device is provided, comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the above-mentioned multi-domain computing resource collaborative deployment optimization method.

[0029] In a fourth aspect, the present invention provides a computer-readable storage medium storing at least one instruction, wherein when the at least one instruction is executed by a processor, the multi-domain computing resource collaborative deployment optimization method as described above is implemented. Compared with the prior art, the present invention has the following beneficial effects: The multi-domain general computing resource collaborative deployment optimization method provided by the present invention is aimed at the high-concurrency differentiated spatiotemporal business flow requirements of cloud-edge, abandons the traditional method of hierarchical business transmission, and calculates the shortest communication delay of cloud-edge business by constructing and solving the maximum redundant delay. This is used to evaluate the cloud-edge resource abundance, and a low-cost multi-domain resource collaborative deployment solution is given, which explores the potential of the original resource cluster, makes the resource deployment in this scenario more reasonable, and realizes reasonable control of the investment scale, and can increase the reuse efficiency of general computing resources to the limit. Under the requirement of ensuring resource abundance, it has important guiding significance for the allocation of related general computing resources in the new distribution network with the goal of minimizing investment costs. A multi-domain general computing resource collaborative deployment optimization device, electronic device and computer-readable storage medium provided by the present invention also solve the problems raised in the background technology part. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 A schematic diagram of a process flow of a multi-domain computing resource collaborative deployment optimization method according to an embodiment of the present invention; Figure 2 A scene diagram of a high-concurrency multiple service being transmitted from an edge substation to a cloud master server in an embodiment of the present invention; Figure 3 This is a structural block diagram of a multi-domain computing resource collaborative deployment optimization device according to an embodiment of the present invention; Figure 4 The present invention is a structural block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0032] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0033] Example 1 An embodiment of the present invention provides a multi-domain computing resource collaborative deployment optimization method that is adapted to distributed driving of high-concurrency differentiated spatiotemporal business flows, the method comprising: calculating the communication delay of a business transmitted from an edge substation to a cloud main station, which is composed of the business unloading delay at the edge substation, the transmission delay from the edge substation to the cloud main station, and the business unloading delay at the cloud main station; constructing a delay redundancy function, calculating the maximum delay redundancy of cloud-edge business transmission, and finding the shortest business communication delay under the maximum delay redundancy; calculating the service quality function of the business transmitted from each edge substation to the cloud main station, calculating the cloud-edge resource abundance index, and constructing a resource abundance evaluation model; when the resource abundance is less than 1, communication and computing power resources need to be redeployed, and a computing resource collaborative deployment optimization model that minimizes the investment cost of computing resources is constructed with the expected resource abundance as a constraint, and this optimization problem is solved by the CVX toolbox in MATLAB.

[0034] like Figure 1 As shown, a multi-domain computing resource collaborative deployment optimization method includes the following steps: S1. Determine the communication delay of the service from the edge substation to the cloud.

[0035] like Figure 2 As shown, in this solution, a scenario in which multiple high-concurrency services are transmitted from an edge substation to a cloud master server may include a cloud master and several edge substations, and the services are transmitted from the edge substation to the cloud master.

[0036] Specifically, edge substation i Communication delay of the service Including the service unloading delay at the edge substation , Transmission delay from edge substation to cloud main station And the service unloading delay at the cloud master station , which is expressed as follows:

[0037] To be more specific, the service unloading delay at the edge substation , Transmission delay from edge substation to cloud main station And the service unloading delay at the cloud master station , respectively expressed as follows:

[0038]

[0039]

[0040]

[0041] in, It is an edge substation The amount of business data; It is at the edge station The amount of data that the service unloads locally; It is an edge substation The amount of data transmitted from the service to the cloud master station; It is the number of CPU cycles required to process a unit bit of data; The computing resources at the edge; Assign to edge substation for edge The percentage of computing power resources in the total computing power resources of the edge; is the cloud-edge transmission bandwidth; Assigned to edge substations The percentage of transmission bandwidth to cloud-edge transmission bandwidth; It is the computing power resource of the cloud; Distribute processing edge substations to the cloud The percentage of computing resources for transmission services to the total computing resources in the cloud.

[0042] S2. Based on the communication delay, a first objective function is constructed with the goal of maximizing delay redundancy, and the communication and computing resources allocated to each edge substation are used as the first constraint condition of the first objective function; based on the first constraint condition, the first objective function is solved to obtain the shortest service communication delay under the maximum delay redundancy.

[0043] Wherein, the first objective function is expressed as:

[0044] The first constraint is expressed as:

[0045]

[0046]

[0047]

[0048]

[0049]

[0050] in, They respectively represent the amount of data unloaded locally by the business, the percentage of edge substation computing resources in the total edge computing resources, the percentage of bandwidth allocated to edge substations in the total transmission bandwidth, and the percentage of computing resources allocated to edge substations in the cloud in the total cloud computing resources. is the number of edge substations at the edge; For services under computing resource constraints, The maximum delay for successful transmission to the cloud master station; Edge Substation Communication delay of the service; It is an edge substation The amount of business data; It is at the edge station The amount of data that the service unloads locally; Assign to edge substation for edge The percentage of computing power resources in the total computing power resources of the edge; Distribute processing edge substations to the cloud The percentage of computing resources for transmission services to the total computing resources in the cloud; Assigned to edge substations The percentage of the transmission bandwidth of edge to cloud transmission bandwidth.

[0051] It should be noted that the shortest service transmission delay refers to the minimum time required for the service to be transmitted from the edge substation to the cloud main station under given resource allocation and constraints. After the shortest service transmission delay is determined, the communication delay Then it will be determined.

[0052] S3. Calculate the service quality function of the service transmitted from each edge substation to the cloud main station based on the shortest service communication delay; calculate the cloud-edge resource abundance index based on each of the service quality functions.

[0053] The calculation formula is as follows:

[0054] in, Edge Substation Quality of service function; It is the resource abundance index. S4. When the cloud-edge resource abundance index does not meet the preset conditions, a collaborative deployment optimization model for computing resources is constructed based on the shortest service communication delay; wherein the collaborative deployment optimization model for computing resources includes a second objective function with the goal of minimizing the investment cost of computing resources, and a second constraint condition with the expected resource abundance as a constraint; the second objective function is solved based on the second constraint condition to obtain the optimal solution for the collaborative deployment of multi-domain computing resources.

[0055] Specifically, when the resource abundance index is less than 1, the general computing resources are redeployed. In this solution, the expected resource abundance is set to 1.

[0056] Wherein, the second objective function is expressed as:

[0057] The second constraint is expressed as:

[0058]

[0059]

[0060] in, They represent the number of bits of data that can be transmitted per second and the number of cycles of computer operation per second respectively; Indicates expected resource sufficiency; To calculate the resource investment cost.

[0061] To be more specific, calculate the resource investment cost It is expressed as:

[0062] in, They are the communication resource deployment cost and computing resource deployment cost respectively; All are factors affecting communication resource costs; All of them are factors affecting computing resource costs; The upper limit of the bandwidth of the communication technology; The maximum computing power supported by a single device; , They are respectively the deployment cost of communication resources when the upper limit of the communication technology bandwidth is not exceeded and the deployment cost of communication resources when the upper limit of the communication technology bandwidth is exceeded; , They are respectively the computing resource deployment cost when the computing power support capability of a single device is not exceeded, and the computing resource deployment cost when the computing power support capability of a single device is exceeded; They respectively represent the number of bits of data that can be transmitted per second and the number of computer cycles per second.

[0063] Specifically, in the step of solving the second objective function based on the second constraint condition, the CVX toolbox in MATLAB is used for solving.

[0064] In another optional embodiment, a multi-domain computing resource collaborative deployment optimization method adapted to distributed drive of high-concurrency differentiated spatiotemporal business flows is provided, the method comprising the following steps: Step 1: Calculate the communication delay of the service from the edge substation to the cloud. The communication delay of the service consists of the unloading delay of the service at the edge substation, the transmission delay from the edge substation to the cloud main station, and the unloading delay of the service at the cloud main station.

[0065] Step 2: Construct a delay redundancy function, calculate the maximum cloud-edge delay redundancy based on the communication and computing resources allocated to each edge substation, and find the shortest service transmission delay under the maximum delay redundancy.

[0066] Step 3: Calculate the service quality function of the business transmitted from each edge substation to the cloud main station, calculate the cloud-edge resource sufficiency index, and build a resource sufficiency evaluation model.

[0067] Step 4: Determine whether it is necessary to redeploy computing resources based on the resource abundance index. When the resource abundance index is less than 1, the transmission quality of the service from the edge substation to the cloud master station is poor, and communication and computing resources need to be redeployed.

[0068] Step 5: Taking the expected resource abundance as a constraint, a computing resource collaborative deployment optimization model that minimizes the computing resource investment cost is constructed. This optimization problem is solved by the CVX toolbox in MATLAB.

[0069] In step 1, the communication delay of the service from the edge substation to the cloud master station in the new distribution network is calculated, including: Edge Substation For example, due to the constraints of communication resources and computing resources, the edge substation Communication delay of the service Delay of unloading services at edge substations , Transmission delay from edge substation to cloud main station And the service unloading delay at the cloud master station The calculation formula is as follows:

[0070] in It is an edge substation The amount of business data; It is at the edge station The amount of data that the service unloads locally; It is an edge substation The amount of data transmitted from the service to the cloud master station; It is the number of CPU cycles required to process a unit bit of data; The computing resources at the edge; Assign to edge substation for edge The percentage of computing power resources in the total computing power resources of the edge; is the cloud-edge transmission bandwidth; Assigned to edge substations The percentage of transmission bandwidth to cloud-edge transmission bandwidth; It is the computing power resource of the cloud; Distribute processing edge substations to the cloud The percentage of computing resources for transmission services to the total computing resources in the cloud; In step 2, a delay redundancy function is constructed to calculate the maximum cloud-edge delay redundancy with the communication and computing resources allocated to each edge substation as constraints. The calculation formula is as follows:

[0071] in, For services under computing resource constraints, The maximum delay for successful transmission to the cloud master station; is the number of edge substations at the edge; In step 2, the maximum delay redundancy can be solved in a given feasible domain using the CXV toolbox in MATLAB.

[0072] In step 3, the edge substation Take the example of computing the service quality function of the business transmitted from each edge substation to the cloud main station, calculating the cloud-edge resource abundance index, and building a resource abundance evaluation model. The calculation formula is as follows:

[0073] in, Edge Substation Quality of service function; It is the resource abundance index. In step 4, it is determined whether it is necessary to redeploy computing resources based on the resource abundance index. When , the general computing resource deployment meets the transmission delay requirements of all business data packets, and there is no need to deploy general computing resources. When the service is transmitted from the edge substation to the cloud main station, the transmission quality is poor, and communication and computing resources need to be redeployed.

[0074] when When , communication and computing resources are redeployed. Taking the expected resource abundance as a constraint, a computing resource collaborative deployment optimization model that minimizes the computing resource investment cost is constructed. This optimization problem is solved by the CVX toolbox in MATLAB, and the calculation formula is: Let the total cost of resource deployment be :

[0075] in, They are the communication resource deployment cost and computing resource deployment cost respectively; All are factors affecting communication resource costs; All of them are factors affecting computing resource costs; The upper limit of the bandwidth of the communication technology; It is the maximum computing power supported by a single device.

[0076] Solve the minimum cloud-edge computing resource deployment cost in a given feasible domain. The calculation formula is as follows:

[0077] In the simulation environment, the total communication delay of the service is determined, and based on its corresponding service quality function, the resource sufficiency under the cloud-edge architecture is further calculated; based on the resource sufficiency index, it is determined whether to reallocate communication and computing resources. When the resource sufficiency index is less than 1, the computing resources are redeployed. With the goal of minimizing the deployment cost and maximizing the resource sufficiency, the corresponding linear programming problem is established and solved to optimize the deployment scale of communication and computing resources.

[0078] Example 2 like Figure 3 As shown, based on the same inventive concept as the above embodiment, the present invention also provides a multi-domain computing resource collaborative deployment optimization device, including: The delay determination module is used to determine the communication delay of the service from the edge substation to the cloud; A first optimization module is used to construct a first objective function based on the communication delay and with the delay redundancy as the goal, and use the communication and computing resources allocated to each edge substation as the first constraint of the first objective function; solve the first objective function based on the first constraint to obtain the shortest service communication delay under the maximum delay redundancy; A sufficiency calculation module is used to calculate the service quality function of the service transmitted from each edge substation to the cloud main station based on the shortest service communication delay; and calculate the cloud-edge resource sufficiency index based on each of the service quality functions; The second optimization module is used to construct a collaborative deployment optimization model of general computing resources based on the shortest business communication delay when the cloud-edge resource abundance index does not meet the preset conditions; wherein the collaborative deployment optimization model of general computing resources includes a second objective function with the goal of minimizing the investment cost of general computing resources, and a second constraint condition with the expected resource abundance as a constraint; the second objective function is solved based on the second constraint condition to obtain the optimal solution for the collaborative deployment of multi-domain general computing resources.

[0079] Example 3 like Figure 4 As shown, the present invention also provides an electronic device 100 for implementing a multi-domain computing resource collaborative deployment optimization method; The electronic device 100 includes a memory 101 , at least one processor 102 , a computer program 103 stored in the memory 101 and executable on the at least one processor 102 , and at least one communication bus 104 .

[0080] The memory 101 can be used to store a computer program 103. The processor 102 implements the steps of a multi-domain computing resource collaborative deployment optimization method in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0081] The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0082] At least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0083] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a method for optimizing the coordinated deployment of multi-domain computing resources, and the processor 102 can execute the plurality of instructions to implement: Determine the communication delay of the service from the edge substation to the cloud; Based on the communication delay, a first objective function is constructed with the maximum delay redundancy as the goal, and the communication and computing resources allocated to each edge substation are used as the first constraint condition of the first objective function; based on the first constraint condition, the first objective function is solved to obtain the shortest service communication delay under the maximum delay redundancy; Calculate the service quality function of the service transmitted from each edge substation to the cloud master station based on the shortest service communication delay; calculate the cloud-edge resource abundance index based on each of the service quality functions; When the cloud-edge resource abundance index does not meet the preset conditions, a collaborative deployment optimization model for computing resources is constructed based on the shortest service communication delay; wherein the collaborative deployment optimization model for computing resources includes a second objective function with the goal of minimizing the investment cost of computing resources, and a second constraint condition with the expected resource abundance as a constraint; the second objective function is solved based on the second constraint condition to obtain the optimal solution for the collaborative deployment of multi-domain computing resources.

[0084] Example 4 If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0085] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0087] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0089] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the collaborative deployment of multi-domain computing resources, characterized in that: The following steps are involved: Determine the communication delay of the service from the edge substation to the cloud; Based on the communication delay, a first objective function is constructed with the maximum delay redundancy as the goal, and the communication and computing resources allocated to each edge substation are used as the first constraint condition of the first objective function; Solving the first objective function based on the first constraint condition to obtain the shortest service communication delay under the maximum delay redundancy; Calculate the service quality function of the service transmitted from each edge substation to the cloud master station based on the shortest service communication delay; calculate the cloud-edge resource abundance index based on each of the service quality functions; When the cloud-edge resource abundance index does not meet the preset conditions, a collaborative deployment optimization model for computing resources is constructed based on the shortest service communication delay; wherein the collaborative deployment optimization model for computing resources includes a second objective function with the goal of minimizing the investment cost of computing resources, and a second constraint condition with the expected resource abundance as a constraint; the second objective function is solved based on the second constraint condition to obtain the optimal solution for the collaborative deployment of multi-domain computing resources.

2. The multi-domain computing resource collaborative deployment optimization method according to claim 1 is characterized in that: Determine the communication latency of services transmitted from edge substations to the cloud, including: The communication delay of the service at the edge substation includes the unloading delay of the service at the edge substation, the transmission delay from the edge substation to the cloud master station, and the unloading delay of the service at the cloud master station.

3. The multi-domain computing resource collaborative deployment optimization method according to claim 2 is characterized in that: Service unloading delay at edge substation , Transmission delay from edge substation to cloud main station And the service unloading delay at the cloud master station , respectively expressed as follows: in, It is at the edge station The amount of data that the service unloads locally; It is an edge substation The amount of data transmitted from the service to the cloud master station; It is the number of CPU cycles required to process a unit bit of data; The computing resources at the edge; Assign to edge substation for edge The percentage of computing power resources in the total computing power resources of the edge; is the cloud-edge transmission bandwidth; Assigned to edge substations The percentage of transmission bandwidth to cloud-edge transmission bandwidth; It is the computing power resource of the cloud; Distribute processing edge substations to the cloud The percentage of computing resources for transmission services to the total computing resources in the cloud.

4. The multi-domain computing resource collaborative deployment optimization method according to claim 1 is characterized in that: Based on the communication delay, a first objective function is constructed with the maximum delay redundancy as the goal, and the communication and computing resources allocated to each edge substation are used as the first constraint condition of the first objective function; Wherein, the first objective function is expressed as: The first constraint is expressed as: in, They respectively represent the amount of data unloaded locally by the business, the percentage of edge substation computing resources in the total edge computing resources, the percentage of bandwidth allocated to edge substations in the total transmission bandwidth, and the percentage of computing resources allocated to edge substations in the cloud in the total cloud computing resources. is the number of edge substations at the edge; For services under computing resource constraints, The maximum delay for successful transmission to the cloud master station; Edge Substation Communication delay of the service; It is an edge substation The amount of business data; It is at the edge station The amount of data that the service unloads locally; Assign to edge substation for edge The percentage of computing power resources in the total computing power resources of the edge; Distribute processing edge substations to the cloud The percentage of computing resources for transmission services to the total computing resources in the cloud; Assigned to edge substations The percentage of the transmission bandwidth of edge to cloud transmission bandwidth.

5. The multi-domain computing resource collaborative deployment optimization method according to claim 1 is characterized in that: The computing resource collaborative deployment optimization model includes a second objective function with the goal of minimizing the computing resource investment cost and a second constraint condition with the expected resource abundance as a constraint; Wherein, the second objective function is expressed as: The second constraint is expressed as: in, They represent the number of bits of data that can be transmitted per second and the number of cycles of computer operation per second respectively; Indicates expected resource sufficiency; To calculate the resource investment cost.

6. The multi-domain computing resource collaborative deployment optimization method according to claim 1 is characterized in that: Calculate resource investment cost It is expressed as: in, They are the communication resource deployment cost and computing resource deployment cost respectively; All are factors affecting the cost of communication resources; All of them are factors affecting computing resource costs; The upper limit of the bandwidth of the communication technology; The maximum computing power supported by a single device; , They are respectively the deployment cost of communication resources when the upper limit of the communication technology bandwidth is not exceeded and the deployment cost of communication resources when the upper limit of the communication technology bandwidth is exceeded; , They are respectively the computing resource deployment cost when the computing power support capability of a single device is not exceeded, and the computing resource deployment cost when the computing power support capability of a single device is exceeded; They respectively represent the number of bits of data that can be transmitted per second and the number of computer cycles per second.

7. The multi-domain computing resource collaborative deployment optimization method according to claim 1 is characterized in that: In the step of solving the second objective function based on the second constraint condition, the CVX toolbox in MATLAB is used for solving.

8. A multi-domain computing resource collaborative deployment optimization device, characterized in that: include: The delay determination module is used to determine the communication delay of the service from the edge substation to the cloud; A first optimization module is used to construct a first objective function based on the communication delay and with the delay redundancy as the goal, and use the communication and computing resources allocated to each edge substation as the first constraint condition of the first objective function; Solving the first objective function based on the first constraint condition to obtain the shortest service communication delay under the maximum delay redundancy; A sufficiency calculation module is used to calculate the service quality function of the service transmitted from each edge substation to the cloud main station based on the shortest service communication delay; and calculate the cloud-edge resource sufficiency index based on each of the service quality functions; The second optimization module is used to construct a collaborative deployment optimization model of general computing resources based on the shortest business communication delay when the cloud-edge resource abundance index does not meet the preset conditions; wherein the collaborative deployment optimization model of general computing resources includes a second objective function with the goal of minimizing the investment cost of general computing resources, and a second constraint condition with the expected resource abundance as a constraint; the second objective function is solved based on the second constraint condition to obtain the optimal solution for the collaborative deployment of multi-domain general computing resources.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the multi-domain computing resource collaborative deployment optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, the multi-domain computing resource collaborative deployment optimization method as described in any one of claims 1 to 7 is implemented.

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