Computing power scheduling method, device, equipment, medium and product

Abstract

The invention provides a computing power scheduling method and device, equipment, a medium and a product, and relates to the technical field of cloud computing. The computing power scheduling method comprises the following steps: creating a solution according to a computing power demand; determining a first scheduling policy from a plurality of scheduling policies according to the solution; optimizing the first scheduling strategy to obtain a second scheduling strategy; according to the second scheduling strategy, calculating the integrating degree of the solution and each preset cloud pool, and generating a scheduling scheme; from the plurality of cloud pools, determining the cloud pool with the maximum integrating degree as a first cloud pool, and determining the scheduling scheme corresponding to the first cloud pool as a target scheduling scheme; and deploying the target scheduling scheme on the first cloud pool to obtain a resource instance of the computing power demand. According to the computing power scheduling method, the computing power demand solution is automatically generated, the optimal strategy is selected through the matching algorithm, the integrating degree of each cloud pool is calculated, and finally the cloud pool with the highest integrating degree is selected for deployment, so that the problem of low efficiency of the computing power scheduling method is solved.

Description

Technical Field

[0001] The present application relates to the field of cloud computing technology, and in particular to a computing power scheduling method, device, equipment, medium and product. Background Art

[0002] Computing power scheduling refers to the intelligent allocation and adjustment of computing power resources according to task requirements and resource availability in computing power resource management. Its purpose is to optimize computing power resource utilization, improve computing efficiency, reduce energy consumption, and ensure that tasks can be executed at the right time and place.

[0003] When implementing computing power scheduling, the existing technology first clarifies the requirements for the activation of computing power resources, and then obtains the data center locations of each cloud provider and its cloud region. The user manually compares the attributes of these cloud regions to determine the most suitable cloud provider and cloud region. After the cloud region is determined, the user needs to log in to the official website of the selected cloud provider, switch to the selected cloud region, and manually create the required cloud resources one by one to complete the scheduling of computing power resources. Because it requires manual comparison and selection of cloud regions, and manual login and creation of resources, the process is cumbersome and time-consuming, and the computing power scheduling of the existing technology has the problem of low scheduling efficiency. Summary of the invention

[0004] The present application provides a computing power scheduling method, device, equipment, medium and product to solve the problem of low scheduling efficiency in the prior art.

[0005] In a first aspect, the present application provides a computing power scheduling method, the method comprising:

[0006] Create a solution based on the preset computing power requirements; wherein the solution is used to plan computing power scheduling;

[0007] According to the solution, determine a first scheduling strategy from a plurality of preset scheduling strategies; wherein the scheduling strategy is used to optimize the computing power scheduling, and the first scheduling strategy refers to a scheduling strategy among the plurality of scheduling strategies that has the highest degree of matching with the solution;

[0008] Optimizing the first scheduling strategy to obtain a second scheduling strategy;

[0009] According to the second scheduling strategy, respectively calculate the degree of fit between the solution and each preset cloud pool, and generate a scheduling scheme generated by each cloud pool according to the solution; wherein the scheduling scheme is a set of steps to implement the solution;

[0010] From the plurality of cloud pools, determining the cloud pool with the greatest degree of fit as the first cloud pool, and determining the scheduling scheme corresponding to the first cloud pool as the target scheduling scheme;

[0011] The target scheduling solution is deployed on the first cloud pool to obtain a resource instance that meets the computing power requirement.

[0012] In a possible design, before creating a solution according to the preset computing power requirement, the method further includes:

[0013] Acquire multiple cloud region information; wherein the cloud region information refers to attribute information of computing resource facilities;

[0014] Creating multiple second cloud pools according to the multiple cloud region information; wherein the second cloud pool refers to a collection of computing resources that are initially created;

[0015] Configuring the parameters of each of the second cloud pools to obtain a third cloud pool corresponding to each of the second cloud pools; wherein the third cloud pool refers to a set of computing resources after parameter configuration;

[0016] The network quality parameters between any two of the third cloud pools are configured respectively to obtain a plurality of the cloud pools.

[0017] In a possible design, optimizing the first scheduling strategy to obtain the second scheduling strategy includes:

[0018] According to the first scheduling strategy, determining at least one target strategy factor from a plurality of preset strategy factors; wherein the strategy factor is used to optimize the first scheduling strategy;

[0019] Setting a weight for each of the target strategy factors;

[0020] The second scheduling policy is generated according to the first scheduling policy, the at least one target policy factor, and the weight of each target policy factor.

[0021] In a possible design, determining at least one target policy factor from a plurality of preset policy factors according to the first scheduling policy includes:

[0022] Obtaining a scheduling target of the first scheduling strategy;

[0023] Calculating the correlation between the scheduling target and each of the strategy factors respectively;

[0024] According to the relevance of each of the policy factors, the multiple policy factors are sorted in descending order, and a preset number of policy factors are selected from the sorting results as the target policy factors.

[0025] In a possible design, the fourth cloud pool refers to any one of the plurality of cloud pools;

[0026] For the fourth cloud pool, the step of calculating the degree of fit between the solution and each preset cloud pool according to the second scheduling strategy, and generating a scheduling scheme for each cloud pool according to the solution includes:

[0027] Acquire multiple configuration parameters of the fourth cloud pool and the weight of each of the configuration parameters in the second scheduling strategy; wherein the multiple configuration parameters include resource parameters, storage parameters, cost parameters and location parameters;

[0028] Calculating the degree of fit of the fourth cloud pool according to the multiple configuration parameters and the weight of each of the configuration parameters;

[0029] A scheduling plan for the fourth cloud pool is generated according to the solution and the degree of compatibility of the fourth cloud pool.

[0030] In a possible design, after deploying the target scheduling solution on the first cloud pool and obtaining the resource instance of the computing power requirement, the method further includes:

[0031] Monitoring the running status of the resource instance;

[0032] According to the running status, the configuration of the resource instance is adjusted.

[0033] In a second aspect, the present application provides a computing power scheduling device, the device comprising:

[0034] A creation module, used to create a solution according to the preset computing power requirements; wherein the solution is used to plan computing power scheduling;

[0035] A first determination module, configured to determine a first scheduling strategy from a plurality of preset scheduling strategies according to the solution; wherein the scheduling strategy is used to optimize the computing power scheduling, and the first scheduling strategy refers to a scheduling strategy among the plurality of scheduling strategies that has the highest degree of matching with the solution;

[0036] An optimization module, used for optimizing the first scheduling strategy to obtain a second scheduling strategy;

[0037] A calculation module, used to calculate the degree of fit between the solution and each preset cloud pool according to the second scheduling strategy, and generate a scheduling scheme generated by each cloud pool according to the solution; wherein the scheduling scheme is a set of steps to implement the solution;

[0038] A second determination module is used to determine the cloud pool with the greatest degree of fit from the multiple cloud pools as the first cloud pool, and determine the scheduling scheme corresponding to the first cloud pool as the target scheduling scheme;

[0039] A deployment module is used to deploy the target scheduling solution on the first cloud pool to obtain a resource instance of the computing power requirement.

[0040] In a possible design, the computing power scheduling device further includes:

[0041] An acquisition module, used to acquire information of multiple cloud regions; wherein the cloud region information refers to attribute information of computing resource facilities;

[0042] A cloud pool creation module, used to create multiple second cloud pools according to the multiple cloud region information; wherein the second cloud pool refers to a collection of computing resources that are initially created;

[0043] A first configuration module is used to configure the parameters of each of the second cloud pools to obtain a third cloud pool corresponding to each of the second cloud pools; wherein the third cloud pool refers to a computing resource set after parameter configuration;

[0044] The second configuration module is used to configure the network quality parameters between any two of the third cloud pools respectively to obtain multiple cloud pools.

[0045] In a possible design, the optimization module includes:

[0046] A determination unit, configured to determine at least one target strategy factor from a plurality of preset strategy factors according to the first scheduling strategy; wherein the strategy factor is used to optimize the first scheduling strategy;

[0047] A weight setting unit, used to set a weight for each of the target strategy factors;

[0048] A policy generating unit is used to generate the second scheduling policy according to the first scheduling policy, the at least one target policy factor, and the weight of each target policy factor.

[0049] In a possible design, the determining unit includes:

[0050] An acquisition component, used to acquire a scheduling target of the first scheduling strategy;

[0051] A calculation component, used to respectively calculate the correlation between the scheduling target and each of the strategy factors;

[0052] The determination component is used to sort the multiple policy factors in descending order according to the relevance of each of the policy factors, and select a preset number of policy factors from the sorting results as the target policy factors.

[0053] In a possible design, the fourth cloud pool refers to any one of the plurality of cloud pools;

[0054] For the fourth cloud pool, the computing module includes:

[0055] An acquisition unit, configured to acquire a plurality of configuration parameters of the fourth cloud pool and a weight of each of the configuration parameters in the second scheduling strategy; wherein the plurality of configuration parameters include resource parameters, storage parameters, cost parameters and location parameters;

[0056] A calculation unit, configured to calculate the degree of fit of the fourth cloud pool according to the plurality of configuration parameters and the weight of each of the configuration parameters;

[0057] A solution generating unit is used to generate a scheduling solution for the fourth cloud pool according to the solution and the degree of compatibility of the fourth cloud pool.

[0058] In a possible design, the computing power scheduling device further includes:

[0059] A monitoring module, used to monitor the running status of the resource instance;

[0060] An adjustment module is used to adjust the configuration of the resource instance according to the running state.

[0061] In a fifth aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0062] The memory stores computer-executable instructions;

[0063] When the processor executes the computer-executable instructions stored in the memory, it is used to implement the computing power scheduling method as described in any one of the first aspects.

[0064] In a sixth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the computing power scheduling method as described in any one of the first aspects.

[0065] In a seventh aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the computing power scheduling method as described in any one of the first aspects.

[0066] The present application provides a computing power scheduling method, device, equipment, medium and product, the method comprising: creating a solution according to a preset computing power demand; wherein the solution is used to plan computing power scheduling; according to the solution, determining a first scheduling strategy from a plurality of preset scheduling strategies; wherein the scheduling strategy is used to optimize the computing power scheduling, and the first scheduling strategy refers to the scheduling strategy with the highest degree of matching with the solution among the plurality of scheduling strategies; optimizing the first scheduling strategy to obtain a second scheduling strategy; according to the second scheduling strategy, respectively calculating the degree of fit between the solution and each preset cloud pool, and generating a scheduling scheme generated by each cloud pool according to the solution; wherein the scheduling scheme is a set of steps to implement the solution; from the plurality of cloud pools, determining the cloud pool with the greatest degree of fit as the first cloud pool, and determining the scheduling scheme corresponding to the first cloud pool as the target scheduling scheme; deploying the target scheduling scheme on the first cloud pool to obtain a resource instance of the computing power demand. The computing power scheduling method of the present application improves the efficiency and accuracy of resource allocation through an automated and intelligent computing power scheduling process. This method automatically selects the most suitable cloud pool and its scheduling scheme by creating and optimizing scheduling strategies, reducing the steps of manual comparison and manual configuration. This not only speeds up the deployment of computing resources, but also ensures the optimal use of resources, reduces energy consumption and operating costs. In addition, by optimizing scheduling strategies and selecting the most suitable cloud pool, computing power needs can be better met and the efficiency of computing power scheduling can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0068] Figure 1 A schematic diagram of an application scenario of the computing power scheduling method provided in an embodiment of the present application;

[0069] Figure 2 Schematic diagram of the process of computing power scheduling method provided in the embodiment of the present application Figure 1 ;

[0070] Figure 3 Schematic diagram of the process of computing power scheduling method provided in the embodiment of the present application Figure 2 ;

[0071] Figure 4 A schematic diagram of the structure of a computing power scheduling device provided in an embodiment of the present application;

[0072] Figure 5 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0073] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0074] In the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit the difference. It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in this application should not be interpreted as being more preferred or more advantageous than other embodiments or design. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way. In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more.

[0075] It should be noted that the "at..." in the embodiments of the present application can be the instant when a certain situation occurs, or can be a period of time after a certain situation occurs, and the embodiments of the present application do not specifically limit this. In addition, the data backup method provided in the embodiments of the present application is only used as an example, and the data backup method can also include more or less content.

[0076] In order to clearly describe the technical solutions of the embodiments of the present application, some terms and technologies involved in the embodiments of the present application are briefly introduced below:

[0077] Computing power: It is a measure of the ability of a computing device or system to process data and perform computing tasks in a unit of time. It can involve multiple aspects such as processor speed, memory capacity, data transmission efficiency, etc., and is often used to describe the performance of supercomputers, data centers or artificial intelligence systems.

[0078] Computing power scheduling: refers to the process of reasonably allocating and optimizing the use of computing power in computing resource management. Its purpose is to dynamically allocate computing resources based on factors such as task priority, resource requirements, and system load to improve the efficiency and performance of the overall system.

[0079] Scheduling strategy: refers to the rules and methods developed in resource management to optimize task execution, which are used to decide how to allocate and manage limited resources. Its purpose is to improve the overall efficiency and performance of the system while meeting task requirements. Scheduling strategies can be based on a variety of factors, such as task priority, resource availability, execution time, and load balancing.

[0080] Cloud pool: refers to a resource collection consisting of multiple virtualized resources in a cloud computing environment. These resources are abstracted and managed through virtualization technology to form a flexible and scalable resource pool that users can dynamically allocate and use according to demand.

[0081] Strategic factors: refers to the key variables or factors that affect decision-making and results during the process of formulating and executing strategies. These factors can include environmental conditions, resource availability, market dynamics, and technological capabilities, depending on the application area of ​​the strategy. The identification and analysis of strategic factors helps to optimize the decision-making process, improve the effectiveness and adaptability of the strategy, and ensure the achievement of goals and the allocation of resources.

[0082] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0083] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0084] In order to clearly understand the technical solution of the present application, the solution of the prior art is first introduced in detail. Computing power resource scheduling refers to the process of dynamically allocating and optimizing the use of these computing capabilities in a computing environment to ensure that tasks can be completed efficiently and in a timely manner. Through computing power resource scheduling, computing resources can be reasonably allocated according to factors such as task requirements, priorities, and system load to improve the efficiency and performance of the overall system. Computing power resource scheduling is particularly critical in fields such as cloud computing, data centers, and high-performance computing, which can ensure the effective use of resources and improve service quality.

[0085] In the prior art, in order to achieve computing power scheduling, it is first necessary to clarify the demand for computing power resources. Then, obtain the data center locations of each cloud provider and its cloud region. Users need to manually compare the properties of these cloud regions to select the most suitable cloud provider and cloud region. After determining the cloud region, users need to log in to the official website of the selected cloud provider, switch to the selected cloud region, and manually create the required cloud resources one by one to complete the scheduling of computing power resources. The entire process lacks automated support, which is time-consuming and error-prone. Therefore, the prior art has the problem of inefficiency in computing power scheduling methods.

[0086] Therefore, in view of the inefficiency of computing power scheduling methods in existing technologies, it was found in the research that in order to solve this problem, it is necessary to develop automated tools or systems to simplify and accelerate the process of cloud provider selection, region switching and resource creation: ① Develop intelligent algorithms or tools to automatically select the most suitable cloud provider and region to reduce the time and errors of manual comparison. ② Build a centralized management platform to integrate the resource management of multiple cloud providers, provide a unified interface and operation process, and improve scheduling efficiency. ③ Create a one-click deployment system that allows users to automatically create all required cloud resources after selecting a cloud region, simplifying the operation process.

[0087] Specific:

[0088] An intelligent computing power scheduling system can be designed. The system first collects and analyzes the resource attributes of each cloud provider and its region through automated tools, and then uses intelligent algorithms to automatically recommend the optimal provider and region combination. Then, it uses a one-click deployment function to realize automatic creation and configuration of resources. Finally, a unified monitoring and management interface is provided through a centralized management platform to ensure the efficiency and accuracy of the entire computing power scheduling process.

[0089] The computing power scheduling method of the embodiment of the present application creates a solution according to the preset computing power demand, and selects the strategy that best matches the solution from multiple scheduling strategies for optimization, ensuring the accuracy and efficiency of the scheduling strategy. By calculating the degree of fit between the solution and each cloud pool, a scheduling plan for each cloud pool is automatically generated to ensure that the selected cloud pool can meet the computing power demand. The cloud pool with the highest degree of fit and its corresponding scheduling plan are selected for deployment, realizing the efficient configuration and utilization of computing power resources. This process reduces manual intervention, improves scheduling speed and accuracy, and solves the problem of inefficiency of computing power scheduling methods.

[0090] Based on the above creative findings, the technical solution of the present application is proposed.

[0091] The following introduces the application scenarios of the computing power scheduling method provided by the embodiment of the present invention. Figure 1 Schematic diagram of application scenarios of the computing power scheduling method provided in the embodiment of the present application. Figure 1As shown, the application scenario includes a mobile terminal 101 and a server 102. The mobile terminal 101 sends the user demand to the server 102, and the server 102 performs computing power scheduling according to the user demand and sends the computing power scheduling result to the mobile terminal 101.

[0092] The embodiments of the present invention are described below in conjunction with the accompanying drawings.

[0093] Figure 2 Schematic diagram of the process of computing power scheduling method provided in the embodiment of the present application Figure 1 .like Figure 2 As shown, in this embodiment, the execution subject of the embodiment of the present invention is a server. Then the computing power scheduling method provided by this embodiment includes the following steps:

[0094] S201. Create a solution based on preset computing power requirements; wherein the solution is used to plan computing power scheduling.

[0095] Specifically, a detailed computing power scheduling plan can be generated by analyzing current and future computing resource requirements, combining historical data and prediction algorithms. The solution takes into account factors such as the priority of different tasks, resource availability, and cost-effectiveness to optimize resource allocation and utilization efficiency. This processing improves the utilization of computing resources, reduces resource waste, and ensures that computing power requirements can be met in a timely manner during peak demand periods, thereby improving the performance and responsiveness of the overall system.

[0096] S202. According to the solution, determine a first scheduling strategy from a plurality of preset scheduling strategies; wherein the scheduling strategy is used to optimize computing power scheduling, and the first scheduling strategy refers to a scheduling strategy among the plurality of scheduling strategies that has the highest degree of matching with the solution.

[0097] Specifically, a matching algorithm can be established to compare the generated solution with multiple preset scheduling strategies to evaluate the effectiveness and efficiency of each strategy in meeting the solution requirements. This process may involve multi-dimensional analysis, including indicators such as resource utilization, execution time, cost, and flexibility. Ultimately, the scheduling strategy with the highest degree of match with the solution is selected as the first scheduling strategy. This process ensures that the selected scheduling strategy can most effectively meet specific computing power requirements, optimize resource allocation, improve the overall performance and responsiveness of the system, and reduce operating costs.

[0098] S203: Optimize the first scheduling strategy to obtain a second scheduling strategy.

[0099] Specifically, the first scheduling strategy can be adjusted and improved by applying optimization algorithms and techniques, such as machine learning, genetic algorithms, or linear programming. This process may involve analyzing the performance bottlenecks of the current strategy, the efficiency of resource allocation, and potential room for improvement, so as to further improve resource utilization and reduce latency while meeting computing power requirements. Through optimization, a more efficient second scheduling strategy is generated, thereby improving the overall performance of the system, reducing resource waste, and enhancing the system's ability to adapt to dynamic demand changes.

[0100] S204. According to the second scheduling strategy, respectively calculate the degree of fit between the solution and each preset cloud pool, and generate a scheduling plan for each cloud pool generated according to the solution; wherein the scheduling plan is a collection of steps to implement the solution.

[0101] Specifically, a fit calculation model can be established that takes into account factors such as the resource characteristics, geographical location, network latency, cost, and requirements of the second scheduling strategy of the cloud pool. Each cloud pool is analyzed, its fit with the solution is calculated, and a corresponding scheduling plan is generated based on this as a set of specific steps to implement the solution. By accurately matching computing power requirements with cloud pool capabilities, it is ensured that the selected cloud pool can execute the scheduling plan, thereby improving resource utilization efficiency, reducing latency and cost, and enhancing system flexibility and reliability.

[0102] S205. From the multiple cloud pools, determine the cloud pool with the greatest degree of fit as the first cloud pool, and determine the scheduling scheme corresponding to the first cloud pool as the target scheduling scheme.

[0103] Specifically, by comparing the previously calculated fit scores of each cloud pool with the solution, the cloud pool with the highest fit can be selected as the first cloud pool. Accordingly, the scheduling scheme of the cloud pool is determined as the target scheduling scheme. This process may involve sorting and screening the fit scores to ensure that the optimal cloud pool is selected. By selecting the most suitable cloud pool, it is ensured that the computing power requirements can be met in the most suitable environment, thereby optimizing resource utilization efficiency, improving system performance, and reducing execution costs and delays.

[0104] S206. Deploy the target scheduling solution on the first cloud pool to obtain a resource instance that meets the computing power requirements.

[0105] Specifically, the resource configuration and execution steps defined in the target scheduling scheme can be gradually implemented into the first cloud pool through automated deployment tools and scripts. This may involve operations such as starting virtual machines, allocating storage resources, configuring network settings, and deploying applications. This processing can achieve efficient satisfaction of computing power requirements, and through automated and precise resource configuration, ensure that computing tasks can be quickly started and run in the optimal environment, thereby improving the system's responsiveness, reliability, and resource utilization efficiency.

[0106] The present embodiment provides a computing power scheduling method, which includes: creating a solution according to a preset computing power demand; wherein the solution is used to plan computing power scheduling; according to the solution, determining a first scheduling strategy from a plurality of preset scheduling strategies; wherein the scheduling strategy is used to optimize computing power scheduling, and the first scheduling strategy refers to a scheduling strategy with the highest degree of matching with the solution among the plurality of scheduling strategies; optimizing the first scheduling strategy to obtain a second scheduling strategy; according to the second scheduling strategy, respectively calculating the degree of fit between the solution and each preset cloud pool, and generating a scheduling scheme generated by each cloud pool according to the solution; wherein the scheduling scheme is a collection of steps to implement the solution; from a plurality of cloud pools, determining the cloud pool with the greatest degree of fit as the first cloud pool, and determining the scheduling scheme corresponding to the first cloud pool as the target scheduling scheme; deploying the target scheduling scheme on the first cloud pool to obtain a resource instance of the computing power demand. A computing power scheduling method achieves the following technical effects: by introducing an automated and intelligent computing power scheduling method, the problem of low scheduling efficiency in the prior art is solved. The solution is automatically generated based on the preset computing power requirements, and the matching algorithm is used to select the best strategy from multiple scheduling strategies for optimization. Then, the compatibility of each cloud pool is automatically calculated based on the optimized strategy, and finally the cloud pool with the highest compatibility is selected for automatic deployment, thus achieving efficient configuration and utilization of computing power resources. This process reduces manual intervention, improves scheduling speed and accuracy, and solves the problem of inefficient computing power scheduling methods.

[0107] In a possible design, S203, optimizing the first scheduling strategy to obtain a second scheduling strategy, includes:

[0108] S2031. According to the first scheduling strategy, determine at least one target strategy factor from a plurality of preset strategy factors; wherein the strategy factor is used to optimize the first scheduling strategy.

[0109] Specifically, by analyzing the performance indicators and demand characteristics of the first scheduling strategy, we can select those policy factors that can significantly improve scheduling efficiency and resource utilization. These policy factors may involve aspects such as the allocation of computing resources, adjustment of task priorities, and optimization of network bandwidth. By selecting appropriate policy factors and assigning them weights, we can effectively optimize the first scheduling strategy and generate a better second scheduling strategy. This process can improve the accuracy and efficiency of computing power scheduling, ensuring resource utilization and efficient execution of tasks.

[0110] S2032. Set a weight for each target strategy factor.

[0111] Specifically, the degree of influence of each factor on the optimization of the scheduling strategy can be calculated through historical data analysis of the target strategy factors, simulation experiments, or expert experience, and corresponding weights can be assigned accordingly. These weights reflect the relative importance of each strategy factor in the overall scheduling strategy. By setting the weights reasonably, the scheduling strategy can be adjusted more accurately to better meet the computing power requirements and resource constraints, thereby improving the overall efficiency and effectiveness of the scheduling. This process can enhance the flexibility and adaptability of the scheduling strategy and optimize the coordination of resource allocation and task execution.

[0112] S2033. Generate a second scheduling strategy according to the first scheduling strategy, at least one target strategy factor, and the weight of each target strategy factor.

[0113] Specifically, an optimization model can be constructed, with the first scheduling strategy as the basic framework, and adjusted and optimized in combination with the selected target strategy factors and their corresponding weights. The specific implementation may include using linear programming, heuristic algorithms, or machine learning techniques to comprehensively consider the impact of each strategy factor, and weightedly adjust the scheduling strategy according to its weight to generate a better second scheduling strategy. Through refined strategy factor adjustment and weight allocation, the overall performance and adaptability of the scheduling strategy can be improved, thereby achieving more efficient resource allocation and task execution to meet dynamic computing power requirements.

[0114] The technical effect of the scheme in this embodiment is: by optimizing the first scheduling strategy, a more efficient second scheduling strategy is generated, thereby improving the overall performance of computing power scheduling. By identifying and selecting key target strategy factors and setting appropriate weights for them, fine-tuning of the scheduling strategy can be achieved. This optimization process can improve resource utilization and task execution efficiency, ensure that computing power resources can better match actual needs, and thus enhance the flexibility and responsiveness of the system to meet computing power requirements in a dynamic environment.

[0115] In a possible design, S2031, according to the first scheduling strategy, determining at least one target strategy factor from a plurality of preset strategy factors, including:

[0116] S20311. Obtain a scheduling target of a first scheduling strategy.

[0117] Specifically, the scheduling target of the first scheduling strategy can be obtained by analyzing the core purpose and expected results of the first scheduling strategy. By clarifying these scheduling targets, policy factors can be more effectively selected and adjusted to optimize the performance of the scheduling strategy. This process can improve the efficiency and accuracy of computing power scheduling, ensure that resource allocation is highly matched with actual demand, and thus improve the performance and resource utilization of the overall system.

[0118] S20312. Calculate the correlation between the scheduling target and each strategy factor respectively.

[0119] Specifically, the correlation between the scheduling objective and each policy factor can be calculated separately through analytical methods or machine learning algorithms. This usually involves using correlation coefficients to quantify the linear relationship between the scheduling objective and each policy factor, or calculating the degree of influence of the factor on the objective through training models. This process can identify and select the policy factors that have the greatest impact on the scheduling objective, thereby optimizing the scheduling strategy, improving the accuracy and efficiency of resource allocation, and ultimately enhancing the overall performance and responsiveness of the system.

[0120] S20313. Sort the multiple strategy factors in descending order according to the relevance of each strategy factor, and select a preset number of strategy factors from the sorting results as target strategy factors.

[0121] Specifically, we can calculate the correlation score between each policy factor and the scheduling target, and then sort these factors from high to low according to the correlation score. From the sorting results, we select the first several factors with the highest correlation as the target policy factors. This process can give priority to the policy factors that have the greatest impact on the scheduling target, thereby optimizing the design of the scheduling strategy, improving the efficiency and effectiveness of resource allocation, and ensuring that the system meets the computing power requirements.

[0122] The technical effect of the solution in this embodiment is that by identifying and selecting policy factors that are highly relevant to the scheduling target, the computing power scheduling strategy can be significantly optimized. This process ensures that the adjustment of the scheduling strategy is based on the factors that have the greatest impact on system performance, thereby improving the accuracy and efficiency of resource allocation. Ultimately, this optimization can improve the overall performance of the system, meet computing power requirements while reducing resource waste, and achieve higher response speed and cost-effectiveness.

[0123] In a possible design, the fourth cloud pool refers to any one of the multiple cloud pools.

[0124] For the fourth cloud pool, S204, according to the second scheduling strategy, respectively calculate the degree of fit between the solution and each preset cloud pool, and generate a scheduling scheme for each cloud pool generated according to the solution, including:

[0125] S2041. Obtain multiple configuration parameters of the fourth cloud pool and the weight of each configuration parameter in the second scheduling strategy; wherein the multiple configuration parameters include resource parameters, storage parameters, cost parameters, and location parameters.

[0126] Specifically, relevant parameter information can be dynamically obtained by accessing and analyzing the configuration management database or interface of the cloud pool. These parameters may include resource parameters, storage parameters, cost parameters, and location parameters. The weight of each parameter in the second scheduling strategy can be calculated by a pre-set algorithm or machine learning model to reflect its importance in a specific scheduling requirement. This process can improve the accuracy and efficiency of resource scheduling, ensure that computing power requirements can be met in the most appropriate cloud pool, thereby optimizing overall resource utilization and reducing operating costs.

[0127] S2042. Calculate the degree of fit of the fourth cloud pool according to multiple configuration parameters and the weight of each configuration parameter.

[0128] Specifically, this can be done by building a weighted scoring model. Each configuration parameter is weighted according to its weight in the second scheduling strategy, and then these weighted values ​​are aggregated to obtain a total score, which represents the fit of the fourth cloud pool. The model can be implemented through linear weighted summation or other multi-factor analysis methods. This treatment can comprehensively consider the importance of different configuration parameters, so as to more accurately evaluate the degree of match between the cloud pool and the computing power requirements, ensure that the selected cloud pool can optimally meet specific scheduling requirements, and improve resource utilization efficiency and service quality.

[0129] S2043. Generate a scheduling plan for the fourth cloud pool based on the solution and the compatibility of the fourth cloud pool.

[0130] Specifically, this can be accomplished through an automated scheduling engine that combines the specific needs of the solution with the fit score of the fourth cloud pool to generate an optimal scheduling solution. This process may involve matching resource requirements with cloud pool capabilities, optimizing task scheduling, and considering factors such as network topology and data transmission. By comprehensively analyzing solution requirements and cloud pool capabilities, the generated scheduling solution can ensure efficient resource utilization and fast task execution, thereby improving overall system performance and user satisfaction.

[0131] The technical effect of the solution in this embodiment is: by analyzing and calculating the configuration parameters of multiple cloud pools, the compatibility of each cloud pool with the solution can be accurately evaluated, so as to select the most suitable cloud pool for resource scheduling. This method not only improves the accuracy and efficiency of resource allocation, but also optimizes cost and performance, thereby improving the overall operating efficiency and service quality of the system.

[0132] Figure 3 Schematic diagram of the process of computing power scheduling method provided in the embodiment of the present application Figure 2 In this embodiment, Figure 2 Based on the provided embodiments, the computing power scheduling method is further explained. The computing power scheduling method includes:

[0133] S301. Obtain information of multiple cloud regions; wherein the cloud region information refers to attribute information of computing resource facilities.

[0134] Specifically, multiple cloud region information can be obtained by calling the cloud service provider's interface, which provides detailed information about each cloud region, such as geographic location, available resource types, network latency, and bandwidth. By obtaining this information, the system can fully understand the computing resource characteristics of different cloud regions, so as to make more accurate and optimized decisions in the subsequent cloud pool creation and scheduling strategy selection process. This processing can improve the efficiency of computing scheduling and resource utilization, while reducing performance bottlenecks and cost increases caused by mismatched resource selection.

[0135] S302. Create multiple second cloud pools based on information of multiple cloud regions; wherein the second cloud pool refers to a collection of computing resources that are initially created.

[0136] Specifically, multiple second cloud pools can be created by analyzing and integrating cloud region information obtained from cloud service providers. Based on the resource attributes, geographic location, network conditions and other information of each cloud region, the system brings together resources with similar characteristics or that meet specific needs to form multiple preliminary second cloud pools. This processing can reasonably classify and organize computing resources at the initial stage, providing a basis for subsequent resource scheduling and optimization, thereby improving resource utilization efficiency and scheduling flexibility.

[0137] S303. Configure the parameters of each second cloud pool to obtain a third cloud pool corresponding to each second cloud pool; wherein the third cloud pool refers to a set of computing resources after parameter configuration.

[0138] Specifically, the third cloud pool corresponding to each second cloud pool can be obtained by adjusting and optimizing the resource parameters of each second cloud pool. This includes configuring computing power, storage capacity, network bandwidth, and security settings to meet the needs of specific applications or workloads. Through this configuration, the second cloud pool is transformed into a more targeted third cloud pool. This processing can improve the adaptability and performance of the cloud pool, enabling it to better support specific computing tasks or application scenarios, thereby optimizing the efficiency and effectiveness of the overall computing power scheduling.

[0139] S304: Configure the network quality parameters between any two third cloud pools respectively to obtain multiple cloud pools.

[0140] Specifically, multiple cloud pools can be obtained by setting and adjusting the network connection characteristics between the third cloud pools. This includes setting parameters such as network bandwidth, latency, jitter, and data transmission rate to ensure that the communication between different cloud pools meets the expected performance standards. By optimizing these network quality parameters, multiple optimized cloud pool sets can be formed. This processing can improve the efficiency and reliability of data transmission across cloud pools, reduce network bottlenecks and latency, and thus enhance the performance and stability of the overall computing power scheduling system.

[0141] S305. Create a solution based on the preset computing power requirements; wherein the solution is used to plan computing power scheduling.

[0142] S306. According to the solution, determine a first scheduling strategy from a plurality of preset scheduling strategies; wherein the scheduling strategy is used to optimize computing power scheduling, and the first scheduling strategy refers to a scheduling strategy among the plurality of scheduling strategies that has the highest degree of matching with the solution.

[0143] S307: Optimize the first scheduling strategy to obtain a second scheduling strategy.

[0144] S308. According to the second scheduling strategy, respectively calculate the degree of fit between the solution and each preset cloud pool, and generate a scheduling plan for each cloud pool generated according to the solution; wherein the scheduling plan is a collection of steps to implement the solution.

[0145] S309: From the multiple cloud pools, determine the cloud pool with the greatest degree of fit as the first cloud pool, and determine the scheduling scheme corresponding to the first cloud pool as the target scheduling scheme.

[0146] S310: deploy the target scheduling solution on the first cloud pool to obtain a resource instance that meets the computing power requirements.

[0147] S305 - S310 are similar to S201 - S206 and will not be described in detail in this embodiment.

[0148] S311. Monitor the operating status of resource instances.

[0149] Specifically, the operating status of resource instances can be monitored through integrated monitoring tools, which can collect and analyze the performance indicators of resource instances in real time, such as memory consumption, network traffic, and response time. By setting alarm rules and automated monitoring scripts, the system can detect abnormal status or performance bottlenecks of resource instances in a timely manner. This processing can ensure the efficient use and stable operation of computing resources, and provide data support for subsequent configuration adjustments, thereby optimizing overall system performance and user experience.

[0150] S312. Adjust the configuration of the resource instance according to the operating status.

[0151] Specifically, the configuration of resource instances can be adjusted through an automated resource management system, which dynamically adjusts instance configuration parameters based on real-time monitored resource instance performance indicators and preset policies, such as expanding or reducing the number of cores, adjusting memory size, and changing network bandwidth. This adjustment can be performed through automated scripts or policy-based scheduling engines. This processing can improve the adaptability and elasticity of resource instances, enabling them to be dynamically adjusted according to actual load requirements, thereby optimizing resource utilization, reducing operating costs, and improving the overall performance and reliability of the system.

[0152] The technical effect of the scheme in this embodiment is: by systematically acquiring and analyzing the information of multiple cloud regions, creating and optimizing the process of cloud pools, the scheduling of computing resources is made more accurate and efficient. First, by acquiring the information of cloud regions, the resource characteristics and network conditions of each region can be fully understood, providing data support for the subsequent creation of cloud pools. Then, by creating a second cloud pool and configuring its parameters, a third cloud pool is formed, so that the resource configuration is more in line with actual needs. Finally, by optimizing the network quality parameters between the third cloud pools, efficient communication across cloud pools is ensured. This processing can improve resource utilization, reduce scheduling delays and costs, and enhance the adaptability and flexibility of the system in different application scenarios, thereby achieving optimal scheduling and management of computing resources; by continuously monitoring the operating status of resource instances and dynamically adjusting the configuration, the automation and intelligence of resource management are realized. Real-time monitoring ensures a comprehensive understanding of the performance of resource instances, and can promptly discover and respond to abnormal situations. Adjusting the configuration according to the operating status enables the system to flexibly adapt to load changes and optimize resource allocation. This process not only improves resource utilization efficiency and system stability, but also reduces operation and maintenance costs, enhances the system's responsiveness and service quality under different workloads, thereby providing users with more reliable and efficient computing services.

[0153] Figure 4 This is a schematic diagram of the structure of the computing power scheduling device provided in the embodiment of the present application. Figure 4 As shown, the computing power scheduling device includes:

[0154] The creation module 401 is used to create a solution according to the preset computing power requirements; wherein the solution is used to plan computing power scheduling.

[0155] The first determination module 402 is used to determine a first scheduling strategy from a plurality of preset scheduling strategies according to the solution; wherein the scheduling strategy is used to optimize computing power scheduling, and the first scheduling strategy refers to a scheduling strategy that has the highest degree of matching with the solution among the plurality of scheduling strategies.

[0156] The optimization module 403 is used to optimize the first scheduling strategy to obtain a second scheduling strategy.

[0157] The calculation module 404 is used to calculate the compatibility of the solution with each preset cloud pool according to the second scheduling strategy, and generate a scheduling plan for each cloud pool generated according to the solution; wherein the scheduling plan is a collection of steps to implement the solution.

[0158] The second determination module 405 is used to determine the cloud pool with the greatest degree of fit from multiple cloud pools as the first cloud pool, and determine the scheduling scheme corresponding to the first cloud pool as the target scheduling scheme.

[0159] The deployment module 406 is used to deploy the target scheduling solution on the first cloud pool to obtain a resource instance of the computing power requirement.

[0160] In a possible design, the optimization module 403 includes:

[0161] A determination unit is used to determine at least one target strategy factor from a plurality of preset strategy factors according to the first scheduling strategy; wherein the strategy factor is used to optimize the first scheduling strategy.

[0162] The weight setting unit is used to set the weight for each target strategy factor.

[0163] The strategy generating unit is used to generate a second scheduling strategy according to the first scheduling strategy, at least one target strategy factor, and a weight of each target strategy factor.

[0164] In one possible design, the determination unit includes:

[0165] The acquisition component is used to obtain the scheduling target of the first scheduling strategy.

[0166] The calculation component is used to calculate the correlation between the scheduling target and each strategy factor respectively.

[0167] The determination component is used to sort the multiple strategy factors in descending order according to the relevance of each strategy factor, and select a preset number of strategy factors from the sorting results as target strategy factors.

[0168] In a possible design, the fourth cloud pool refers to any one of the multiple cloud pools.

[0169] For the fourth cloud pool, the computing module 404 includes:

[0170] The acquisition unit is used to acquire multiple configuration parameters of the fourth cloud pool and the weight of each configuration parameter in the second scheduling strategy; wherein the multiple configuration parameters include resource parameters, storage parameters, cost parameters and location parameters.

[0171] The calculation unit is used to calculate the compatibility of the fourth cloud pool according to multiple configuration parameters and the weight of each configuration parameter.

[0172] The solution generating unit is used to generate a scheduling solution for the fourth cloud pool according to the solution and the compatibility of the fourth cloud pool.

[0173] In a possible design, the computing power scheduling device further includes:

[0174] The acquisition module is used to obtain information of multiple cloud regions; wherein the cloud region information refers to the attribute information of computing resource facilities.

[0175] The cloud pool creation module is used to create multiple second cloud pools based on multiple cloud region information; wherein the second cloud pool refers to a collection of computing power resources that are initially created.

[0176] The first configuration module is used to configure the parameters of each second cloud pool to obtain a third cloud pool corresponding to each second cloud pool; wherein the third cloud pool refers to a set of computing resources after parameter configuration.

[0177] The second configuration module is used to configure the network quality parameters between any two third cloud pools respectively to obtain multiple cloud pools.

[0178] In a possible design, the computing power scheduling device further includes:

[0179] The monitoring module is used to monitor the operating status of resource instances.

[0180] The adjustment module is used to adjust the configuration of resource instances according to the running status.

[0181] The computing power scheduling device provided in this embodiment can execute Figures 2 to 4 The technical solution of the method embodiment shown in the figure has the same implementation principle and technical effect as Figures 2 to 4 The method embodiments shown are similar and will not be described in detail here.

[0182] Figure 5 The hardware structure diagram of the electronic device provided in the embodiment of the present application is shown in FIG. Figure 5 As shown, the electronic device includes: at least one processor 510 and a memory 520. The electronic device also includes a communication component 530. The processor 510, the memory 520 and the communication component 530 are connected via a bus 540.

[0183] During the specific implementation process, at least one processor 510 executes the computer execution instructions stored in the memory 520, so that at least one processor 510 is used to implement the computing power scheduling method of the above embodiment.

[0184] The specific implementation process of the processor 510 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0185] In the above embodiments, it should be understood that the processor 510 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The steps of the method disclosed in the invention may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0186] The memory 520 may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk storage.

[0187] The bus 540 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus 540 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus 540 in the drawings of the present application is not limited to only one bus or one type of bus.

[0188] The above-mentioned functions implemented by the electronic device and the main control device introduce the scheme provided by the embodiment of the present invention. It can be understood that in order to implement the above-mentioned functions, the electronic device or the main control device includes a hardware structure and / or software module corresponding to the execution of each function. In combination with the units and algorithm steps of each example described in the embodiment disclosed in the embodiment of the present invention, the embodiment of the present invention can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the technical solution of the embodiment of the present invention.

[0189] The embodiment of the present application also provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the computing power scheduling method of the above embodiment. In the specific implementation of the above computing power scheduling method, each module can be implemented as a processor.

[0190] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0191] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in an electronic device or a main control device.

[0192] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the computing power scheduling method of the above embodiment.

[0193] The computer program is stored in a readable storage medium. At least one processor can read the computer program from the readable storage medium. At least one processor executes the computer program to execute the solution provided in any of the above embodiments.

[0194] Those skilled in the art can understand that all or part of the steps of the above method embodiments can be completed by hardware related to program instructions. The above program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above method embodiments are executed; and the above storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0195] So far, the technical solution of the present application has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments, and the above embodiments are only used to illustrate the technical solution of the present application rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A computing power scheduling method, characterized in that: The method comprises: Create a solution based on the preset computing power requirements; wherein the solution is used to plan computing power scheduling; According to the solution, determine a first scheduling strategy from a plurality of preset scheduling strategies; wherein the scheduling strategy is used to optimize the computing power scheduling, and the first scheduling strategy refers to a scheduling strategy among the plurality of scheduling strategies that has the highest degree of matching with the solution; Optimizing the first scheduling strategy to obtain a second scheduling strategy; According to the second scheduling strategy, respectively calculate the degree of fit between the solution and each preset cloud pool, and generate a scheduling scheme generated by each cloud pool according to the solution; wherein the scheduling scheme is a set of steps to implement the solution; From the plurality of cloud pools, determining the cloud pool with the greatest degree of fit as the first cloud pool, and determining the scheduling scheme corresponding to the first cloud pool as the target scheduling scheme; The target scheduling solution is deployed on the first cloud pool to obtain a resource instance that meets the computing power requirement.

2. The computing power scheduling method according to claim 1, characterized in that: Before creating a solution according to the preset computing power requirement, the method further includes: Acquire multiple cloud region information; wherein the cloud region information refers to attribute information of computing resource facilities; Creating multiple second cloud pools according to the multiple cloud region information; wherein the second cloud pool refers to a collection of computing resources that are initially created; Configuring the parameters of each of the second cloud pools to obtain a third cloud pool corresponding to each of the second cloud pools; wherein the third cloud pool refers to a set of computing resources after parameter configuration; The network quality parameters between any two of the third cloud pools are configured respectively to obtain a plurality of the cloud pools.

3. The computing power scheduling method according to claim 1, characterized in that: The optimizing the first scheduling strategy to obtain a second scheduling strategy includes: According to the first scheduling strategy, determining at least one target strategy factor from a plurality of preset strategy factors; wherein the strategy factor is used to optimize the first scheduling strategy; Setting a weight for each of the target strategy factors; The second scheduling policy is generated according to the first scheduling policy, the at least one target policy factor, and the weight of each target policy factor.

4. The computing power scheduling method according to claim 3, characterized in that: The determining, according to the first scheduling strategy, at least one target strategy factor from a plurality of preset strategy factors comprises: Obtaining a scheduling target of the first scheduling strategy; Calculating the correlation between the scheduling target and each of the strategy factors respectively; According to the relevance of each of the policy factors, the multiple policy factors are sorted in descending order, and a preset number of policy factors are selected from the sorting results as the target policy factors.

5. The computing power scheduling method according to claim 1, characterized in that: The fourth cloud pool refers to any one of the plurality of cloud pools; For the fourth cloud pool, the step of calculating the degree of fit between the solution and each preset cloud pool according to the second scheduling strategy, and generating a scheduling scheme for each cloud pool according to the solution includes: Acquire multiple configuration parameters of the fourth cloud pool and the weight of each of the configuration parameters in the second scheduling strategy; wherein the multiple configuration parameters include resource parameters, storage parameters, cost parameters and location parameters; Calculating the degree of fit of the fourth cloud pool according to the multiple configuration parameters and the weight of each of the configuration parameters; A scheduling plan for the fourth cloud pool is generated according to the solution and the degree of compatibility of the fourth cloud pool.

6. The computing power scheduling method according to claim 1, characterized in that: After the target scheduling solution is deployed on the first cloud pool to obtain the resource instance of the computing power requirement, the method further includes: Monitoring the running status of the resource instance; According to the running status, the configuration of the resource instance is adjusted.

7. A computing power scheduling device, characterized in that: include: A creation module, used to create a solution according to the preset computing power requirements; wherein the solution is used to plan computing power scheduling; A first determination module, configured to determine a first scheduling strategy from a plurality of preset scheduling strategies according to the solution; wherein the scheduling strategy is used to optimize the computing power scheduling, and the first scheduling strategy refers to a scheduling strategy among the plurality of scheduling strategies that has the highest degree of matching with the solution; An optimization module, used for optimizing the first scheduling strategy to obtain a second scheduling strategy; A calculation module, used to calculate the degree of fit between the solution and each preset cloud pool according to the second scheduling strategy, and generate a scheduling scheme generated by each cloud pool according to the solution; wherein the scheduling scheme is a set of steps to implement the solution; A second determination module is used to determine the cloud pool with the greatest degree of fit from the multiple cloud pools as the first cloud pool, and determine the scheduling scheme corresponding to the first cloud pool as the target scheduling scheme; A deployment module is used to deploy the target scheduling solution on the first cloud pool to obtain a resource instance of the computing power requirement.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; When the processor executes the computer-executable instructions stored in the memory, it is used to implement the computing power scheduling method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the computing power scheduling method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, it is used to implement the computing power scheduling method according to any one of claims 1 to 6.

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