Resource scheduling efficiency optimization method and system for IaaS cloud platform

Abstract

The invention relates to the technical field of cloud platform resource scheduling, in particular to an IaaS cloud platform-oriented resource scheduling efficiency optimization method and system, and the method comprises the steps: formulating a global resource scheduling strategy according to a unified view and task classification of cross-domain resources; carrying out local resource allocation and scheduling according to resource conditions and local task requirements in each domain; and establishing an evaluation index system of the resource scheduling efficiency, and regularly evaluating, analyzing and optimizing the performance of the resource scheduling. The IaaS cloud platform resource scheduling method has the beneficial effects that the resource scheduling efficiency of the IaaS cloud platform is improved by constructing the resource use model and the index system, and the resource utilization rate is remarkably improved through accurate task allocation. The method surpasses a traditional scheduling algorithm, adopts an optimization algorithm specially designed for an IaaS cloud platform, and realizes performance improvement in multiple aspects.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud platform resource scheduling, and in particular to a resource scheduling efficiency optimization method and system for an IaaS cloud platform. Background Art

[0002] Cloud computing is an Internet-based computing method, through which cloud computing providers can provide shared software and hardware resources to various computer terminals and mobile devices on demand. Cloud computing usually includes three common service models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). In the IaaS service model, the allocation of computing resources is provided to users in the form of virtual machines, that is, the resource scheduling in the IaaS cloud platform is actually the dynamic migration of virtual machines.

[0003] In traditional IaaS cloud platforms, each resource domain is independent and lacks a unified management mechanism, making it difficult to fully understand resource usage, resulting in low scheduling efficiency and poor resource utilization. Task types are diverse and complex, and traditional single strategies are difficult to meet demand, which can easily lead to inappropriate task allocation. As the scale of the platform expands, cross-domain resource management and scheduling becomes more difficult, and factors such as network delays have a great impact, making it easy for problems such as uneven resource allocation to occur. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a resource scheduling efficiency optimization method and system for an IaaS cloud platform, which can solve the problems mentioned in the background technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a resource scheduling efficiency optimization method for an IaaS cloud platform, including formulating a global resource scheduling strategy based on a unified view of cross-domain resources and task classification;

[0009] Perform local resource allocation and scheduling based on the resource situation and local task requirements in each domain;

[0010] Establish an evaluation index system for resource scheduling efficiency, and regularly evaluate, analyze and optimize the performance of resource scheduling.

[0011] As a preferred solution of the resource scheduling efficiency optimization method for the IaaS cloud platform of the present invention, wherein: a unified view of cross-domain resources is established, including

[0012] By setting up multi-dimensional monitoring points, we can obtain resource information data of each domain in real time;

[0013] Integrate the collected cross-domain resource information data to remove redundant and erroneous data;

[0014] Establish mapping relationships between resources in different domains to associate and collaboratively manage resources.

[0015] As a preferred solution of the resource scheduling efficiency optimization method for IaaS cloud platform of the present invention, task classification includes:

[0016] Extract the required task features for tasks submitted to the IaaS cloud platform;

[0017] Quantify the extracted task features and assign specific values ​​and ranges;

[0018] Establish clear classification standards based on task characteristics and the resource characteristics and scheduling requirements of the IaaS cloud platform;

[0019] Classify tasks according to the classification criteria.

[0020] As a preferred solution of the resource scheduling efficiency optimization method for IaaS cloud platform of the present invention, the global resource scheduling strategy includes:

[0021] In-depth analysis of business and user needs of the IaaS cloud platform;

[0022] According to the analysis results, set the goals and performance indicators of the global resource scheduling strategy;

[0023] Build the resource model of the IaaS cloud platform and define the attributes, status, and availability of resources;

[0024] Determine and optimize the global resource scheduling algorithm of the IaaS cloud platform;

[0025] Establish a policy evaluation mechanism to regularly evaluate the effectiveness of global resource scheduling policies.

[0026] As a preferred solution of the resource scheduling efficiency optimization method for IaaS cloud platform of the present invention, local resource allocation and scheduling include

[0027] Real-time monitoring of local resource status information;

[0028] Prioritize tasks submitted to local resources;

[0029] Develop resource allocation strategies for local resources;

[0030] Select a scheduling algorithm that is suitable for local resource characteristics and task features;

[0031] Optimize the scheduling process of local resources and make dynamic adjustments and feedback.

[0032] As a preferred solution of the resource scheduling efficiency optimization method for the IaaS cloud platform of the present invention, the scheduling algorithm suitable for local resource characteristics and task characteristics includes:

[0033] Shortest job first algorithm, earliest deadline first algorithm and feedback-based scheduling algorithm.

[0034] As a preferred solution of the resource scheduling efficiency optimization method for the IaaS cloud platform of the present invention, an evaluation index system for resource scheduling efficiency is established, including:

[0035] Define key performance indicators;

[0036] Develop a monitoring mechanism and regularly collect relevant performance data of each resource node and task in the IaaS cloud platform;

[0037] Organize and analyze the collected performance data, and calculate the specific values ​​and trends of various performance indicators;

[0038] According to the performance evaluation results, formulate corresponding adjustment strategies and re-evaluate the performance of the adjusted resource scheduling system.

[0039] In a second aspect, the present invention provides a resource scheduling efficiency optimization system for an IaaS cloud platform, comprising: a formulation module for formulating a global resource scheduling strategy based on a unified view of cross-domain resources and task classification;

[0040] The allocation and scheduling module is used to perform local resource allocation and scheduling based on the resource conditions and local task requirements in each domain;

[0041] An evaluation module is established to establish an evaluation index system for resource scheduling efficiency, and to regularly evaluate, analyze and optimize the performance of resource scheduling.

[0042] In a third aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0043] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when the computer program is executed by a processor.

[0044] Compared with the prior art, the present invention has the following beneficial effects: by constructing a resource usage model and an indicator system, it not only improves the resource scheduling efficiency of the IaaS cloud platform, but also significantly improves resource utilization through precise task allocation. It surpasses the traditional scheduling algorithm and adopts an optimization algorithm designed specifically for the IaaS cloud platform to achieve multi-faceted performance improvements. For large-scale cross-domain IaaS cloud platforms, the present invention further enhances the cross-domain utilization efficiency of resources and the execution speed of tasks by realizing cross-domain perception and integration of resources, as well as dynamic adjustment of global and local scheduling strategies, thereby bringing significant operational benefits and technical advantages to the cloud platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0046] Figure 1 This is a flowchart of a resource scheduling efficiency optimization method for an IaaS cloud platform.

[0047] Figure 2 A schematic diagram of the internal structure of a computer device. DETAILED DESCRIPTION

[0048] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0049] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0050] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0051] Example 1

[0052] Reference Figure 1, which is the first embodiment of the present invention, and provides a resource scheduling efficiency optimization method for an IaaS cloud platform, which includes:

[0053] S1. Develop a global resource scheduling strategy based on a unified view of cross-domain resources and task classification.

[0054] Furthermore, a unified view of cross-domain resources is established, including

[0055] By setting up multi-dimensional monitoring points, the resource information data of each domain is obtained in real time. The monitoring points cover the resource status of different domains, providing a basis for subsequent resource management;

[0056] It should be noted that resource information data in different domains is obtained through detectors and interfaces. Further, the detector is used to scan and detect periodically, specifically:

[0057]

[0058] Among them, T refers to the detection period, I refers to the amount of resource information data detected each time, N refers to the total amount of resource information data, and T 总 Refers to the total detection time.

[0059] Integrate the collected cross-domain resource information data to remove redundant and erroneous data;

[0060] Establish mapping relationships between resources in different domains to associate and collaboratively manage resources.

[0061] It should be noted that as the cloud platform operates and resource status changes, the perception and integration results of cross-domain resources are updated and maintained in a timely manner.

[0062] Preferably, real-time data collection and transmission ensures the timeliness of resource information, so that the integration center can obtain the latest resource data of each domain in a timely manner. The integration and cleaning of resource information removes redundant and erroneous data, and the unified resource view formed is clearer and more accurate, which is convenient for managers to intuitively understand the cross-domain resource situation. Resource mapping and association establish the connection between resources in different domains, realize the collaborative management of resources, and break the resource isolation between domains. Dynamic update and maintenance can timely reflect the changes in resource status during the operation of the cloud platform, ensure that the perception and integration results of cross-domain resources are always consistent with the actual situation, thereby providing a reliable basis for subsequent resource scheduling, allocation and other operations, and effectively improving the utilization efficiency and management efficiency of cross-domain resources.

[0063] Further, the task classification includes

[0064] For tasks submitted to the IaaS cloud platform, the required task features are extracted, where the task features include task type (such as computing intensive, storage intensive, etc.), task priority, and the scale of resources required for the task.

[0065] Quantify the extracted task features and assign specific values ​​and ranges for subsequent classification and processing;

[0066] Based on the characteristics of tasks and the resource characteristics and scheduling requirements of the IaaS cloud platform, formulate clear classification standards, such as classifying tasks into high-priority computing tasks and low-priority storage tasks;

[0067] Classify tasks according to the classification criteria.

[0068] It should be noted that according to the classification standards, tasks with similar characteristics are divided into corresponding categories to ensure that each task can be accurately attributed to a certain category. Further, it also includes verifying the classified tasks, checking the accuracy and rationality of the classification, and ensuring that the classification results can effectively support subsequent resource scheduling optimization work.

[0069] Preferably, extracting task features can clarify the scale of resources required for the task, facilitating subsequent targeted management; quantifying task features can make them more operational and comparable; formulating classification standards can make classification more based on evidence and more in line with platform requirements, and further accurately classifying tasks can improve resource allocation efficiency; verifying classification results can ensure the accuracy and rationality of classification, provide a more reliable foundation for subsequent cross-domain task scheduling, and improve the operating efficiency and management level of the entire IaaS cloud platform.

[0070] Furthermore, the global resource scheduling strategy includes

[0071] In-depth analysis of business and user needs of the IaaS cloud platform;

[0072] It should be noted that business needs and user needs include resource demand characteristics of different types of tasks, service level agreements, etc. By analyzing the current resource usage and scheduling efficiency bottlenecks of the platform, the direction that needs improvement can be determined.

[0073] According to the analysis results, set the goals and performance indicators of the global resource scheduling strategy;

[0074] It should be noted that the goals include improving resource utilization, reducing response time, and improving service quality.

[0075] Build the resource model of the IaaS cloud platform and define the attributes, status, and availability of resources;

[0076] It should be noted that building a resource model suitable for the IaaS cloud platform includes the abstraction and description of computing resources, storage resources, network resources, etc. Further, by defining the attributes, status and availability of resources, a basic data structure is provided for resource scheduling.

[0077] Determine and optimize the global resource scheduling algorithm of the IaaS cloud platform;

[0078] It should be noted that the global resource scheduling algorithms suitable for IaaS cloud platforms include priority-based scheduling, load balancing-based scheduling, prediction-based scheduling, etc. When optimizing the selected scheduling algorithm, factors such as the algorithm's complexity, convergence, and adaptability should be considered to improve scheduling efficiency and performance.

[0079] Establish a policy evaluation mechanism to regularly evaluate the effectiveness of global resource scheduling policies, verifying the effectiveness of the policies through actual resource usage data and performance indicators.

[0080] It should be noted that it also includes timely adjustment and optimization of scheduling strategies based on the results of the evaluation to adapt to changes in the platform and changes in demand. On the one hand, by conducting experiments in the actual IaaS cloud platform environment, collecting experimental data, analyzing the performance of the strategy in different scenarios, and further optimizing the strategy to ensure its stability and reliability in the actual production environment; on the other hand, the optimized global resource scheduling strategy is deployed to the IaaS cloud platform, and a corresponding monitoring system is established to monitor the resource scheduling situation and performance indicators in real time, and to promptly handle problems and abnormal situations found in the monitoring to ensure the efficient operation of resource scheduling.

[0081] It is better to deeply understand the platform situation through demand analysis and accurately determine the direction of improvement; set clear goals and metrics to facilitate strategy effectiveness evaluation and monitoring; build a suitable resource model to provide basic data for scheduling; select and optimize scheduling algorithms to improve scheduling efficiency; strategy evaluation and adjustment can improve strategies in a timely manner; optimize strategies in actual environments through experimental verification; establish a monitoring system to control resource scheduling in real time, ensure efficient use and stable operation of platform resources, and improve the performance and management level of the entire IaaS cloud platform.

[0082] S2. Perform local resource allocation and scheduling based on the resource situation and local task requirements within each domain.

[0083] Further, local resource allocation and scheduling, including

[0084] Real-time monitoring of local resource status information;

[0085] It should be noted that the status information includes resource usage, idle status, performance indicators, etc.

[0086] Prioritize tasks submitted to local resources;

[0087] It should be noted that priority assessment can determine the priority level of a task based on a variety of factors, including the type of task, urgency, and requirements for service quality.

[0088] Develop resource allocation strategies for local resources;

[0089] It should be noted that the resource allocation strategy may be to allocate resources to high-priority tasks first, or to preempt or queue resources according to certain rules when resources are scarce.

[0090] Select a scheduling algorithm that is suitable for local resource characteristics and task features;

[0091] Further, scheduling algorithms suitable for local resource characteristics and task characteristics include

[0092] Shortest job first algorithm, earliest deadline first algorithm and feedback-based scheduling algorithm.

[0093] Optimize the scheduling process of local resources and make dynamic adjustments and feedback.

[0094] It should be noted that optimizing the scheduling process of local resources includes reducing scheduling delays, improving scheduling accuracy and timeliness, avoiding resource waste, etc. This can be further achieved by optimizing the execution process of the scheduling algorithm, reducing the time of scheduling decisions, etc.

[0095] It should be further explained that, according to the actual usage of local resources and the execution of tasks, the resource allocation strategy and scheduling algorithm parameters are adjusted in a timely manner to adapt to the changing resource requirements and task conditions. At the same time, feedback information during the scheduling process is collected to further optimize the effect of local resource scheduling.

[0096] Preferably, by real-time monitoring of resource status, the local resource status can be grasped, providing an accurate basis for subsequent decision-making; task priority assessment can reasonably arrange tasks and improve resource utilization efficiency; formulating resource allocation strategies can make resource allocation more targeted and reasonable; selecting appropriate scheduling algorithms can optimize the local resource scheduling effect; scheduling process optimization improves the smoothness and efficiency of scheduling; dynamic adjustment and feedback can adjust strategies in time according to actual conditions to ensure that local resources are always in the best utilization state, thereby improving the performance and service quality of the entire system and ensuring user experience.

[0097] It should be further explained that the method also includes real-time monitoring of the resource usage and task execution status of the cross-domain platform, and dynamic resource adjustment according to the changes. Specifically, real-time monitoring of the resource usage and task execution status of the cross-domain platform can be achieved by periodically collecting resource indicator data, specifically:

[0098]

[0099] Where, T represents the total monitoring time, T 1 Represents the acquisition period, N 1 Represents the total number of resource indicators, and M represents the number of resource indicators collected each time.

[0100] S3. Establish an evaluation index system for resource scheduling efficiency, and regularly evaluate, analyze and optimize the performance of resource scheduling.

[0101] Furthermore, an evaluation index system for resource scheduling efficiency is established, including

[0102] Define key performance indicators;

[0103] It should be noted that key performance indicators include average response time, resource utilization, task completion rate, etc.

[0104] Develop a monitoring mechanism and regularly collect relevant performance data of each resource node and task in the IaaS cloud platform;

[0105] Organize and analyze the collected performance data, and calculate the specific values ​​and trends of various performance indicators;

[0106] It should be noted that by comparing performance indicators in different time periods and under different scheduling strategies, the changes in resource scheduling efficiency can be evaluated and performance bottlenecks or problematic areas can be identified, such as excessive or low utilization of certain resource nodes, and long response time of certain tasks.

[0107] According to the performance evaluation results, the corresponding adjustment strategy is formulated, and the performance of the adjusted resource scheduling system is re-evaluated. The performance evaluation results are presented to the resource scheduling team and relevant managers in an intuitive manner.

[0108] It should be noted that the formulation of adjustment strategies is based on the actual situation of the platform and business needs.

[0109] Preferably, through the definition and collection of key performance indicators, the evaluation standards can be clarified and accurate data can be obtained, laying the foundation for subsequent analysis. The analysis and evaluation of performance data can clearly understand the changing trends of resource scheduling efficiency and facilitate the discovery of problems. The timely presentation of evaluation results can enable relevant personnel to quickly understand the situation and formulate targeted adjustment strategies, which will help to continuously optimize resource scheduling, improve the overall performance and efficiency of the IaaS cloud platform, enhance user experience and satisfaction, and better adapt to changes in business needs.

[0110] In summary, the beneficial effects of a resource scheduling efficiency optimization method for IaaS cloud platform are that by constructing a resource usage model and an indicator system, it not only improves the resource scheduling efficiency of the IaaS cloud platform, but also significantly improves resource utilization through precise task allocation. It surpasses the traditional scheduling algorithm and adopts an optimization algorithm designed specifically for the IaaS cloud platform to achieve multi-faceted performance improvement. For large-scale cross-domain IaaS cloud platforms, the present invention further enhances the cross-domain utilization efficiency of resources and the execution speed of tasks by realizing cross-domain perception and integration of resources, as well as dynamic adjustment of global and local scheduling strategies, thereby bringing significant operational benefits and technical advantages to the cloud platform.

[0111] Example 2

[0112] This embodiment provides a resource scheduling efficiency optimization system for an IaaS cloud platform, which includes a formulation module for formulating a global resource scheduling strategy based on a unified view of cross-domain resources and task classification;

[0113] The allocation and scheduling module is used to perform local resource allocation and scheduling based on the resource conditions and local task requirements in each domain;

[0114] An evaluation module is established to establish an evaluation index system for resource scheduling efficiency, and to regularly evaluate, analyze and optimize the performance of resource scheduling.

[0115] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0116] Example 3

[0117] This embodiment provides a computer device, which may be a terminal, and its internal structure diagram may be as follows: Figure 2As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a resource scheduling efficiency optimization method for an IaaS cloud platform is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse.

[0118] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements: formulating a global resource scheduling strategy based on a unified view of cross-domain resources and task classification; performing local resource allocation and scheduling based on the resource conditions and local task requirements in each domain; establishing an evaluation index system for resource scheduling efficiency, and regularly evaluating, analyzing and optimizing the performance of resource scheduling.

[0119] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A resource scheduling efficiency optimization method for an IaaS cloud platform, characterized in that: include, Formulate a global resource scheduling strategy based on a unified view of cross-domain resources and task classification; Perform local resource allocation and scheduling based on the resource situation and local task requirements in each domain; Establish an evaluation index system for resource scheduling efficiency, and regularly evaluate, analyze and optimize the performance of resource scheduling.

2. The resource scheduling efficiency optimization method for an IaaS cloud platform according to claim 1, characterized in that: Establish a unified view of cross-domain resources, including By setting up multi-dimensional monitoring points, we can obtain resource information data of each domain in real time; Integrate the collected cross-domain resource information data to remove redundant and erroneous data; Establish mapping relationships between resources in different domains to associate and collaboratively manage resources.

3. The resource scheduling efficiency optimization method for an IaaS cloud platform according to claim 2, characterized in that: The task classification includes Extract the required task features for tasks submitted to the IaaS cloud platform; Quantify the extracted task features and assign specific values ​​and ranges; Establish clear classification standards based on task characteristics and the resource characteristics and scheduling requirements of the IaaS cloud platform; The tasks are classified according to the classification criteria.

4. The resource scheduling efficiency optimization method for an IaaS cloud platform as claimed in claim 3, characterized in that: The global resource scheduling strategy includes: In-depth analysis of business and user needs of the IaaS cloud platform; According to the analysis results, set the goals and performance indicators of the global resource scheduling strategy; Build the resource model of the IaaS cloud platform and define the attributes, status, and availability of resources; Determine and optimize the global resource scheduling algorithm of the IaaS cloud platform; Establish a policy evaluation mechanism to regularly evaluate the effectiveness of global resource scheduling policies.

5. The resource scheduling efficiency optimization method for an IaaS cloud platform according to claim 4, characterized in that: The local resource allocation and scheduling includes Real-time monitoring of local resource status information; Prioritize tasks submitted to local resources; Develop resource allocation strategies for local resources; Select a scheduling algorithm that is suitable for local resource characteristics and task features; Optimize the scheduling process of local resources and make dynamic adjustments and feedback.

6. The resource scheduling efficiency optimization method for an IaaS cloud platform according to claim 5, characterized in that: The scheduling algorithm suitable for local resource characteristics and task characteristics includes: Shortest job first algorithm, earliest deadline first algorithm and feedback-based scheduling algorithm.

7. The resource scheduling efficiency optimization method for an IaaS cloud platform according to any one of claims 1 to 6, characterized in that: The resource scheduling efficiency evaluation index system is established, including: Define key performance indicators; Develop a monitoring mechanism and regularly collect relevant performance data of each resource node and task in the IaaS cloud platform; Organize and analyze the collected performance data, and calculate the specific values ​​and trends of various performance indicators; According to the performance evaluation results, formulate corresponding adjustment strategies and re-evaluate the performance of the adjusted resource scheduling system.

8. A resource scheduling efficiency optimization system for IaaS cloud platform, characterized in that: include: The formulation module is used to formulate global resource scheduling strategies based on the unified view of cross-domain resources and task classification; The allocation and scheduling module is used to perform local resource allocation and scheduling based on the resource conditions and local task requirements in each domain; An evaluation module is established to establish an evaluation index system for resource scheduling efficiency, and to regularly evaluate, analyze and optimize the performance of resource scheduling.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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