Calculation network multi-element fusion arrangement method

Abstract

The invention discloses a computing network multi-element fusion arrangement method, which comprises the following steps of: researching and designing a multi-element fusion arrangement mechanism, designing an arrangement mechanism aiming at business and SLA (Service Level Agreement) requirements, determining that the mechanism can support arrangement of a plurality of elements in a computing power network, researching and determining selection, scheduling and distribution strategies of resources, and performing multi-element fusion arrangement under different business requirements and environments. And decision-making and optimization are performed on the strategy, and artificial intelligence and big data technologies are combined to construct a multi-element fusion arrangement platform. According to the invention, by researching and designing a multi-element fusion arrangement mechanism, making a multi-element arrangement decision strategy, realizing a dynamic resource supply and demand matching technology and constructing a multi-element fusion arrangement platform, arrangement operation is carried out on multiple elements, and an arrangement platform for computing network service full life cycle management and service whole process visual management is developed at the same time. And the operation state of the monitoring and management service and the scheduling and configuration of the resources can be checked.

Description

Technical Field

[0001] The present invention relates to the field of multi-factor fusion and arrangement methods for computing networks, and in particular to a multi-factor fusion and arrangement method for computing networks. Background Art

[0002] A computing network is usually a new type of information infrastructure. It can usually allocate and flexibly schedule computing resources, storage resources, and network resources on demand between the cloud, network, and edge according to business needs. The computing network combines computing power, storage capacity, and network capabilities, and is managed and optimized through a unified platform to provide efficient, flexible, and scalable information technology services.

[0003] Computing-network fusion orchestration is based on the integrated development of communication network facilities and heterogeneous computing facilities, and uniformly orchestrates and controls multiple resources such as data, computing, and networks. Computing-network fusion orchestration involves the integration of elements. However, existing technologies for computing-network fusion orchestration usually lack a multi-element fusion orchestration platform when orchestrating multiple elements, and are unable to provide full lifecycle management of computing-network services and full-process visualization management of services, resulting in viewing problems. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for fusion and orchestration of multiple elements of a computing network to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for fusion orchestration of multiple elements in a computing network, comprising the following specific steps:

[0006] Step 1: Research and design a multi-element fusion orchestration mechanism. Based on business and SLA requirements, design an orchestration mechanism to ensure that it can support the orchestration of multiple elements in the computing network.

[0007] Step 2: Develop a multi-factor orchestration decision-making strategy to research and determine resource selection, scheduling, and allocation strategies, taking into account resource availability, real-time performance, and cost constraints. Make decisions and optimize strategies based on different business needs and environments.

[0008] Step 3: Implement dynamic resource supply and demand matching technology. Combining artificial intelligence and big data technologies, we will establish a dynamic supply and demand matching model between computing network business needs and computing network infrastructure resources. This model will monitor and analyze changes in business needs and the status of infrastructure resources in real time.

[0009] Step 4: Build a multi-factor integrated orchestration platform and develop an orchestration platform for full lifecycle management and full visualization management of computing network services, so that the operating status of monitoring and management services as well as resource scheduling and configuration can be viewed.

[0010] Preferably, the research and design of the multi-factor fusion orchestration mechanism in step one includes demand analysis and goal setting, factor identification and modeling, orchestration mechanism setting and monitoring and management. The demand analysis and goal setting operation method is to deeply understand the business needs, including the usage scenarios, business types and performance requirements of the computing power network, and make SLA requirements for resource allocation, performance and reliability, and then set the design goals of the multi-factor fusion orchestration mechanism based on the demand analysis results.

[0011] Preferably, the operation mode of element identification and modeling is to identify the key elements in the computing network, which include computing resources, network resources, storage resources and security resources, and then perform modeling operations on each element, and model the attributes, states and relationships of the elements. The operation mode of setting the orchestration mechanism is to formulate a strategy for multi-element fusion orchestration based on business needs and goals, and then design the process of the orchestration mechanism. The process includes resource application, resource allocation, resource scheduling and resource release, and develop algorithms and models that support the orchestration mechanism. The algorithm can be a resource allocation algorithm and a scheduling algorithm. The algorithm formula for the resource allocation weight can be:

[0012] P_i,k(t)=frac{r_i,k(t)}{R_i(t)}

[0013] where r_i,k(t) is the rate that user i can reach in resource block k at time t, and R_i(t) is the average rate of user i in a time window T_c at time t.

[0014] Preferably, the monitoring and management operation mode is to deploy the orchestration mechanism into the production environment, configure and debug it, and then establish a monitoring and management mechanism to monitor and manage the operation status of the orchestration mechanism in real time, and pay attention to the corresponding indicators and any anomalies and failures that occur during the monitoring process.

[0015] Preferably, the formulation of a multi-factor orchestration decision strategy in step 2 includes determining the decision goal and analyzing the background, identifying and evaluating factors, formulating alternative plans and implementing them, and establishing a monitoring mechanism. The method of determining the decision goal and analyzing the background operation is to clarify the specific goals that the decision is expected to achieve. The specific goals can be improving resource utilization, reducing costs, and optimizing business processes, and then understanding the current organizational environment, market environment, and technical environment, as well as the impact of the environment on the decision.

[0016] Preferably, the method of factor identification and evaluation is to list all factors related to the decision, including resources, technology, personnel, time and cost, and then conduct a detailed evaluation of each factor, including its importance, feasibility and scope of impact. The method of formulating and implementing alternative plans is to conceive multiple solutions based on the results of the factor evaluation, and to conduct a preliminary screening of the conceived plans to exclude plans that are obviously not feasible for the decision-making goals. The method of establishing a monitoring mechanism is to establish an effective monitoring mechanism, monitor the implementation process in real time to ensure smooth progress, and make necessary adjustments and optimizations to the plan according to the monitoring results and actual conditions during the implementation process.

[0017] Preferably, the technology for implementing dynamic resource supply and demand matching in step three includes clarifying business needs and evaluating the current situation, building a technology platform and integrating data, building a supply and demand matching model, specifying and allocating dynamic resource scheduling strategies, and establishing a monitoring mechanism and feedback mechanism. The operation method of clarifying business needs and evaluating the current situation is to deeply understand the specific needs of the business, including the demand and demand pattern of computing power, storage and network resources, and analyze the dynamic changes in business needs, and then conduct a comprehensive evaluation of currently available resources. The evaluation content includes the type, quantity and performance of resources, and analyzes the availability, real-time and cost constraints of resources. The operation method of building a technology platform and integrating data is to select a suitable technology platform. The technology platform can be a cloud computing platform and a big data processing platform to support the implementation of dynamic resource supply and demand matching, and integrate business demand data and resource status data to ensure the accuracy and completeness of the data, and then clean and pre-process the data.

[0018] Preferably, the operation mode of constructing the supply and demand matching model is to design a supply and demand matching model according to business needs and resource status. The matching model includes a matching algorithm and a matching strategy, and takes into account the availability, real-time and cost constraints of resources. The operation mode of specifying and allocating the dynamic resource scheduling strategy is to formulate a resource scheduling strategy according to the results of the supply and demand matching model. The resource scheduling strategy includes resource allocation and scheduling priority. Then, according to changes in business needs and changes in resource status, the resource scheduling strategy is adjusted in real time, and dynamic scheduling and allocation of resources are realized through automated tools or systems. The operation mode of establishing the monitoring mechanism and feedback mechanism is to establish an effective monitoring mechanism to monitor the resource supply and demand matching process in real time, monitor resource utilization and business response time indicators, as well as possible anomalies and failures, and then establish a feedback mechanism to collect user feedback on the resource supply and demand matching effect, and adjust and optimize the supply and demand matching model and resource scheduling strategy based on the feedback.

[0019] Preferably, the construction of a multi-factor fusion orchestration platform in step four includes clarifying the platform and conducting platform planning, technology selection and architecture design, functional module development and integration, data management and interface design, and platform deployment and operation and maintenance. The operation method of clarifying the platform and conducting platform planning is to deeply understand business needs. Business needs include the needs for computing power, storage and network resources, as well as the specific requirements of the business for resource orchestration. According to the results of the demand analysis, the overall plan and goals of the platform construction are formulated, and the functional modules, technical architecture and key elements of the data flow of the platform are determined. The operation method of the technology selection and architecture design is to select a suitable technology stack, which includes programming languages, databases, middleware and container technologies, and then design the overall architecture of the platform. The overall structure includes the front-end interface, back-end services, data storage and resource management modules, and determines the interaction mode and data flow path between modules.

[0020] Preferably, the operation mode of the functional module development and integration is to develop various functional modules according to the platform planning. The functional modules can be resource management, orchestration and scheduling, monitoring and alarm, and log auditing. Then the various functional modules are integrated to ensure data interaction and collaborative work between modules, and integration testing is performed to verify the overall function and performance of the platform. The operation mode of the data management and interface design is to design a data model. The data model includes resource data, business data and configuration data. Then, data storage, query, update and deletion operations can be realized, and the interface between the platform and the external system is designed. The interface can be API and SDK. The operation mode of the platform deployment and operation and maintenance is to prepare the deployment environment, including servers, networks and storage resources, and then deploy the platform to the production environment, and perform necessary configuration and debugging, and then establish an operation and maintenance management mechanism, which includes monitoring, alarm, backup and recovery.

[0021] Technical effects and advantages of the present invention:

[0022] The present invention orchestrates multiple factors by researching and designing a multi-factor fusion orchestration mechanism, formulating a multi-factor orchestration decision-making strategy, realizing dynamic resource supply and demand matching technology, and constructing a multi-factor fusion orchestration platform. At the same time, it develops an orchestration platform for the full life cycle management and full visualization management of computing network services, which is conducive to monitoring and managing the operating status of services and scheduling and configuring resources. It is also conducive to combining artificial intelligence, big data and other factors to achieve the optimal supply and demand matching between computing network business needs and various computing network infrastructure resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a schematic diagram of the multi-factor fusion orchestration process of the computing network of the present invention.

[0024] Figure 2A flow chart of the multi-factor fusion orchestration mechanism is studied and designed for the present invention.

[0025] Figure 3 A schematic diagram of the process flow for formulating a multi-factor orchestration decision strategy for the present invention.

[0026] Figure 4 This is a schematic diagram of the technical process of implementing dynamic resource supply and demand matching in the present invention.

[0027] Figure 5 A schematic diagram of the process of constructing a multi-factor fusion orchestration platform for the present invention. DETAILED DESCRIPTION

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0029] The present invention provides Figure 1-5 The method for fusion and arrangement of multiple elements of a computing network shown in the figure includes the following specific steps:

[0030] Step 1: Research and design a multi-element fusion orchestration mechanism. Based on business and SLA requirements, design an orchestration mechanism to ensure that it can support the orchestration of multiple elements in the computing network.

[0031] Step 2: Develop a multi-factor orchestration decision-making strategy to research and determine resource selection, scheduling, and allocation strategies, taking into account resource availability, real-time performance, and cost constraints. Make decisions and optimize strategies based on different business needs and environments.

[0032] Step 3: Implement dynamic resource supply and demand matching technology. Combining artificial intelligence and big data technologies, we will establish a dynamic supply and demand matching model between computing network business needs and computing network infrastructure resources. This model will monitor and analyze changes in business needs and the status of infrastructure resources in real time.

[0033] Step 4: Build a multi-factor integrated orchestration platform and develop an orchestration platform for full lifecycle management and full visualization management of computing network services, so that the operating status of monitoring and management services as well as resource scheduling and configuration can be viewed.

[0034] Specifically, the research and design of the multi-factor fusion orchestration mechanism in step one includes demand analysis and goal setting, factor identification and modeling, orchestration mechanism setting, and monitoring and management. The operation method of demand analysis and goal setting is to deeply understand the business needs, including the usage scenarios, business types and performance requirements of the computing network, and make SLA requirements for resource allocation, performance and reliability. Then, based on the results of the demand analysis, set the design goals of the multi-factor fusion orchestration mechanism. The operation method of factor identification and modeling is to identify the key elements in the computing network. The key elements include computing resources, network resources, storage resources and security resources. Then, model each element and model the attributes, status and relationships of the elements. The operation method of setting the orchestration mechanism is to formulate a multi-factor fusion orchestration strategy based on business needs and goals. The strategy should be able to balance the conflicts between different elements to achieve overall optimization. Then, design the process of the orchestration mechanism, which includes resource application, resource allocation, resource scheduling and resource release, and develop algorithms and models to support the orchestration mechanism. The algorithm can be a resource allocation algorithm and a scheduling algorithm. The algorithm formula for resource allocation weight can be:

[0035] P_i,k(t)=frac{r_i,k(t)}{R_i(t)}

[0036] Where r_i,k(t) is the rate that user i can reach in resource block k at time t, and R_i(t) is the average rate of user i within a time window T_c at time t. The monitoring and management operation method is to deploy the orchestration mechanism into the production environment, configure and debug it, and then establish a monitoring and management mechanism to monitor and manage the operation status of the orchestration mechanism in real time. During the monitoring process, pay attention to the corresponding indicators and possible anomalies and failures. At the same time, the orchestration mechanism can be tested and optimized after it is set up. The specific operation method is to build a test environment to simulate the actual business scenario and resource environment. The test environment should be as close to the production environment as possible to ensure the accuracy of the test results. Functional testing performs functional testing on the orchestration mechanism to verify whether it meets business requirements and goals. During the testing process, attention should be paid to whether the resource allocation, scheduling, and release processes are running normally. Performance testing performs performance testing on the orchestration mechanism to evaluate its performance. During the testing process, attention should be paid to indicators such as business response time, throughput, and resource utilization. Optimization and adjustment: Based on the test results, the orchestration mechanism is optimized and adjusted. The optimization process should focus on algorithm and model improvement, process optimization, etc.

[0037] Specifically, the formulation of a multi-factor orchestration decision strategy in step two includes determining the decision goal and analyzing the background, identifying and evaluating factors, formulating and implementing alternative plans, and establishing a monitoring mechanism. The method of determining the decision goal and analyzing the background is to clarify the specific goals that the decision is expected to achieve. The specific goals can be improving resource utilization, reducing costs, and optimizing business processes. Then, understand the current organizational environment, market environment, and technological environment, as well as the impact of the environment on the decision. The method of identifying and evaluating factors is to list all factors related to the decision, including resources, technology, personnel, time, and cost, and then conduct a detailed evaluation of each factor. The evaluation content includes its importance, feasibility, and scope of impact. The method of formulating and implementing alternative plans is to conceive multiple solutions based on the factor evaluation results, and conduct preliminary screening of the conceived solutions to eliminate solutions that are obviously not feasible for the decision goals. The method of establishing a monitoring mechanism is to establish an effective monitoring mechanism, monitor the implementation process in real time to ensure smooth progress, and make necessary adjustments and optimizations to the plan based on the monitoring results and actual conditions during implementation.

[0038] In particular, the technology for realizing dynamic resource supply and demand matching in step three includes clarifying business needs and evaluating the current situation, building a technology platform and integrating data, building a supply and demand matching model, specifying and allocating dynamic resource scheduling strategies, and establishing monitoring and feedback mechanisms. The operation method for clarifying business needs and evaluating the current situation is to deeply understand the specific needs of the business, including the demand and demand pattern of computing power, storage and network resources, and analyze the dynamic changing characteristics of business needs, and then conduct a comprehensive evaluation of the currently available resources. The evaluation content includes the type, quantity and performance of resources, and analyzes the availability, real-time and cost constraints of resources. The operation method for building a technology platform and integrating data is to select a suitable technology platform, which can be a cloud computing platform and a big data processing platform to support the realization of dynamic resource supply and demand matching, and integrate business demand data and resource status data to ensure the accuracy and completeness of the data, and then clean and pre-process the data to remove redundant and erroneous data to improve data quality. The operation method for building a supply and demand matching model is to select a suitable technology platform based on business needs and resources. Based on the current status of the source, a supply and demand matching model is designed. The matching model includes matching algorithms and matching strategies, and takes into account the availability, real-time nature and cost constraints of resources. The dynamic resource scheduling strategy specifies and allocates the operation mode as follows: according to the results of the supply and demand matching model, a resource scheduling strategy is formulated. The resource scheduling strategy includes resource allocation and scheduling priority, considering strategies such as resource load balancing and fault recovery to ensure the effective utilization of resources and the stable operation of the business. Then, according to changes in business needs and changes in resource status, the resource scheduling strategy is adjusted in real time, and dynamic scheduling and allocation of resources are achieved through automated tools or systems to improve resource utilization and business responsiveness. The operation mode for establishing the monitoring mechanism and feedback mechanism is to establish an effective monitoring mechanism to monitor the resource supply and demand matching process in real time, monitor indicators such as resource utilization, business response time, and possible anomalies and failures, and then establish a feedback mechanism to collect user feedback on the resource supply and demand matching effect, and adjust and optimize the supply and demand matching model and resource scheduling strategy based on the feedback.

[0039] In particular, the construction of a multi-factor fusion orchestration platform in step four includes clarifying the platform and conducting platform planning, technology selection and architecture design, functional module development and integration, data management and interface design, and platform deployment and operation and maintenance. The operation method of clarifying the platform and conducting platform planning is to deeply understand business needs. Business needs include the needs for computing power, storage and network resources, as well as the specific requirements of the business for resource orchestration. According to the results of demand analysis, formulate the overall plan and goals of platform construction, determine the functional modules, technical architecture and key elements of data flow of the platform, and the operation method of technology selection and architecture design is to select a suitable technology stack. The technology stack includes programming language, database, middleware and container technology, and then design the overall architecture of the platform. The overall structure includes front-end interface, back-end service, data storage and resource management modules, and determine the interaction mode and data flow path between modules. The operation method of functional module development and integration is to develop each function according to the platform plan. Modules, functional modules can be resource management, orchestration and scheduling, monitoring and alarm, and log auditing, to ensure the functional integrity and performance stability of each module, and then integrate the various functional modules to ensure data interaction and collaborative work between modules, and perform integration testing, while verifying the overall function and performance of the platform. The operation method of data management and interface design is to design a data model, which includes resource data, business data, and configuration data. Then, data storage, query, update, and deletion operations can be implemented, and the interface between the platform and the external system can be designed. The interface can be API and SDK. The operation method of platform deployment and operation and maintenance is to prepare the deployment environment, including servers, networks, and storage resources, configure necessary software and services, such as databases and middleware, and then deploy the platform to the production environment, and perform necessary configuration and debugging to ensure the stability and availability of the platform, and then establish an operation and maintenance management mechanism, which includes monitoring, alarming, backup, and recovery.

[0040] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for fusion and arrangement of multiple elements of a computing network, characterized in that: The specific steps include: Step 1: Research and design a multi-element fusion orchestration mechanism. Based on business and SLA requirements, design an orchestration mechanism to ensure that it can support the orchestration of multiple elements in the computing network. Step 2: Develop a multi-factor orchestration decision-making strategy to research and determine resource selection, scheduling, and allocation strategies, taking into account resource availability, real-time performance, and cost constraints. Make decisions and optimize strategies based on different business needs and environments. Step 3: Implement dynamic resource supply and demand matching technology. Combining artificial intelligence and big data technologies, we will establish a dynamic supply and demand matching model between computing network business needs and computing network infrastructure resources. This model will monitor and analyze changes in business needs and the status of infrastructure resources in real time. Step 4: Build a multi-factor integrated orchestration platform and develop an orchestration platform for full lifecycle management and full visualization management of computing network services, so that the operating status of monitoring and management services as well as resource scheduling and configuration can be viewed.

2. A method for fusion and arrangement of multiple elements of a computing network according to claim 1, characterized in that: The research and design of the multi-factor fusion orchestration mechanism in step one includes demand analysis and goal setting, factor identification and modeling, orchestration mechanism setting, and monitoring and management. The demand analysis and goal setting operation method is to deeply understand the business needs, including the usage scenarios, business types and performance requirements of the computing power network, and make SLA requirements for resource allocation, performance and reliability, and then set the design goals of the multi-factor fusion orchestration mechanism based on the demand analysis results.

3. A method for fusion and arrangement of multiple elements of a computing network according to claim 2, characterized in that: The operation mode of element identification and modeling is to identify the key elements in the computing network, which include computing resources, network resources, storage resources and security resources, and then perform modeling operations on each element, and model the attributes, status and relationships of the elements. The operation mode of setting the orchestration mechanism is to formulate a strategy for multi-element fusion orchestration based on business needs and goals, and then design the process of the orchestration mechanism. The process includes resource application, resource allocation, resource scheduling and resource release, and develop algorithms and models to support the orchestration mechanism. The algorithm can be a resource allocation algorithm and a scheduling algorithm. The algorithm formula for resource allocation weight can be: P_i,k(t)=frac{r_i,k(t)}{R_i(t)} where r_i,k(t) is the rate that user i can reach in resource block k at time t, and R_i(t) is the average rate of user i in a time window T_c at time t.

4. The method for fusion and arrangement of multiple elements of a computing network according to claim 2, characterized in that: The monitoring and management operation method is to deploy the orchestration mechanism into the production environment, configure and debug it, and then establish a monitoring and management mechanism to monitor and manage the operation status of the orchestration mechanism in real time, paying attention to the corresponding indicators and any anomalies and failures that occur during the monitoring process.

5. The method for fusion and arrangement of multiple elements of a computing network according to claim 1, characterized in that: The formulation of a multi-factor orchestration decision strategy in step 2 includes determining the decision goal and analyzing the background, identifying and evaluating factors, formulating alternative plans and implementing them, and establishing a monitoring mechanism. The method of determining the decision goal and analyzing the background is to clarify the specific goals that the decision is expected to achieve. The specific goals can be improving resource utilization, reducing costs, and optimizing business processes, and then understanding the current organizational environment, market environment, and technical environment, as well as the impact of the environment on the decision.

6. A method for fusion and arrangement of multiple elements of a computing network according to claim 5, characterized in that: The operation method of factor identification and evaluation is to list all factors related to the decision, including resources, technology, personnel, time and cost, and then conduct a detailed evaluation of each factor, including its importance, feasibility and scope of impact. The operation method of formulating and implementing alternative plans is to conceive multiple solutions based on the results of factor evaluation, and conduct preliminary screening of the conceived plans to exclude plans that are obviously not feasible for decision-making goals. The operation method of establishing a monitoring mechanism is to establish an effective monitoring mechanism, monitor the implementation process in real time to ensure smooth progress, and make necessary adjustments and optimizations to the plan according to the monitoring results and actual conditions during the implementation process.

7. The method for fusion and arrangement of multiple elements of a computing network according to claim 1, characterized in that: The technology for implementing dynamic resource supply and demand matching in step three includes clarifying business needs and evaluating the current situation, building a technology platform and integrating data, building a supply and demand matching model, specifying and allocating dynamic resource scheduling strategies, and establishing monitoring and feedback mechanisms. The operation method of clarifying business needs and evaluating the current situation is to deeply understand the specific needs of the business, including the demand and demand pattern of computing power, storage and network resources, and analyze the dynamic changes in business needs. Then, a comprehensive evaluation of currently available resources is conducted. The evaluation content includes the type, quantity and performance of resources, and analyzes the availability, real-time and cost constraints of resources. The operation method of building a technology platform and integrating data is to select a suitable technology platform. The technology platform can be a cloud computing platform and a big data processing platform to support the implementation of dynamic resource supply and demand matching, and integrate business demand data and resource status data to ensure the accuracy and completeness of the data, and then clean and pre-process the data.

8. The method for fusion and arrangement of multiple elements of a computing network according to claim 7 is characterized in that: The operation mode of constructing the supply and demand matching model is to design a supply and demand matching model according to business needs and resource status. The matching model includes a matching algorithm and a matching strategy, and takes into account the availability, real-time and cost constraints of resources. The operation mode of specifying and allocating the dynamic resource scheduling strategy is to formulate a resource scheduling strategy according to the results of the supply and demand matching model. The resource scheduling strategy includes resource allocation and scheduling priority. Then, according to changes in business needs and changes in resource status, the resource scheduling strategy is adjusted in real time, and dynamic scheduling and allocation of resources are realized through automated tools or systems. The operation mode of establishing the monitoring mechanism and feedback mechanism is to establish an effective monitoring mechanism to monitor the resource supply and demand matching process in real time, monitor resource utilization and business response time indicators, as well as possible anomalies and failures, and then establish a feedback mechanism to collect user feedback on the resource supply and demand matching effect, and adjust and optimize the supply and demand matching model and resource scheduling strategy based on the feedback.

9. The method for fusion and arrangement of multiple elements of a computing network according to claim 1, characterized in that: The construction of a multi-factor fusion orchestration platform in step four includes clarifying the platform and conducting platform planning, technology selection and architecture design, functional module development and integration, data management and interface design, and platform deployment and operation and maintenance. The operation method of clarifying the platform and conducting platform planning is to deeply understand the business needs. Business needs include the needs for computing power, storage and network resources, as well as the specific requirements of the business for resource orchestration. According to the results of the demand analysis, the overall plan and goals of the platform construction are formulated, and the functional modules, technical architecture and key elements of the data flow of the platform are determined. The operation method of the technology selection and architecture design is to select a suitable technology stack, which includes programming languages, databases, middleware and container technologies, and then design the overall architecture of the platform. The overall structure includes the front-end interface, back-end services, data storage and resource management modules, and determines the interaction mode and data flow path between modules.

10. A computing network multi-element fusion arrangement method according to claim 9, characterized in that: The operation mode of functional module development and integration is to develop various functional modules according to the platform planning. The functional modules can be resource management, orchestration and scheduling, monitoring and alarm, and log auditing. Then the various functional modules are integrated to ensure data interaction and collaborative work between modules, and integration testing is performed to verify the overall function and performance of the platform. The operation mode of data management and interface design is to design a data model. The data model includes resource data, business data and configuration data. Then, data storage, query, update and deletion operations can be implemented, and the interface between the platform and the external system can be designed. The interface can be API and SDK. The operation mode of platform deployment and operation and maintenance is to prepare the deployment environment, including server, network and storage resources, and then deploy the platform to the production environment, and perform necessary configuration and debugging, and then establish an operation and maintenance management mechanism, which includes monitoring, alarm, backup and recovery.

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