Multi-cloud platform configuration management method

Abstract

The invention particularly relates to a multi-cloud platform configuration management method. According to the multi-cloud platform configuration management method, a unified resource management platform is constructed, and the resources of a plurality of cloud platforms are automatically configured according to requirements by defining standardized configuration and templated operation; the resource configuration of the cloud platform is automatically deployed and changed based on an infrastructure, namely a code IaC technology; automatically scheduling and optimizing cross-cloud platform resource allocation according to service requirements, resource use conditions and a cloud platform pricing strategy; monitoring and analyzing the use condition, the performance index and the cost consumption of each cloud platform resource in real time, and performing resource optimization based on data analysis; and carrying out security and compliance management on the resource configuration of the cross-cloud platform. According to the multi-cloud platform configuration management method, the complexity of multi-cloud configuration management is remarkably reduced, the configuration management efficiency and reliability are improved, the operation and maintenance requirements of enterprises in a complex multi-cloud environment are met, and important technical support is provided for modern cloud computing.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing, and in particular to a method for multi-cloud platform configuration management. Background Art

[0002] With the rapid development of cloud computing technology, more and more enterprises are adopting multi-cloud strategies, taking advantage of the different advantages provided by multiple cloud service providers (such as AWS, Azure, Google Cloud, etc.) to meet complex business needs. However, there are significant differences in technical specifications, API interfaces, and resource management methods between different cloud platforms, making configuration management in a multi-cloud environment complex and inefficient. At the same time, frequent and uncontrollable configuration changes can easily lead to inconsistencies across platforms, thereby increasing operational risks.

[0003] In addition, traditional single-platform management tools lack support for multi-cloud scenarios, making it difficult to effectively detect and dynamically repair configuration conflicts, resulting in frequent system failures and resource waste.

[0004] In order to achieve configuration standardization, automated adaptation and consistency maintenance, help enterprises simplify operation and maintenance processes, and improve the reliability and management efficiency of multi-cloud environments, the present invention proposes a method for multi-cloud platform configuration management. Summary of the invention

[0005] In order to overcome the deficiencies of the prior art, the present invention provides a simple and efficient method for multi-cloud platform configuration management.

[0006] The present invention is achieved through the following technical solutions:

[0007] A method for multi-cloud platform configuration management, comprising the following steps:

[0008] Step S1: Build a unified resource management platform to automatically configure resources of multiple cloud platforms according to demand by defining standardized configurations and templated operations;

[0009] Step S2: Based on the Infrastructure as Code (IaC) technology, automatically deploy and change the resource configuration of the cloud platform;

[0010] Step S3: Automatically schedule and optimize resource allocation across cloud platforms based on business needs, resource usage, and cloud platform pricing strategies;

[0011] Step S4: monitor and analyze the usage, performance indicators and cost consumption of each cloud platform resource in real time, and optimize resources based on data analysis;

[0012] Step S5: Perform security and compliance management on resource configuration across cloud platforms.

[0013] In step S1, the implementation steps are as follows:

[0014] Step S1.1, designing a resource management platform using a layered architecture, including a management layer, an adaptation layer, and an execution layer;

[0015] The management layer is used to implement centralized resource configuration management, the adaptation layer is responsible for abstracting the APIs of different cloud platforms to form a unified management interface, and the execution layer is the cloud resources to be managed and configured (virtual machines, databases, etc.);

[0016] Step S1.2: formulate a unified resource naming rule to facilitate unified management and query;

[0017] Step S1.3: Develop a labeling strategy, use labels to identify and classify resources, so as to quickly locate and identify resources;

[0018] Step S1.4: Use template tools such as Terraform or Pulumi to manage multi-cloud resources to improve configuration reusability, reduce human errors, and ensure consistency of resources on different cloud platforms.

[0019] In step S1, the resource management platform supports modular configuration management and distributed deployment, and integrates load balancing mechanism and disaster recovery mechanism. The load balancing mechanism is used to ensure the reasonable allocation of resources. When a cloud platform fails, it can quickly switch to a backup cloud platform under the disaster recovery mechanism.

[0020] Use centralized identity management tools such as Azure AD or Okta to unify authentication mechanisms for all cloud platforms to ensure consistent access control across cloud platforms.

[0021] Customize and select sensitive data (such as API keys, database credentials, etc.), encrypt them, and manage them using Vault tools or AWS Secrets Manager tools to ensure data security;

[0022] The resource management platform adopts a zero-trust architecture, which requires verification of all users and devices. Regardless of whether the user or device is on the intranet or the extranet, they must pass identity authentication before accessing resources.

[0023] Configuration files and related sensitive data confirmed by users are encrypted and stored, and access to configuration files is managed through fine-grained permission control.

[0024] All user-defined key data is backed up between different cloud platforms, and the backup data can use cross-cloud backup tools such as Veeam and CloudEndure, and the automated recovery mechanism is used to synchronize and restore data.

[0025] Customize scheduled tasks and perform fault injection tests regularly to simulate cloud platform or service failures to verify the system's disaster recovery capabilities and high availability.

[0026] In a multi-cloud environment, use load balancers such as AWS ELB or Google Cloud Load Balancing to customize traffic distribution to resources on different cloud platforms and automatically failover in the event of a failure, ensuring that traffic can be smoothly switched between different cloud platforms to avoid single points of failure.

[0027] To achieve high availability and disaster recovery, a cross-region and cross-cloud multi-active architecture is designed. When a region or cloud platform fails, traffic will automatically switch to other healthy regions or cloud platforms to ensure uninterrupted business.

[0028] Introduce automation tools (such as Terraform, CloudFormation, etc.) to manage cross-cloud disaster recovery architecture, combined with real-time monitoring tools to detect faults in real time and start preset recovery processes to ensure that the system can quickly recover to normal.

[0029] In step S1, each configuration change must be reviewed and approved to prevent configuration errors;

[0030] Use version control tools (such as Git) to manage configuration files in a versioned manner, and support quick rollback to historical versions to avoid system failures caused by configuration errors.

[0031] Record logs of all configuration changes, customize scheduled tasks, and generate audit reports regularly;

[0032] Before changing the configuration, use the Terraform Plan tool to simulate the execution results to ensure that the configuration change will not have an adverse impact on the system.

[0033] In step S2, the implementation steps are as follows:

[0034] Step S2.1: Use Terraform or Pulumi to encode resource creation, modification, and destruction operations into IaC configuration files to ensure consistency and reusability of resource configuration.

[0035] Step S2.2: Put the IaC configuration files into version control (such as Git) and automate the deployment and update of resources through the CI / CD pipeline to achieve rapid configuration changes and deployment and avoid manual operation errors.

[0036] Step S2.3: Use Ansible or SaltStack to coordinate and automate resource management across multiple cloud platforms to ensure that multi-cloud resources can be uniformly scheduled.

[0037] Step S2.4: Use the plan command of the Terraform tool to regularly check the matching degree between the actual status of resources and the configuration file, so as to promptly discover and fix resource drift problems.

[0038] In step S3, the implementation steps are as follows:

[0039] Step S3.1: Use the third-party tool CloudHealth or the cost analysis tools provided by various cloud service providers to customize scheduled tasks, regularly analyze resource usage and cost structure, and identify potential waste points;

[0040] Step S3.2, regularly review resource usage, optimize resource allocation, and shut down resources that are idle or whose utilization is below a custom threshold;

[0041] Step S3.3: Set budgets for each project or team based on demand, and use a real-time warning mechanism to ensure that relevant persons in charge are promptly reminded to make adjustments when the budget is overspent;

[0042] Step S3.4: Optimize the cost of cloud resources by selecting reserved instances, spot instances or long-term contracts; Step S3.5: Use automated tools such as AWS Lambda or Google Cloud Functions to regularly audit and clean up resources to promptly recycle unused resources and prevent resource idleness and waste.

[0043] In step S4, the implementation steps are as follows:

[0044] Step S4.1 uses cross-cloud monitoring tools such as Prometheus or Datadog to uniformly collect and display performance data and log information from different cloud platforms;

[0045] Step S4.2: Use the visualization tool Grafana to display the monitoring data as a dashboard and monitor key indicators in real time, including CPU utilization, storage utilization, and network latency.

[0046] Step S4.3: Automatically adjust resource configuration through the Auto Scaling function to ensure that resources are expanded in time when the load increases and resources are recovered in time when the load decreases;

[0047] Step S4.4: Set up multi-level alarm rules to promptly notify relevant personnel and initiate emergency response mechanisms when system performance abnormalities occur.

[0048] The step S5 is implemented as follows:

[0049] Step S5: Use Cloud Custodian or AWS Config to regularly scan resource configurations on all cloud platforms to ensure that they comply with industry standards.

[0050] Step S5: Sensitive data and resources are encrypted according to industry standards to ensure the security of data during transmission and storage;

[0051] Step S5: Customize the scheduled tasks and generate compliance reports regularly to ensure that managers can understand the compliance status of the current system in a timely manner and make necessary adjustments based on the reports.

[0052] A multi-cloud platform configuration management system, comprising:

[0053] A unified resource management platform for automating the configuration of resources on multiple cloud platforms through standardized configuration and templated operations;

[0054] Automated deployment and change management module, which is used to automatically manage the resource configuration and changes of the cloud platform based on infrastructure as code (IaC) technology;

[0055] The cross-cloud platform scheduling and optimization module is used to automatically optimize resource allocation across cloud platforms based on business needs and resource usage;

[0056] Real-time monitoring and data analysis module, used to collect and analyze resource usage, performance indicators and cost consumption data in real time, and provide a data visualization interface for managers to make decisions;

[0057] The compliance and security management module is used to manage the security and compliance of the resource configuration of the cloud platform.

[0058] A device for multi-cloud platform configuration management, characterized in that it includes a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the above-mentioned method steps when executing the computer program.

[0059] A readable storage medium, characterized in that: a computer program is stored on the readable storage medium, and the computer program implements the above method steps when executed by a processor.

[0060] The beneficial effects of the present invention are as follows: the method for multi-cloud platform configuration management significantly reduces the complexity of multi-cloud configuration management and improves the efficiency and reliability of configuration management through templating, automation and intelligent means. It is particularly suitable for the operation and maintenance needs of enterprises in complex multi-cloud environments and provides important technical support for modern cloud computing. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0062] Attached Figure 1 This is a schematic diagram of the multi-cloud platform configuration management system architecture of the present invention.

[0063] Attached Figure 2 A schematic diagram of a method for multi-cloud platform configuration management according to the present invention. DETAILED DESCRIPTION

[0064] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a 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 creative work should fall within the scope of protection of the present invention.

[0065] The complexity of multi-cloud environments brought about by multi-cloud configuration management brings about differences between different platforms. How to manage these resources in a unified and efficient manner is the key.

[0066] Unified resource management: Enterprises need to manage all resources uniformly across multiple cloud platforms, including virtual machines, storage, networks, etc. A central management platform is needed to coordinate resources from different cloud service providers.

[0067] Configuration changes are traceable: Each configuration change needs to have audit capabilities to ensure that every operation is recorded and can be quickly rolled back to the previous stable state.

[0068] Automation and scalability: Automated deployment is the key to reducing human errors, and automated configuration management can improve efficiency and simplify operations. At the same time, as the business expands, the platform needs to be elastic and support rapid resource expansion and reduction.

[0069] Basic security and compliance requirements: Resources in a multi-cloud environment involve different security standards and compliance requirements. It is necessary to ensure that data protection measures, authentication, and access control mechanisms are consistent.

[0070] Cost optimization goal: The management of multi-cloud resources not only needs to meet performance requirements, but also needs to control costs, avoid unnecessary waste of resources, and ensure maximum resource utilization.

[0071] The multi-cloud platform configuration management method comprises the following steps:

[0072] Step S1: Build a unified resource management platform to automatically configure resources of multiple cloud platforms according to demand by defining standardized configurations and templated operations;

[0073] Step S2: Based on the Infrastructure as Code (IaC) technology, automatically deploy and change the resource configuration of the cloud platform;

[0074] Step S3: Automatically schedule and optimize resource allocation across cloud platforms based on business needs, resource usage, and cloud platform pricing strategies;

[0075] Step S4: monitor and analyze the usage, performance indicators and cost consumption of each cloud platform resource in real time, and optimize resources based on data analysis;

[0076] Step S5: Perform security and compliance management on resource configuration across cloud platforms.

[0077] In step S1, the implementation steps are as follows:

[0078] Step S1.1, designing a resource management platform using a layered architecture, including a management layer, an adaptation layer, and an execution layer;

[0079] The management layer is used to implement centralized resource configuration management, the adaptation layer is responsible for abstracting the APIs of different cloud platforms to form a unified management interface, and the execution layer is the cloud resources (virtual machines, databases, etc.) to be managed and configured.

[0080] In a multi-cloud environment, the complexity of resource management comes from the differences between different cloud platforms. Therefore, it is crucial to establish unified configuration standards and resource consistency management.

[0081] Step S1.2: Develop a unified resource naming rule. All cloud platform resources (such as virtual machines, storage, networks, etc.) should follow the same naming rule. These rules may include information such as business module, environment type, region, instance number, etc., for unified management and query;

[0082] Step S1.3: Develop a tagging strategy to use tags to identify and classify resources. The tagging strategy should cover all cloud platforms and include information such as responsibility, environment type, and business module to quickly locate and identify resources.

[0083] Step S1.4: Use template tools such as Terraform or Pulumi to manage multi-cloud resources to improve configuration reusability, reduce human errors, and ensure consistency of resources on different cloud platforms.

[0084] In step S1, the resource management platform supports modular configuration management and distributed deployment, and integrates load balancing mechanism and disaster recovery mechanism. The load balancing mechanism is used to ensure the reasonable allocation of resources. When a cloud platform fails, it can quickly switch to a backup cloud platform under the disaster recovery mechanism.

[0085] Use centralized identity management tools such as Azure AD or Okta to unify authentication mechanisms for all cloud platforms to ensure consistent access control across cloud platforms.

[0086] Customize and select sensitive data (such as API keys, database credentials, etc.), encrypt them, and manage them using Vault tools or AWS Secrets Manager tools to ensure data security;

[0087] The resource management platform adopts a zero-trust architecture, which requires verification of all users and devices. Regardless of whether users and devices are on the intranet or the extranet, they must pass identity authentication before they can access resources. This mechanism can improve security in a multi-cloud environment.

[0088] Configuration files and related sensitive data confirmed by users are encrypted and stored, and access to configuration files is managed through fine-grained permission control.

[0089] In a multi-cloud environment, disaster recovery and high availability design are core requirements to ensure business continuity. Since services on multiple cloud platforms may fail in different regions, it is crucial to design an architecture that can ensure normal operation of the system when any cloud platform or region fails.

[0090] All user-defined key data is backed up between different cloud platforms, and the backup data can use cross-cloud backup tools such as Veeam and CloudEndure, and the automated recovery mechanism is used to synchronize and restore data.

[0091] Customize scheduled tasks and conduct fault injection tests regularly to simulate cloud platform or service failures to verify the system's disaster recovery capabilities and high availability, ensuring that the system can quickly switch to the backup system when a failure occurs, minimizing downtime.

[0092] In a multi-cloud environment, use load balancers such as AWS ELB or Google Cloud Load Balancing to customize traffic distribution to resources on different cloud platforms and automatically failover in the event of a failure, ensuring that traffic can be smoothly switched between different cloud platforms to avoid single points of failure.

[0093] To achieve high availability and disaster recovery, a cross-region and cross-cloud multi-active architecture is designed. When a region or cloud platform fails, traffic will automatically switch to other healthy regions or cloud platforms to ensure uninterrupted business.

[0094] Introduce automation tools (such as Terraform, CloudFormation, etc.) to manage cross-cloud disaster recovery architecture, combined with real-time monitoring tools to detect faults in real time and start preset recovery processes to ensure that the system can quickly recover to normal.

[0095] The management and auditing mechanism of configuration changes is a key link in ensuring resource stability and traceability.

[0096] In step S1, each configuration change must be reviewed and approved to prevent configuration errors;

[0097] Use version control tools (such as Git) to manage configuration files in a versioned manner, and support quick rollback to historical versions to avoid system failures caused by configuration errors.

[0098] Record logs of all configuration changes, customize scheduled tasks, and generate audit reports regularly; audit reports are crucial for problem tracing, compliance checking, and problem fixing.

[0099] Before changing the configuration, use the Terraform Plan tool to simulate the execution results to ensure that the configuration change will not have an adverse impact on the system.

[0100] In step S2, the implementation steps are as follows:

[0101] Step S2.1: Use Terraform or Pulumi to encode resource creation, modification, and destruction operations into IaC configuration files to ensure consistency and reusability of resource configuration.

[0102] Step S2.2: Put the IaC configuration files into version control (such as Git) and automate the deployment and update of resources through the CI / CD pipeline to achieve rapid configuration changes and deployment and avoid manual operation errors.

[0103] Step S2.3: Use Ansible or SaltStack to coordinate and automate resource management across multiple cloud platforms to ensure that multi-cloud resources can be uniformly scheduled.

[0104] Step S2.4: Use the plan command of the Terraform tool to regularly check the matching degree between the actual status of resources and the configuration file, so as to promptly discover and fix resource drift problems.

[0105] In step S3, the implementation steps are as follows:

[0106] Step S3.1: Use the third-party tool CloudHealth or the cost analysis tools provided by various cloud service providers to customize scheduled tasks, regularly analyze resource usage and cost structure, and identify potential waste points;

[0107] Step S3.2, regularly review resource usage, optimize resource allocation, and shut down idle resources or resources with utilization rates below a custom threshold to avoid unnecessary cost expenditures;

[0108] Step S3.3: Set budgets for each project or team based on demand, and use real-time warning mechanisms to ensure that relevant persons in charge are promptly reminded to make adjustments when budget overruns occur; this can help ensure that the costs of various cloud resources are within the budget, thereby achieving more accurate financial management.

[0109] Step S3.4, optimize the cost of cloud resources by selecting reserved instances, spot instances, or long-term contracts; these resources are relatively cheap and suitable for fixed-load applications or services.

[0110] Step S3.5: Use the automated tools AWS Lambda or Google Cloud Functions to regularly audit and clean up resources to promptly recycle unused resources and prevent resource idleness and waste.

[0111] In step S4, the implementation steps are as follows:

[0112] Step S4.1 uses cross-cloud monitoring tools such as Prometheus or Datadog to uniformly collect and display performance data and log information from different cloud platforms;

[0113] Step S4.2: Use the visualization tool Grafana to display the monitoring data as a dashboard and monitor key indicators in real time, including CPU utilization, storage utilization, and network latency.

[0114] Step S4.3: Automatically adjust resource configuration through the Auto Scaling function to ensure that resources are expanded in time when the load increases and resources are recovered in time when the load decreases;

[0115] Step S4.4: Set up multi-level alarm rules to promptly notify relevant personnel and initiate emergency response mechanisms when system performance abnormalities occur.

[0116] The step S5 is implemented as follows:

[0117] Step S5: Use Cloud Custodian or AWS Config to regularly scan resource configurations on all cloud platforms to ensure that they comply with industry standards.

[0118] Step S5: Sensitive data and resources are encrypted according to industry standards to ensure the security of data during transmission and storage;

[0119] Step S5: Customize the scheduled tasks and generate compliance reports regularly to ensure that managers can understand the compliance status of the current system in a timely manner and make necessary adjustments based on the reports.

[0120] The multi-cloud platform configuration management system includes:

[0121] A unified resource management platform for automating the configuration of resources on multiple cloud platforms through standardized configuration and templated operations;

[0122] Automated deployment and change management module, which is used to automatically manage the resource configuration and changes of the cloud platform based on infrastructure as code (IaC) technology;

[0123] The cross-cloud platform scheduling and optimization module is used to automatically optimize resource allocation across cloud platforms based on business needs and resource usage;

[0124] Real-time monitoring and data analysis module, used to collect and analyze resource usage, performance indicators and cost consumption data in real time, and provide a data visualization interface for managers to make decisions;

[0125] The compliance and security management module is used to manage the security and compliance of the resource configuration of the cloud platform.

[0126] The multi-cloud platform configuration management device includes a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the above method steps when executing the computer program.

[0127] The readable storage medium stores a computer program, and when the computer program is executed by a processor, the above method steps are implemented.

[0128] Compared with the existing technology, the multi-cloud platform configuration management method has the following characteristics:

[0129] First, by defining a unified configuration template and standardizing the description of network, storage, and security rules, the differences between different cloud platforms can be shielded.

[0130] At the same time, we designed multi-cloud adapters to convert common templates into native configuration formats or API calls supported by various cloud platforms (such as AWS, Azure, Google Cloud, etc.);

[0131] Second, based on load conditions and business needs, the optimal cloud platform is dynamically selected and resource allocation is optimized through intelligent scheduling algorithms, reducing costs.

[0132] Third, we use an event-driven dynamic synchronization mechanism to monitor configuration changes in multi-platform environments and update them in real time to ensure configuration consistency.

[0133] Fourth, it implements identity and access management, data encryption, log auditing, and automated compliance checks across cloud platforms to ensure the security and compliance of resources. It also has a built-in conflict detection and repair module that analyzes potential conflicts based on an intelligent rule engine and can provide automated or manual processing solutions.

[0134] Fifth, it provides version management functions, supports configuration history tracing and rapid rollback, and ensures system stability.

[0135] In summary, this multi-cloud platform configuration management method solves the configuration management complexity and inefficiency problems caused by platform differences in a multi-cloud environment, realizes the standardized, automated and intelligent management of multi-cloud platform resource configuration, reduces the difficulty of operation and maintenance, improves system reliability and management efficiency, and provides technical support for enterprises to efficiently deploy and manage resources in a complex multi-cloud environment.

[0136] The embodiment described above is only one specific implementation of the present invention. Common changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for multi-cloud platform configuration management, characterized in that: The following steps are involved: Step S1: Build a unified resource management platform to automatically configure resources of multiple cloud platforms according to demand by defining standardized configurations and templated operations; Step S2: Based on the Infrastructure as Code (IaC) technology, automatically deploy and change the resource configuration of the cloud platform; Step S3: Automatically schedule and optimize resource allocation across cloud platforms based on business needs, resource usage, and cloud platform pricing strategies; Step S4: monitor and analyze the usage, performance indicators and cost consumption of each cloud platform resource in real time, and optimize resources based on data analysis; Step S5: Perform security and compliance management on resource configuration across cloud platforms.

2. The method for multi-cloud platform configuration management according to claim 1, characterized in that: In step S1, the implementation steps are as follows: Step S1.1, designing a resource management platform using a layered architecture, including a management layer, an adaptation layer, and an execution layer; The management layer is used to implement centralized resource configuration management, the adaptation layer is responsible for abstracting the APIs of different cloud platforms to form a unified management interface, and the execution layer is the cloud resources to be managed and configured. The resource management platform supports modular configuration management and distributed deployment, and integrates load balancing and disaster recovery mechanisms. The load balancing mechanism is used to ensure the rational allocation of resources. When a cloud platform fails, it can quickly switch to a backup cloud platform under the disaster recovery mechanism. Use centralized identity management tools such as Azure AD or Okta to unify authentication mechanisms for all cloud platforms to ensure consistent access control across cloud platforms. Customize and select sensitive data, encrypt it, and manage it using Vault or AWS Secrets Manager to ensure data security; The resource management platform adopts a zero-trust architecture, which requires verification of all users and devices. Regardless of whether the user or device is on the intranet or the extranet, they must pass identity authentication before accessing resources. Configuration files and user-defined and confirmed related sensitive data are encrypted and stored, and access to configuration files is managed through fine-grained permission control; All user-defined key data is backed up between different cloud platforms, and the backup data can use cross-cloud backup tools, and the automatic recovery mechanism is familiar with data synchronization and recovery operations; Customize scheduled tasks and perform fault injection tests regularly to simulate cloud platform or service failures to verify the system's disaster recovery capabilities and high availability. In a multi-cloud environment, use load balancers such as AWS ELB or Google Cloud Load Balancing to distribute traffic to resources on different cloud platforms and automatically failover in the event of a failure, ensuring that traffic can be smoothly switched between different cloud platforms to avoid single points of failure. To achieve high availability and disaster recovery, a cross-region and cross-cloud multi-active architecture is designed. When a region or cloud platform fails, traffic will automatically switch to other healthy regions or cloud platforms to ensure uninterrupted business. Introduce automated tools to manage cross-cloud disaster recovery architecture, combined with real-time monitoring tools to detect faults in real time and initiate preset recovery processes to ensure that the system can quickly recover to normal status; Step S1.2: formulate a unified resource naming rule to facilitate unified management and query; Step S1.3: Develop a labeling strategy, use labels to identify and classify resources, so as to quickly locate and identify resources; Step S1.4: Use template tools such as Terraform or Pulumi to manage multi-cloud resources to improve configuration reusability, reduce human errors, and ensure consistency of resources on different cloud platforms.

3. The method for multi-cloud platform configuration management according to claim 2, characterized in that: In step S1, each configuration change must be reviewed and approved to prevent configuration errors; Use version control tools to manage configuration files in a versioned manner, and support quick rollback to historical versions to avoid system failures caused by configuration errors. Record logs of all configuration changes, customize scheduled tasks, and generate audit reports regularly; Before changing the configuration, use the Terraform Plan tool to simulate the execution results to ensure that the configuration change will not have an adverse impact on the system.

4. The method for multi-cloud platform configuration management according to claim 1, characterized in that: In step S2, the implementation steps are as follows: Step S2.1: Use Terraform or Pulumi to encode resource creation, modification, and destruction operations into IaC configuration files to ensure consistency and reusability of resource configuration. Step S2.2: Include the IaC configuration file in version control and automate the deployment and update of resources through the CI / CD pipeline to achieve rapid configuration changes and deployment and avoid manual operation errors. Step S2.3: Use Ansible or SaltStack to coordinate and automate resource management across multiple cloud platforms to ensure that multi-cloud resources can be uniformly scheduled. Step S2.4: Use the plan command of the Terraform tool to regularly check the matching degree between the actual status of resources and the configuration file, so as to promptly discover and fix resource drift problems.

5. The method for multi-cloud platform configuration management according to claim 1, characterized in that: In step S3, the implementation steps are as follows: Step S3.1: Use the third-party tool CloudHealth or the cost analysis tools provided by various cloud service providers to customize scheduled tasks, regularly analyze resource usage and cost structure, and identify potential waste points; Step S3.2, regularly review resource usage, optimize resource allocation, and shut down resources that are idle or whose utilization is below a custom threshold; Step S3.3: Set budgets for each project or team based on demand, and use a real-time warning mechanism to ensure that relevant persons in charge are promptly reminded to make adjustments when the budget is overspent; Step S3.4: Optimize the cost of cloud resources by selecting reserved instances, spot instances or long-term contracts; Step S3.5: Use automated tools such as AWS Lambda or Google Cloud Functions to regularly audit and clean up resources to promptly recycle unused resources and prevent resource idleness and waste.

6. The method for multi-cloud platform configuration management according to claim 1, characterized in that: In step S4, the implementation steps are as follows: Step S4.1 uses cross-cloud monitoring tools such as Prometheus or Datadog to uniformly collect and display performance data and log information from different cloud platforms; Step S4.2: Use the visualization tool Grafana to display the monitoring data as a dashboard and monitor key indicators in real time, including CPU utilization, storage utilization, and network latency. Step S4.3: Automatically adjust resource configuration through the Auto Scaling function to ensure that resources are expanded in time when the load increases and resources are recovered in time when the load decreases; Step S4.4: Set up multi-level alarm rules to promptly notify relevant personnel and initiate emergency response mechanisms when system performance abnormalities occur.

7. The method for multi-cloud platform configuration management according to claim 1, characterized in that: The step S5 is implemented as follows: Step S5: Use Cloud Custodian or AWS Config to regularly scan resource configurations on all cloud platforms to ensure that they comply with industry standards. Step S5: Sensitive data and resources are encrypted according to industry standards to ensure the security of data during transmission and storage; Step S5: Customize the scheduled tasks and generate compliance reports regularly to ensure that managers can understand the compliance status of the current system in a timely manner and make necessary adjustments based on the reports.

8. A multi-cloud platform configuration management system, characterized by: include: A unified resource management platform for automating the configuration of resources on multiple cloud platforms through standardized configuration and templated operations; Automated deployment and change management module, which is used to automatically manage the resource configuration and changes of the cloud platform based on infrastructure as code (IaC) technology; The cross-cloud platform scheduling and optimization module is used to automatically optimize resource allocation across cloud platforms based on business needs and resource usage; Real-time monitoring and data analysis module, used to collect and analyze resource usage, performance indicators and cost consumption data in real time, and provide a data visualization interface for managers to make decisions; The compliance and security management module is used to manage the security and compliance of the resource configuration of the cloud platform.

9. A device for multi-cloud platform configuration management, characterized in that: The method comprises a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the method according to any one of claims 1 to 7 when executing the computer program.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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