Service deployment method and device based on cloud computing, electronic equipment and storage medium

Abstract

The invention provides a service deployment method and device based on cloud computing, electronic equipment and a storage medium. According to the service deployment method and device based on cloud computing, a system can automatically receive an executable file of a service system and operating environment parameters including an operating system type, a middleware type, a performance index and a security requirement; and judging an adaptive container or virtual machine environment according to the performance index and the security requirement, automatically matching the cloud computing resource component meeting the performance index based on the running environment parameter, and completing the full-process operation of corresponding environment creation, component deployment initialization and business system starting. The problems that in the prior art, due to the fact that a large amount of manual operation is relied on, deployment efficiency is low, errors are prone to occurring, and proper cloud computing resources and operation environments are difficult to accurately match according to the performance and safety requirements of a service system can be solved, and the purposes of reducing manual intervention, improving deployment efficiency, reducing human errors and improving deployment efficiency are achieved. And meanwhile, the technical effect of accurate matching of the cloud computing resources and the service requirements is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cloud computing business system, and particularly relates to a business deployment method and device based on cloud computing, electronic equipment and storage medium. BACKGROUND

[0002] With the rapid development of cloud computing technology, as an important support platform of modern IT architecture, it has been widely used in enterprise-level application deployment, resource elastic scheduling and business rapid iteration. In related technologies, through the collaborative work of parameter receiving, running environment judgment, resource matching and deployment, and business life cycle management modules, the basic framework of an automatic deployment system is constructed. Specifically, this technical system covers the whole process from business demand input, running environment intelligent decision, resource component dynamic matching, infrastructure creation to business system deployment and management, including key links such as operating system image selection, middleware configuration, container and virtual machine resource scheduling, load balancing strategy, forming a cloud resource adaptation mechanism oriented to performance and security requirements.

[0003] However, in the existing business deployment method, manual configuration or static rules are directly used for environment selection and resource matching, and a dynamic evaluation model based on multi-dimensional parameters is not established, which may lead to problems such as low resource utilization, insufficient security isolation or performance bottleneck. Specifically, the traditional deployment process usually relies on operation and maintenance personnel to manually select operating systems, middleware versions and running environments, lacks systematic analysis of performance indicators such as business concurrency, response time and data processing capacity, and cannot effectively combine data encryption, access control and other security requirements for environment adaptation. In addition, in the existing technology, in the mixed deployment scene of containers and virtual machines, there is a lack of unified management interface, resulting in the need to handle expansion, start and stop operations for different platforms respectively, increasing the operation and maintenance complexity and limiting the rapid response and flexible expansion capability of the business system. SUMMARY

[0004] The present application provides a business deployment method and device based on cloud computing, electronic equipment and storage medium, to at least solve the problem of high operation and maintenance complexity in the related art in the mixed deployment scene of containers and virtual machines, which lacks a unified management interface, resulting in the need to handle expansion, start and stop operations for different platforms respectively.

[0005] The present application provides a business deployment method based on cloud computing, comprising: receiving an uploaded business system executable file and its running environment parameters required for running, the running environment parameters including operating system type, middleware type, performance indicators and security requirements; judging whether the business system is suitable for running in a container environment or a virtual machine environment according to the performance indicators and the security requirements; based on the operation environment parameter, match a cloud computing resource component satisfying the performance index in a cloud resource library; create a corresponding container or virtual machine environment according to the judgment result, deploy the matched cloud computing resource component into the container or virtual machine environment, and perform initialization configuration; deploy the business system executable file into the configured container or virtual machine environment, and start the business system.

[0006] The application further provides a business deployment device based on cloud computing, which comprises: a receiving unit configured to receive an uploaded business system executable file and operation environment parameters required for operation of the business system, wherein the operation environment parameters comprise an operating system type, a middleware type, a performance index, and a security requirement; a judgment unit configured to judge whether the business system is suitable for operation in a container environment or a virtual machine environment according to the performance index and the security requirement; a matching unit configured to match a cloud computing resource component satisfying the performance index in a cloud resource library based on the operation environment parameter; a first deployment unit configured to create a corresponding container or virtual machine environment according to the judgment result, deploy the matched cloud computing resource component into the container or virtual machine environment, and perform initialization configuration; a second deployment unit configured to deploy the business system executable file into the configured container or virtual machine environment, and start the business system.

[0007] The application further provides an electronic device, which comprises a memory configured to store a computer program, and a processor configured to execute the computer program to implement the steps of any one of the above business deployment methods based on cloud computing.

[0008] The application further provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of any one of the above business deployment methods based on cloud computing.

[0009] The application further provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the steps of any one of the above business deployment methods based on cloud computing.

[0010] The application provides a cloud computing-based business deployment method and device, electronic equipment and a storage medium. According to the application, the system can automatically receive a business system executable file and a running environment parameter containing an operating system type, a middleware type, a performance index and security requirements, judge an adaptive container or virtual machine environment according to the performance index and the security requirements, automatically match a cloud computing resource component satisfying the performance index based on the running environment parameter, and complete a full-process operation of corresponding environment creation, component deployment initialization and business system startup. Therefore, the problems that the deployment efficiency is low, errors are prone to occur, and it is difficult to accurately match the adaptive cloud computing resource and running environment according to the performance and security requirements of the business system in the prior art due to the dependence on a large amount of manual operation can be solved, technical effects of reducing manual intervention, improving deployment efficiency, reducing human errors, accurately matching cloud computing resources and business requirements, and ensuring stable operation of the business system are achieved.

[0011] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0012] The accompanying drawings are used to better understand the present application, and do not constitute a limitation on the present disclosure. Among them: Figure 1 A flowchart of a cloud computing-based business deployment method provided by an embodiment of the present application is shown in the figure. Figure 2 A structural diagram of a cloud computing-based business deployment device provided by an embodiment of the present application is shown in the figure. Figure 3 A structural diagram of another cloud computing-based business deployment device provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0013] Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the present disclosure to help understanding, and should be considered as merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Also, in order to be clear and concise, the description in the following description omits the description of well-known functions and structures.

[0014] In order to enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0015] In combination with a specific application environment architecture or a specific hardware architecture on which the execution of the cloud computing-based service deployment method depends, the specific application environment architecture or the specific hardware architecture is described herein.

[0016] Embodiments of the present application provide a cloud computing-based service deployment method, Figure 1 A flowchart of the cloud computing-based service deployment method provided by embodiments of the present application is shown in FIG. 1.

[0017] As shown in FIG. 1, the method comprises the following steps: Figure 1 Step 101, receiving an uploaded service system executable file and a running environment parameter required for running the service system executable file, the running environment parameter comprising an operating system type, a middleware type, a performance index and a security requirement.

[0018] In embodiments of the present application, the core of step 101 is to receive key information uploaded by a service system implementer, and these information are the basic prerequisite for completing subsequent service deployment. The service system executable file is the core carrier of the service that can run in the cloud computing environment, and it is a directly executable program file formed after pre-compilation and packaging processing, which contains the core function code and running logic of the service system and can ensure that the service achieves the intended service capability in the target environment.

[0019] ​The running environment parameter is the key configuration information to ensure the normal running of the business system executable file, meet the business performance and security requirements, and specifically covers four categories of operating system type, middleware type, performance index and security requirements. The operating system type refers to the type of underlying operating system required for the running of the business system. Different operating systems have differences in hardware resource support, software compatibility and running stability. Accurate operating system type information can ensure that the subsequent matched cloud computing resources are adapted to the underlying running requirements of the business system. The middleware type includes database middleware, web middleware, etc. such as MySQL database middleware and Nginx web middleware. Such middleware is a bridge connecting the business system and the underlying hardware resources, and can provide basic support services such as data storage, network communication and transaction processing for the business system. Clearly defining the middleware type is an important step to ensure the normal implementation of the functions of the business system. The performance index is a key parameter to measure the running capacity of the business system, usually including expected concurrent access volume, data processing speed, response time, etc. For example, an online business may require an expected concurrent access volume of 5000 people, a data processing speed of 1000 records per second, and a response time of no more than 2 seconds. These indicators directly determine the performance specifications of the subsequent required cloud computing resources. The security requirements involve the security protection needs of the business data and the running environment, including data encryption level, access control policy, security isolation level, etc. For example, data transmission throughout encryption, strict user identity verification and permission control, etc. These requirements are important basis to ensure data security and avoid unauthorized access during business running.

[0020] By accurately receiving the above business system executable file and running environment parameter, accurate data support can be provided for subsequent operations such as judging the running environment suitable for the business system, matching appropriate cloud computing resources, etc. to reduce deployment errors caused by information missing or inaccuracy, and lay a foundation for the smooth deployment of the business system.

[0021] Step 102, according to the performance index and security requirements, judge whether the business system is suitable for running in a container environment or a virtual machine environment.

[0022] In the embodiments of the present application, according to the performance indicators and security requirements obtained in advance, the type of environment suitable for running the business system, i.e. container environment or virtual machine environment, is scientifically judged. Among them, the performance indicators, as one of the judgment bases, cover the key performance requirements in the running process of the business system, such as expected concurrent access volume, data processing speed, response time, etc. These indicators directly reflect the demand of the business system for the carrying capacity and running efficiency of computing resources. For example, if the expected concurrent access volume of the business system is low, the sensitivity to resource occupation is high, and it needs to be quickly deployed and flexibly expanded to cope with short-term business fluctuations, such performance demand characteristics of light weight are more in line with the technical characteristics of container environment; while when the expected concurrent access volume of the business system is extremely high, the data processing task is complex and the requirement for running performance stability is strict, and it needs long-term stable resource support, its performance demand is more matched with the advantages of virtual machine environment.

[0023] The security requirement is also a core consideration factor for environment judgment, mainly including data encryption level, access control policy, security isolation level, etc. The data encryption level determines the security protection strength of business data in the transmission and storage process, the access control policy specifies the access permission range of different users or roles to the resources of the business system, and the security isolation level affects the logical isolation degree between the business system and other systems or resources. If the business system involves sensitive data processing, the requirement for data encryption level is extremely high, and strict access control and high-intensity security isolation are needed to prevent data leakage or unauthorized access, such high security demand is more suitable for implementation in virtual machine environment, because virtual machine can provide stronger security protection capability through hardware-level resource isolation; while for the business system with relatively moderate security requirement, which does not need extreme isolation protection, the container environment can meet the basic security demand due to its lightweight characteristics.

[0024] In the actual judgment process, the priority and specific parameters of performance indicators and security requirements need to be weighed comprehensively, and the technical differences between container environment and virtual machine environment, such as lightweight, flexibility, fast deployment of container environment and strong isolation, high stability of virtual machine environment, are combined to form a clear judgment logic, so as to ensure that the finally selected running environment can meet the performance demand of the business system and guarantee its running safety, and provide the correct direction for subsequent resource matching and business deployment.

[0025] This step effectively avoids the performance waste or security risk caused by improper environment selection by accurately matching the business demand and running environment, and provides a basic guarantee for the stable and efficient running of the business system.

[0026] Step 103, based on the running environment parameters, matching the cloud computing resource components meeting the performance indicators in the cloud resource library.

[0027] In the embodiments of the present application, relying on the previously obtained operating environment parameters, the cloud computing resource components that meet the business performance indicators are accurately screened out in the cloud resource library, providing adaptive resource support for subsequent business system deployment. The operating environment parameters here cover operating system types, middleware types, performance indicators, and security requirements, among which the operating system types specify the underlying system environment required for business operation; the middleware types point to the key component categories that support business functions, including database middleware (such as MySQL, Oracle), web middleware (such as Nginx, Apache), etc., which together constitute the basic screening conditions for resource matching. The cloud resource library, as a collection of various cloud computing resources, contains operating system images, middleware installation packages, and supporting components of different versions and different performance specifications, and can provide diversified resource selection for different business needs.

[0028] In the matching process, first, the operating system types and middleware types in the operating environment parameters are used as the basis for screening dimensions to preliminarily screen out a set of resource candidates that meet the underlying environment requirements of the business system from the cloud resource library. For example, if the business requires a Linux operating system and a MySQL database middleware, resources that do not meet the system and middleware types are excluded first. Then, the candidate set is further screened in combination with performance indicators, and the expected concurrent access volume, data processing speed, response time, etc. in the performance indicators become the core basis for determining whether the resources are adaptive. For example, when the business system expects a high concurrent access volume (such as 5000 people accessing simultaneously) and requires a data processing speed of 1000 records per second, the optimized version of the MySQL database middleware with high concurrent processing capability, the Linux operating system image supporting high throughput, and the high-performance web component are preferentially selected from the candidate set to ensure that the selected cloud computing resource components can meet the business's demand for running efficiency and avoid problems such as business lagging, response delay, etc. caused by insufficient resource performance. Through the explicit parameter mapping and performance verification logic, the entire matching process realizes the accurate connection from "demand" to "resource", ensuring that each selected cloud computing resource component is highly compatible with the business system's operating environment requirements and performance requirements. By accurately matching the cloud computing resource components, the performance waste or business operation limitation problems caused by resource mismatch are effectively avoided, providing reliable resource support for the subsequent stable and efficient operation of the business system in the target environment.

[0029] Step 104, according to the judgment result, creating a corresponding container or virtual machine environment, deploying the matched cloud computing resource components into the container or virtual machine environment, and performing initialization configuration.

[0030] In the embodiments of the present application, the pre-environment judgment and resource matching results are landed to the actual running environment. The core execution link needs to strictly follow the container or virtual machine environment judgment result obtained in step 102 to complete the environment creation, resource deployment and initialization configuration operation in stages.

[0031] The environment creation link needs to rely on the resource creation interface provided by the cloud computing platform to realize automatic operation. If the judgment result is a virtual machine environment, the system will automatically call the virtual machine creation interface of the cloud platform, generate a virtual machine instance that meets the requirements according to the potential resource requirements of the business system (such as CPU core number, memory capacity, storage specification, etc.), which can be indirectly derived from performance indicators, for example, higher CPU and memory configuration for high concurrency business), if the judgment result is a container environment, the container orchestration interface (such as Docker API, Kubernetes interface, etc.) of the cloud platform is called to create a lightweight container instance. The resource allocation of the container instance also refers to the performance indicators to ensure that it can carry the business running load.

[0032] Then is the cloud computing resource component deployment link. The resource components here are the operating system images, database middleware, web components and other PASS components matched in step 103. For a virtual machine environment, the system will import the matched operating system image into the created virtual machine through the image deployment tool of the cloud platform, and then install the database middleware, web component and the like in sequence through the automatic script to ensure that the component version is consistent with the business demand; for a container environment, a container image is constructed based on the matched operating system base image, and the database middleware, web component and the like are integrated into the container instance in a containerized deployment manner, and the lightweight characteristics of the container are used to realize rapid deployment of components.

[0033] Finally is the initialization configuration operation, which is carried out for the deployed resource components and running environment. For example, the database middleware is initialized, including creating a database instance required by the business, configuring the database connection parameters, setting the data storage path and backup strategy; the web component is configured, such as setting the port number, configuring the reverse proxy rule, optimizing the concurrent connection number, etc.; at the same time, the operating system is configured, such as closing unnecessary services, configuring firewall rules, setting system time zone and log storage strategy, etc. These initialization configuration operations are executed through automatic scripts to avoid errors in manual configuration and ensure that the running environment and resource components can directly meet the running requirements of the business system. Through automatic environment creation, component deployment and initialization configuration, the period from resource preparation to running state of the business system is greatly shortened, and the configuration accuracy of the running environment and resource components is guaranteed, laying a stable foundation for subsequent business system deployment and startup.

[0034] Step 105, deploy the business system executable file to the configured container or virtual machine environment, and start the business system.

[0035] In the embodiments of the present application, the business system executable file serves as the carrier of business functions, containing business logic code, function modules and running dependencies. The deployment process needs to perform adaptation operations in combination with the characteristics of the target environment (container or virtual machine). If the target environment is a virtual machine, the system will transmit the executable file to the preset business deployment directory (such as the / usr / local / business / directory) in the virtual machine through a remote file transmission protocol (such as the SSH file transfer protocol). The directory has been configured in advance and has the permissions and dependent environments required for executable file running. If the target environment is a container, the executable file is integrated into a container image that already contains matching cloud computing resource components through a container image building tool, or the executable file is directly mapped to a specified path inside the container through a container mounting directory method, ensuring that the environment inside the container can recognize and call the file.

[0036] After the file deployment is completed, the start operation will execute the corresponding start command according to the environment type. In the virtual machine environment, the system calls the preset start script (such as start.sh) through a remote command execution tool. The script checks the integrity of the executable file, the running state of the dependent components (such as whether the database middleware is started normally, whether the web component port is occupied), confirms that there is no error, starts the business process, and records the process ID to the specified log file, which is convenient for subsequent process management. In the container environment, the container instance containing the business executable file is started through a container management command (such as docker start, kubectl apply). The containerized start method automatically associates the component dependencies configured in advance, ensuring that the business process runs cooperatively with the middleware and operating system.

[0037] During the start process, the system also monitors the initialization state of the business system in real time. By checking whether the business port is normally listened to and whether it can return a preset health check response (such as HTTP 200 status code), it confirms whether the business system is successfully started. If an exception occurs during the start process (such as connection failure of dependent components, insufficient file permissions), error logs will be output in time and the start process will be suspended, providing a basis for subsequent problem troubleshooting. If the start is successful, the business system enters a normal running state and can start receiving and processing user requests. Through automated file deployment and start verification, the business system is ensured to be quickly and stably put into operation, avoiding start delays or configuration errors caused by manual operation. At the same time, through start state monitoring, a preliminary guarantee is provided for the reliable operation of the business system.

[0038] The application provides a business deployment method based on cloud computing. The system can automatically receive a business system executable file and a running environment parameter containing an operating system type, a middleware type, a performance index and a security requirement, judge an adaptive container or virtual machine environment according to the performance index and the security requirement, automatically match a cloud computing resource component satisfying the performance index based on the running environment parameter, and complete a whole-process operation of corresponding environment creation, component deployment initialization and business system startup. Therefore, the problems that the deployment efficiency is low, errors are prone to occur and it is difficult to accurately match the adaptive cloud computing resource and running environment according to the performance and security requirements of the business system due to the dependence on a large amount of manual operation in the prior art can be solved, the technical effects of reducing manual intervention, improving deployment efficiency, reducing human errors, accurately matching the cloud computing resource and business requirement, and ensuring stable operation of the business system are achieved.

[0039] In the embodiment of the application, for the operation of "judging whether the business system is suitable for running in a container environment or a virtual machine environment according to the performance index and the security requirement", the specific implementation manners are various. For clear description, the following specific introduction part introduces some example implementation manners: a preset decision model is used to perform weighted evaluation on the security requirement and the performance index, and output a running environment type suggestion; the final running environment is determined to be a container or a virtual machine according to the running environment type suggestion.

[0040] Specifically, the weighted evaluation by using the preset decision model is a specific and implementable implementation manner. First, the preset decision model needs to be constructed in advance in combination with the technical characteristics of the container environment and the virtual machine environment in the field of cloud computing and the differences in business scene requirements, to construct a multi-dimensional evaluation index system. The system takes the performance index and the security requirement as the core evaluation dimensions. The performance index can be refined into quantifiable parameters such as expected concurrent access volume, data processing speed and response time, and the security requirement can be decomposed into hierarchical indexes such as data encryption level, access control strictness and security isolation requirement.

[0041] In the model construction stage, the corresponding weight values need to be assigned to each refined index through historical business deployment data and industry best practice cases. For example, for business scenarios such as finance and medical care that have extremely high requirements for data security, the weight of data encryption level and security isolation requirement will be significantly higher than that of other indexes. In the actual evaluation process, the system first extracts the specific numerical values of the business system performance indexes and the security requirement levels obtained in step 101, and scores each index by comparing the evaluation scale. Then, according to the preset weight, the comprehensive score of the performance index and the security requirement is calculated. Subsequently, the model outputs the running environment type suggestion according to the comprehensive score and the preset environment determination rule. Finally, the system performs secondary verification according to the running environment type suggestion output by the decision model, in combination with the current resource load of the cloud platform. If the resources of the suggested environment can meet the business requirements, the final running environment is determined as a container or a virtual machine. If the resources of the suggested environment are tight and there is adjustment space for non-core indexes of the business, the suboptimal but resource-sufficient environment type will be selected after confirmation with the business system implementation personnel, to ensure that the environment judgment meets the core requirements of the business and has actual executability.

[0042] Further, in the embodiments of the present application, the decision model is a classification model based on machine learning, and the training data includes performance data, security configuration and corresponding running environment type of historical business systems.

[0043] Specifically, the composition of the training data needs to closely match the key elements of the business deployment scenario, specifically including the performance data, security configuration and corresponding running environment type of historical business systems. The performance data includes quantifiable indexes such as expected concurrent access volume, actual data processing speed and average response time in the actual operation of each historical business system, which directly reflects the load demand of the business on computing resources. The security configuration includes configuration information such as data encryption level, access control strategy and security isolation scheme adopted by the historical business system, which reflects the security protection demand of the business on the running environment. The corresponding running environment type is the final container or virtual machine environment result determined during the historical business deployment, which serves as the label data for model training and provides a basis for model learning of the “demand - environment” mapping relationship.

[0044] In the model training stage, the training data needs to be preprocessed first: the interpolation method or deletion method is used to correct the abnormal values in the performance data, and the non-quantitative information in the security configuration is encoded and converted into numerical features recognizable by the model. Subsequently, a suitable classification algorithm is selected according to the characteristics of the business scenario, such as logistic regression, random forest, gradient boosting tree (XGBoost) and the like. Considering the interactive influence of performance and security indexes in business deployment, an integrated learning algorithm that can capture complex correlations between features is preferred, which learns the “performance - security - environment” relationship by building multiple decision trees to reduce the bias of a single model.

[0045] In the model application stage, when receiving the performance indicators and security requirements of the current business system, the system will perform feature conversion on these data according to the pre-processing rules in the training stage, and input them into the trained machine learning classification model; the model outputs the prediction result of whether the current business system is suitable for the container environment or the virtual machine environment by calculating the association probability of each feature and the environment type.

[0046] Further, in the embodiments of the present application, the running environment parameters further include at least one of the expected concurrent access volume, data processing speed, response time, data encryption level, access control policy and security isolation level of the business system.

[0047] Specifically, as the core requirement basis for the deployment of the business system, in addition to the operating system type and the middleware type, the running environment parameters also explicitly cover at least one of the expected concurrent access volume, data processing speed, response time, data encryption level, access control policy and security isolation level of the business system. These parameters further refine the specific requirements of the business system for the cloud computing environment from the two key dimensions of performance and security, and provide more accurate decision basis for subsequent environment judgment and resource matching.

[0048] Among them, the expected concurrent access volume refers to the number of users or requests expected to access simultaneously during the normal operation period of the business system; the data processing speed is the number of data records that can be processed by the business system per unit time, which is an important indicator for selecting high-performance database middleware and server components; the response time is the delay time from the user's request to the system's result return, which is usually required to be controlled within 2 seconds. This parameter is closely related to network bandwidth and resource scheduling efficiency, and is a key performance standard for guaranteeing user experience.

[0049] From the security dimension, the data encryption level explicitly defines the encryption strength of the business data during transmission and storage, such as using SSL / TLS protocol for transmission encryption and AES-256 algorithm for storage encryption. Different encryption levels correspond to different security protection capabilities and also affect the selection of encryption components and key management services on the cloud platform; the access control policy specifies the access rights of different role users to the business system resources; the security isolation level reflects the isolation degree of the business system from other systems or resources, such as whether a separate network partition and dedicated computing node are needed. High isolation level usually corresponds to a virtual machine environment, and low isolation level can adapt to a container environment. This parameter is an important security basis for judging the running environment type.

[0050] In the embodiments of the present application, for the operation of "matching cloud computing resource components satisfying performance indicators in the cloud resource library based on running environment parameters", the specific implementation manners are various. For clear illustration, the following specific embodiments are introduced: filtering compatible operating system images in the cloud resource library according to the operating system type; matching database middleware and Web service components supporting corresponding performance levels according to the middleware type and performance indicators.

[0051] Specifically, compatible images are filtered according to the operating system type in the running environment parameters. The cloud resource library stores operating system images of various versions and different adaptation scenarios, and each image is marked with a clear system type, kernel version, supported hardware architecture and adapted business scenario. When filtering, the system first extracts the specified operating system type in the running environment parameters, matches the image marked information with the candidate image set consistent with the specified system type through the built-in search engine of the cloud resource library, then performs compatibility verification on the candidate set to exclude images with known vulnerabilities, discontinued maintenance or incompatibility with the current version of the cloud platform, and finally determines the operating system images that can be used for business deployment, ensuring that the underlying system environment is fully adapted to the business requirements and avoiding subsequent component deployment failures due to system incompatibility.

[0052] Subsequently, corresponding components are matched in combination with the middleware type and performance indicators. The middleware type in the running environment parameters specifies the component category required by the business, and the performance indicators specify the running capacity that the component needs to meet. For database middleware, the system first filters components consistent with the specified type from the cloud resource library, and then performs secondary filtering according to the performance indicators. For Web service components, candidate components are also filtered by type, and then matched in combination with performance indicators - if the business expects high concurrent access volume, the Nginx component supporting the event-driven model and capable of high-concurrency processing is preferred; if the business has high requirements on static resource loading speed, the Web service component version integrated with Gzip compression and cache acceleration function is selected to ensure that the Web service response time meets the performance indicator requirements. During the entire matching process, the system will real-time call the performance test report of the components in the cloud resource library and compare it with the business performance indicators. Only the components whose measured data meet or exceed the indicator requirements will be included in the final matching result, ensuring that the component performance fully supports the business operation requirements.

[0053] Further, in the embodiments of the present application, for the operation of "matching cloud computing resource components satisfying performance indicators in the cloud resource library based on running environment parameters", it also includes: performing compatibility verification on the selected components to verify compatibility with the selected operating system images and business system executable files.

[0054] Specifically, after the operating system image screening, the database middleware and the Web service component matching are completed, the compatibility verification link of the selected component is added, which is a key supplementary step to ensure the stable deployment and operation of the subsequent business system. The compatibility verification needs to be carried out around the two core dimensions of "selected component and operating system image" and "selected component and business system executable file". On the one hand, the compatibility of the selected component and the operating system image is verified. Since different operating system versions have differences in dependent libraries, running permissions and kernel interfaces of the component, the system automatically simulates the installation process of the component in the target operating system image, checks whether there are problems such as missing dependencies and permission conflicts in the component installation process, and returns an incompatible result if there is an exception, and reselects other component versions that are compatible with the operating system image from the cloud resource library.

[0055] On the other hand, the compatibility of the selected component and the business system executable file is verified. During the development of the business system executable file, the code is written based on a specific version of middleware interface and protocol specification. Therefore, during the compatibility verification, the system compares the configuration information of the business system executable file with the version information and supported interface types of the selected component; at the same time, by starting a lightweight test environment, the business system executable file and the selected component are deployed in the test environment to perform basic function tests and check whether there are problems such as connection failure, data format incompatibility and interface response exception. If the test is passed, it is determined that the component is compatible with the business system executable file; if the test fails, the incompatible reason is analyzed, and the cloud resource library is returned to match the component that meets the requirements of the business system executable file.

[0056] During the entire compatibility verification process, the system automatically records the verification results and exception information to form a compatibility report. If all the selected components pass the verification of the two dimensions, it is confirmed that the batch of cloud computing resource components can be used for subsequent deployment; if there are incompatible components, according to the problem type indicated in the report, the component version is upgraded, the missing dependent package is installed, etc. to solve the problem, if it cannot be solved, the resource matching process is reinitiated until the completely compatible components are screened out.

[0057] In the embodiments of the present application, for the operation of "creating a corresponding container or virtual machine environment according to the judgment result, and deploying the matched cloud computing resource component into the container or virtual machine environment", the specific implementation modes are various. For the sake of clear description, the following specific introduction part introduces some example embodiments: calling the application program interface provided by the cloud computing platform to create a virtual machine or container instance; installing and configuring the matched cloud computing resource component in the virtual machine or container instance; deploying the business system executable file and starting to run.

[0058] Specifically, the cloud computing platform application program interface (API) is called to create a virtual machine or container instance. The cloud computing platform provides standardized resource creation APIs, such as a virtual machine creation API that can support parameters such as CPU core number, memory capacity, storage type (such as SSD, HDD), network configuration (such as private IP, security group rules), and the like, and a container instance creation API that can specify container image version, resource quota, and mounted storage volume configuration. When executed, the system first reads the environment judgment result of step 102 - if it is a virtual machine environment, the resource requirements derived in the early stage according to the performance indicators (such as 4-core 8G CPU memory, 100G SSD storage for high-concurrency business) are extracted, these parameters are packaged into a request format that meets the platform API requirements, and the virtual machine creation interface is called; if it is a container environment, based on the matched operating system base image information, the container CPU usage rate upper limit, memory limit, and the like are set in combination with the business resource requirement, and the container creation API (such as the create interface of Docker, the create pod interface of Kubernetes) is called. After the interface is called, the system will listen to the creation status returned by the platform in real time, if it returns "creating", it will continue to wait, if it returns "creation success", it will obtain the instance unique identifier (such as virtual machine ID, container ID), and if it returns "creation failure" (such as insufficient resources, parameter error), it will adjust the parameters (such as reducing non-core resource configuration, replacing the availability zone) according to the error prompt and then call the interface again to ensure successful creation of the instance.

[0059] Next, the matched cloud computing resource components are installed and configured in the instance. For a virtual machine instance, the system connects the instance through a remote execution tool (such as SSH), first calls a corresponding package management tool (such as the yum or apt command for a Linux system) to install the basic dependent library based on the matched operating system image type, and then transmits the installation package of the matched database middleware (such as MySQL) and web service component (such as Nginx) in the cloud resource library to a specified directory of the instance to execute an automated installation script. For example, when installing MySQL, the script automatically sets the installation path, initializes the database service, and configures the listening port; when installing Nginx, the script automatically configures the service startup item and sets the default site directory. For a container instance, the system uses a containerized deployment method to integrate the matched components into a container image: first, use the filtered operating system base image as the bottom layer, write component installation instructions (such as "RUN apt install mysql-server") through a Dockerfile, build a custom container image containing the database middleware and web service component, push the image to the cloud platform image repository, and finally start the container instance by specifying the custom image through the container creation API to realize the integrated deployment of the component and the container. Regardless of the type of instance, after the component is installed, a basic configuration verification is performed, such as checking whether the database service is normally started and whether the web component port can normally listen, to ensure that the component is in a usable state.

[0060] In addition to the foregoing, the embodiments of the present application also include other specific implementation steps. To clearly present these components, the following detailed description of the related specific implementation is expanded: receiving a management instruction issued by a business system, the management instruction including expansion, start or shutdown; automatically performing a corresponding resource adjustment operation according to the instruction type, and updating the system state after the operation is completed.

[0061] Specifically, the system provides a unified instruction interaction entrance for business system implementers, which can be integrated in the business management interface or opened through an API interface form, supporting implementers to initiate expansion, start or shutdown instructions according to actual business needs. Among them, the expansion instruction usually needs to be accompanied by basic requirement parameters (such as the expected number of new users and the type of resources to be expanded), the start instruction needs to specify the target business instance identifier, and the shutdown instruction needs to confirm whether to retain configuration data; after the system receives the instruction, it will first verify the instruction format and parameter integrity, such as verifying whether the expansion instruction contains the "expected value of concurrent access after expansion", to avoid the subsequent operation from being unable to execute due to missing parameters, and after verification, the instruction is stored in the task queue and processed in turn according to the receiving order.

[0062] Subsequently, according to the instruction type, the corresponding resource adjustment operation is performed: for the expansion instruction, the system first combines the current running state of the business (such as the CPU usage rate and memory occupancy rate of the existing virtual machine / container, the current concurrent access volume) with the demand parameter in the instruction to automatically evaluate the amount of resources required for expansion - if the business runs in a virtual machine environment and the current virtual machine CPU usage rate has reached 80% and needs to support 2000 new concurrent users, then 2 new virtual machines of the same configuration are evaluated to be added; if it runs in a container environment, then 3 container instances are evaluated to be expanded. After the evaluation is completed, the cloud computing platform resource creation interface is called to create a corresponding number of virtual machine / container instances, the matching cloud computing resource components are deployed to the new instances and initialized synchronously, and finally the new user requests are distributed to the new instances through load balancing configuration to realize the expansion operation.

[0063] For the start instruction, the system first locates the virtual machine / container instance where the target business is located, suspends the running of the business system process in the instance (such as stopping the application service process and disconnecting the database connection), but retains the resource configuration and stored business data of the instance - in the virtual machine environment, the disk partition and component configuration file are retained, and in the container environment, the mounted storage volume data is retained, avoiding data loss due to start operation, and at the same time marking the instance state as "suspended running" for subsequent quick recovery.

[0064] For the close instruction, after stopping the business process, the system further releases the resources occupied by the instance: in the virtual machine environment, the virtual machine instance is logged out, the bound CPU, memory and storage resources are released, and the temporary configuration file is deleted; in the container environment, the container instance is deleted, the non-persistent storage data is cleaned up, only the business data that needs to be backed up for a long time is retained to the cloud storage service, and at the same time the resource occupation record is updated so that the released resources can be allocated to other businesses.

[0065] During the execution of all resource adjustment operations, the system records operation logs (such as instruction receiving time, resource adjustment quantity, execution result) in real time, automatically updates the business system state information (such as updating "current instance quantity" and "maximum concurrent capacity" after expansion, updating "running state" to suspended after start, and updating "resource occupation state" to released after closing) after the operation is completed, and synchronizes the state to the business management interface, so that the implementer can view the operation result in real time, ensuring that the management instruction execution is transparent and traceable.

[0066] Further, after receiving the expansion instruction issued by the business system, the expansion operation is performed according to the instruction type, including: monitoring the running state and resource usage of the current business system; automatically calculating the number of resources required to be increased according to the preset strategy; calling the expansion and contraction interface of the cloud computing platform to increase the number of container instances or virtual machines; adjusting the load balancing configuration to ensure stable operation of the business system.

[0067] Specifically, the system will collect the core running data of the container or virtual machine instance where the business system is located in real time through the monitoring interface provided by the cloud computing platform. The running state data includes business process survival state, request processing success rate, average response time, concurrent request number, and other key indicators. For example, real-time monitoring shows that the current concurrent request number has reached 8000 times per second, close to the system's preset concurrent upper limit of 10000 times per second, and the response time has been extended from the normal 1.2 seconds to 2.5 seconds. The resource usage data includes CPU usage, memory occupancy, disk I / O read / write speed, network bandwidth utilization, etc. For example, monitoring shows that the average CPU usage of the existing 3 virtual machines has reached 85%, the memory occupancy is over 70%, and the disk I / O is in a high load state. At the same time, the system will compare the collected data with the historical running baseline (such as the average resource usage and average response time of the same time period in the past 7 days) to identify the current resource bottleneck and provide data support for subsequent resource calculation, avoiding blind expansion.

[0068] Next, the system will automatically calculate the number of resources needed to increase according to the preset strategy. The preset strategy will be formulated based on business performance requirements, resource usage efficiency, and cost control targets. For example, the strategy stipulates that "when the CPU usage exceeds 80% for 5 minutes or the concurrent request number reaches 80% of the upper limit, expand by 30%-50% of the current total resources" and "if the response time exceeds 1.5 times the preset threshold, prioritize response speed, and the expansion ratio is the upper limit". Based on the monitoring data in the previous section, the current CPU usage is 85%, the concurrent request number reaches 80% of the upper limit, and the response time exceeds the threshold. According to the preset strategy, the expansion ratio is 50%, so 1-2 new virtual machines are needed for the existing 3 virtual machines. If further combined with the future 1-hour business growth prediction (such as predicting that the concurrent request number will increase to 9500 times per second based on historical data), the final determination is to add 2 virtual machines of the same configuration. If the business runs in a container environment, the existing 10 container instances need to be expanded by 5 container instances according to the 50% ratio, ensuring that the new resources can completely cover the current and short-term predicted resource gap, while avoiding excessive expansion leading to resource waste.

[0069] Subsequently, the scaling interface of the cloud computing platform is called to increase the number of container instances or virtual machines. The system will select the corresponding scaling interface according to the business running environment (container or virtual machine) judged in advance - if it is a virtual machine environment, the platform virtual machine scaling API is called, and the configuration parameters of the new virtual machine (such as CPU core number, memory capacity, storage type, consistent with the existing virtual machine to ensure compatibility), the corresponding availability zone, network security group rules, etc. Information is passed in; if it is a container environment, the Horizontal Pod Autoscaler interface of the container orchestration platform (such as Kubernetes) or the custom scaling API is called, and the number of new container instances, image version (consistent with the existing container image), resource quota, etc. Parameters are specified. After the interface is called, the system will listen to the instance creation progress in real time, obtain the IP address, instance ID, etc. of the new instance, and after the instance is created and passes the health check (such as checking whether the instance is started normally, whether it can establish a connection with the database middleware), it is confirmed that the scaling instance is in a usable state.

[0070] Finally, the load balancing configuration is adjusted to ensure stable operation of the business system. The system will call the load balancing service interface of the cloud computing platform to add the new container instance or virtual machine instance to the backend server list of the load balancing cluster, and update the load balancing strategy - if the round robin strategy is used, it needs to ensure that the request can be evenly distributed to all instances (including the original and new instances); if the weighted round robin strategy is used, a higher weight can be set according to the resource configuration difference of the instance (such as the CPU performance of the new instance is stronger), to improve the resource utilization efficiency. For example, the IP of the newly added 2 virtual machines is added to the load balancing backend, and the round robin weight is adjusted to make the request proportion received by each instance consistent, avoiding the performance problem of a single instance due to excessive requests. At the same time, the system will continuously monitor the request distribution and running state of each instance after adjustment, and if it is found that the request of a certain instance is too high or the response is abnormal, the load balancing weight will be adjusted in real time to ensure that the load of all instances is uniform, the overall response time of the business system returns to the normal range, and the concurrent processing capacity meets the demand, realizing the stable operation of the business after scaling.

[0071] Further, in the embodiments of the present application, the suspend operation includes suspending the operation of the business system but retaining its running environment and configuration information; the shutdown operation includes stopping the operation of the business system and releasing all cloud computing resources occupied by it.

[0072] Specifically, for the operation of closing, the core is to completely retain the running environment and configuration information of the business system while suspending its operation, providing a basis for subsequent rapid recovery of business. When executing, the system first locates the container or virtual machine instance where the target business system is located through the business management module, and sends a process suspension instruction to the instance - if it is a virtual machine environment, it will stop the application service process corresponding to the business system (such as the tomcat process of Java application, the uwsgi process of Python application), but will not shut down the virtual machine instance itself, while retaining the operating system image, database middleware, Web service components and all configuration files (such as database connection configuration, port mapping configuration, security policy configuration) deployed in the virtual machine. If it is a container environment, it will call container management commands (such as docker pause, kubectl pause) to suspend the business process in the container, retain the container instance and its mounted storage volume, network configuration, and ensure that the running environment and personalized configuration of the business system remain unchanged. After the operation is completed, the system marks the business state as "suspended running", and records the shutdown time, operator and other information. When the business needs to be recovered later, only a process restart instruction (such as starting the tomcat process, docker unpause command) is needed to quickly restore the business operation, without the need to redeploy the environment and configuration, greatly shortening the business interruption time.

[0073] For the shutdown operation, the focus is on completely releasing all cloud computing resources occupied by the business system after stopping its operation, avoiding resource idling and waste. In terms of process, the system first stops the business process (consistent with the process stopping logic of the shutdown operation), and then performs resource release operations for container or virtual machine environment - if it is a virtual machine environment, it will call the virtual machine deletion interface of the cloud computing platform, cancel the target virtual machine instance, release its occupied CPU, memory, storage resources and network IP address, and at the same time delete the temporary business data (such as log cache, temporary file) generated in the virtual machine, and only backup the core business data (such as user database data) that needs to be retained for a long time to the cloud storage service; if it is a container environment, it will execute container deletion commands (such as docker rm, kubectl delete pod) to remove the container instance, release the computing resources and storage volumes (non-persistent storage volumes) occupied by the container, and clean up the network configuration corresponding to the container. In addition, the shutdown operation will also release the resources occupied by the cloud computing resource components matched in the early stage (such as database middleware, Web service components), update the cloud platform resource occupation account, so that the released resources can be allocated to other businesses. After the operation is completed, the system updates the business state to "closed", and generates a resource release report to clearly indicate the type, quantity and release time of the released resources, facilitating the tracking of resource usage by operation and maintenance personnel.

[0074] In the embodiments of the present application, in addition to the foregoing, based on a preset graphical user interface or application program interface, the uploaded file, the set parameter, the sent management instruction and the viewed deployment state are received.

[0075] Specifically, from the perspective of the preset graphical user interface (GUI), the system will design a visual operation interface, and divide clear function areas to adapt to different operation needs. From the perspective of the application program interface (API), the system will provide standardized API interface documents, and clearly indicate the calling mode, request parameter, return format and error code of each interface, so as to be integrated into an automatic deployment script or a third-party system by a technical personnel through a programming mode. Whether the operation is through the GUI or the API, the system will uniformly check and process the received file, parameter and instruction.

[0076] It should be noted that the embodiments of the present disclosure can include a plurality of steps, which are numbered for the convenience of description, but these numbers are not a limitation on the execution time slot and execution order between the steps; the steps can be implemented in any order, and the embodiments of the present disclosure do not limit this.

[0077] Corresponding to the above-mentioned business deployment method based on cloud computing, the present disclosure also proposes a business deployment device based on cloud computing. Since the device embodiment of the present disclosure corresponds to the above-mentioned method embodiment, for the details not disclosed in the device embodiment, reference can be made to the above-mentioned method embodiment, which will not be described in detail in the present disclosure.

[0078] Figure 2 A structural schematic diagram of a business deployment device based on cloud computing provided by the embodiments of the present disclosure is shown in FIG. 1, which includes: Figure 2 A receiving unit 21 is configured to receive an uploaded business system executable file and a running environment parameter required for running the business system executable file, and the running environment parameter includes an operating system type, a middleware type, a performance index and a security requirement. A judging unit 22 is configured to judge whether the business system is suitable for running in a container environment or a virtual machine environment according to the performance index and the security requirement. A matching unit 23 is configured to match a cloud computing resource component satisfying the performance index in a cloud resource library based on the running environment parameter. A first deployment unit 24 is configured to create a corresponding container or virtual machine environment according to the judgment result, deploy the matched cloud computing resource component to the container or virtual machine environment, and perform initialization configuration. A second deployment unit 25 is configured to deploy the business system executable file to the configured container or virtual machine environment, and start the business system.

[0079] ​The application provides a cloud computing-based business deployment device. The system can automatically receive a business system executable file and a running environment parameter containing an operating system type, a middleware type, a performance index and security requirements. The performance index and the security requirements are used to determine an adaptive container or virtual machine environment. The cloud computing resource component meeting the performance index is automatically matched based on the running environment parameter, and the whole-process operation of corresponding environment creation, component deployment initialization and business system startup is completed. Therefore, the problems that the deployment efficiency is low, errors are prone to occur and it is difficult to accurately match the adaptive cloud computing resource and running environment according to the performance and security requirements of the business system due to the dependence on a large amount of manual operation in the prior art can be solved, and the technical effects of reducing manual intervention, improving deployment efficiency, reducing human errors, accurately matching cloud computing resources and business requirements and ensuring stable operation of the business system are achieved.

[0080] Further, in a possible implementation manner of the embodiment, as shown in Figure 3 The judgment unit 22 comprises: The evaluation module 221 is configured to perform weighted evaluation on the security requirements and the performance index by using a preset decision model, and output a running environment type suggestion. The determination module 222 is configured to determine that the final running environment is a container or a virtual machine according to the running environment type suggestion.

[0081] Further, in a possible implementation manner of the embodiment, the decision model is a classification model based on machine learning, and the training data comprises performance data, security configurations and corresponding running environment types of historical business systems.

[0082] Further, in a possible implementation manner of the embodiment, the running environment parameter further comprises at least one of an expected concurrent access volume, a data processing speed, a response time, a data encryption level, an access control policy and a security isolation level of the business system.

[0083] Further, in a possible implementation manner of the embodiment, as shown in Figure 3 The matching unit 23 comprises: The screening module 231 is configured to screen a compatible operating system image in the cloud resource library according to the operating system type. The matching module 232 is configured to match a database middleware and a Web service component supporting a corresponding performance level according to the middleware type and the performance index.

[0084] Further, in a possible implementation manner of the embodiment, as shown in Figure 3 The matching unit 23 further comprises: The verification module 233 is configured to perform compatibility verification on the selected component to verify the compatibility with the selected operating system image and the business system executable file.

[0085] Further, in a possible implementation manner of the embodiment, as shown in Figure 3 The first deployment unit 24 comprises: The creation module 241 is configured to invoke an application program interface provided by the cloud computing platform to create a virtual machine or a container instance. The configuration module 242 is configured to install and configure the matched cloud computing resource component in the virtual machine or the container instance. The deployment module 243 is configured to deploy the executable file of the business system and start running.

[0086] Further, in a possible implementation manner of the embodiment, as shown in Figure 3 The apparatus further comprises: The first receiving unit 26 is configured to receive a management instruction issued by the business system, and the management instruction comprises capacity expansion, suspension or shutdown. The execution unit 27 is configured to automatically perform a corresponding resource adjustment operation according to the type of the instruction, and update the system state after the operation is completed.

[0087] Further, in a possible implementation manner of the embodiment, the capacity expansion operation comprises: Monitoring the running state and resource usage of the current business system; Automatically calculating the required number of resources to be increased according to a preset strategy; Invoking a capacity expansion and contraction interface of the cloud computing platform to increase the number of container instances or virtual machines; Adjusting the load balancing configuration to ensure stable operation of the business system.

[0088] Further, in a possible implementation manner of the embodiment, the suspension operation comprises suspending the running of the business system but retaining the running environment and configuration information thereof, and the shutdown operation comprises stopping the running of the business system and releasing all cloud computing resources occupied by the business system.

[0089] Further, in a possible implementation manner of the embodiment, as shown in Figure 3 The apparatus further comprises: The second receiving unit 28 is configured to receive an uploaded file, set a parameter, send a management instruction and view a deployment state based on a preset graphical user interface or an application program interface.

[0090] It should be noted that the foregoing explanation and description of the method embodiment are also applicable to the apparatus of the embodiment, and the principle is the same, which is not limited in the embodiment.

[0091] The features in the embodiment of the business deployment apparatus based on cloud computing can be referred to the related description of the embodiment of the business deployment method based on cloud computing, which will not be repeated here.

[0092] Embodiments of the present application also provide an electronic device, comprising a memory and a processor, the memory storing a computer program, and the processor being configured to execute the computer program to perform the steps in any of the above-mentioned cloud computing-based service deployment method embodiments.

[0093] Embodiments of the present application also provide a computer readable storage medium, which stores a computer program, wherein the computer program is configured to perform the steps in any of the above-mentioned cloud computing-based service deployment method embodiments when executed.

[0094] In an example embodiment, the above-mentioned computer readable storage medium can include, but is not limited to, a U disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a mobile hard disk, a magnetic or optical disk, and other various media that can store computer programs.

[0095] Embodiments of the present application also provide a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the steps in any of the above-mentioned cloud computing-based service deployment method embodiments.

[0096] Embodiments of the present application also provide another computer program product, which comprises a non-volatile computer readable storage medium, and the non-volatile computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps in any of the above-mentioned cloud computing-based service deployment method embodiments.

[0097] Those skilled in the art can further appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in general terms in the above description. Whether the functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0098] The above describes in detail the business deployment method and device based on cloud computing, the electronic device and the storage medium provided by the application. The principles and implementation modes of the application are described by applying specific examples. The above description of the embodiments is only used to help understand the method of the application and its core idea. It should be pointed out that, for those skilled in the art, without departing from the principles of the application, the application can be improved and modified in several ways, and these improvements and modifications also fall within the protection scope of the claims of the application.

Claims

1. A cloud computing-based service deployment method, characterized by, The method comprises the following steps: receiving an uploaded business system executable file and its required running environment parameters, the running environment parameters including operating system type, middleware type, performance index and security requirement; judging whether the business system is suitable for running in a container environment or a virtual machine environment according to the performance index and the security requirement; matching a cloud computing resource component satisfying the performance index in a cloud resource library based on the running environment parameters; creating a corresponding container or virtual machine environment according to the judgment result, deploying the matched cloud computing resource component into the container or virtual machine environment, and performing initialization configuration; deploying the business system executable file into the configured container or virtual machine environment, and starting the business system.

2. The cloud computing-based business deployment method according to claim 1, characterized by, The step of judging whether the business system is suitable for running in a container environment or a virtual machine environment according to the performance index and the security requirement comprises the following steps: adopting a preset decision model to perform weighted evaluation on the security requirement and the performance index, and outputting a running environment type suggestion; determining the final running environment as a container or a virtual machine according to the running environment type suggestion.

3. The cloud-computing-based business deployment method according to claim 2, characterized by, The decision model is a classification model based on machine learning, and the training data includes performance data, security configuration and corresponding running environment type of historical business systems.

4. The method of claim 1, wherein, The running environment parameters further include at least one of expected concurrent access volume, data processing speed, response time, data encryption level, access control policy and security isolation level of the business system.

5. The cloud computing based business deployment method of claim 1, wherein, The step of matching a cloud computing resource component satisfying the performance index in a cloud resource library based on the running environment parameters comprises the following steps: screening a compatible operating system image in the cloud resource library according to the operating system type; matching a database middleware and a Web service component supporting a corresponding performance level according to the middleware type and the performance index.

6. The cloud-computing-based service deployment method according to claim 5, wherein The step of matching a cloud computing resource component satisfying the performance index in a cloud resource library based on the running environment parameters further comprises the following step: performing compatibility verification on the selected component to verify compatibility with the selected operating system image and the business system executable file.

7. The cloud-computing-based business deployment method according to claim 1, wherein The step of creating a corresponding container or virtual machine environment according to the judgment result, and deploying the matched cloud computing resource component into the container or virtual machine environment comprises the following steps: calling an application program interface provided by a cloud computing platform to create a virtual machine or a container instance; installing and configuring the matched cloud computing resource component in the virtual machine or the container instance; deploying the business system executable file and starting running.

8. The cloud-computing-based business deployment method according to claim 1, characterized by, The method further comprises the following steps: receiving a management instruction issued by the business system, the management instruction including expansion, start or shutdown; automatically performing a corresponding resource adjustment operation according to the instruction type, and updating the system state after the operation is completed. 9.The cloud-computing-based service deployment method according to claim 8, wherein, The expansion operation comprises the following steps: monitoring the running state and resource usage of the current business system; automatically calculating the required number of increased resources according to a preset strategy; calling an expansion and contraction interface of the cloud computing platform to increase the number of container instances or virtual machines; adjusting the load balancing configuration to ensure stable running of the business system. 10.The cloud-computing-based service deployment method according to claim 8, wherein, The starting operation includes suspending the operation of the business system but retaining the operation environment and configuration information thereof; and the closing operation includes stopping the operation of the business system and releasing all cloud computing resources occupied by the business system.

11. The cloud-computing-based business deployment method according to claim 1, characterized by, Further comprising: Based on a preset graphical user interface or application program interface, receiving an uploaded file, setting parameters, sending management instructions and viewing deployment status.

12. A cloud computing-based service deployment apparatus characterized by comprising: Further comprising: A receiving unit configured to receive an uploaded business system executable file and operation environment parameters required for operation of the business system, wherein the operation environment parameters include an operating system type, a middleware type, performance indicators and security requirements; A judging unit configured to judge whether the business system is suitable for running in a container environment or a virtual machine environment according to the performance indicators and the security requirements; A matching unit configured to match, based on the operation environment parameters, a cloud computing resource component satisfying the performance indicators in a cloud resource library; A first deployment unit configured to create a corresponding container or virtual machine environment according to a judgment result, deploy the matched cloud computing resource component into the container or virtual machine environment and perform initialization configuration; A second deployment unit configured to deploy the business system executable file into the configured container or virtual machine environment and start the business system.

13. An electronic device, comprising: Further comprising: At least one processor; And A memory connected in communication with the at least one processor; wherein The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the cloud computing-based business deployment method of any one of claims 1-11.

14. A non-transitory computer-readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to perform the cloud computing-based business deployment method of any one of claims 1-11.

15. A computer program product, characterised in that, A computer program, when executed by a processor, implements the cloud computing-based business deployment method of any one of claims 1-11.

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