Method and system for deploying hyper-converged all-in-one machine based on console program

Abstract

The invention relates to the technical field of cloud native technologies and digital platforms, in particular to a method and system for deploying a hyper-converged all-in-one machine based on a console program, and the method comprises the steps: receiving a deployment instruction inputted by a user through the console program; analyzing the deployment instruction through a control unit, wherein the control unit comprises a command line analyzer, a deployment engine, a configuration manager and a log recorder core component; the command line parser parses the deployment instruction into an executable operation; the method has the beneficial effects that by providing the preset deployment template and script and the automatic deployment tool, the deployment complexity is remarkably reduced, the consistency and efficiency of the deployment process are ensured, and the deployment of the large-scale cluster becomes simple and rapid.

Description

Technical Field

[0001] The present invention relates to the technical fields of cloud native technology and digital platform technology, and specifically provides a method and system for deploying a hyper-converged all-in-one machine based on a console program. Background Art

[0002] With the rapid development of cloud computing technology, the hyper-converged infrastructure (HCI), as a solution that highly integrates computing, storage, and network resources, has been widely used in enterprises and data centers due to its advantages such as high availability, easy scalability, and simplified operation and maintenance. However, traditional methods for deploying hyper-converged all-in-one machines mostly rely on graphical user interface (GUI) operations, which not only increase the complexity of deployment but also limit the degree of deployment automation.

[0003] Therefore, developing a deployment method based on a console program (CLI) to improve deployment efficiency and automation has important practical significance. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for deploying a hyper-converged all-in-one machine based on a console program to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A method for deploying a hyper-converged all-in-one machine based on a console program, including:

[0006] Receiving a deployment instruction input by a user using a console program;

[0007] Parsing the deployment instruction through a control unit, where the control unit includes core components such as a command line parser, a deployment engine, a configuration manager, and a logger;

[0008] The command line parser parses the deployment instruction into executable operations;

[0009] The deployment engine calls corresponding modular deployment scripts to execute operations. The modular deployment scripts cover independent and reusable components for network configuration, storage management, and computing resource allocation, and each component supports parameterized configuration;

[0010] The configuration manager manages the configuration information during the deployment process;

[0011] The logger records key information and error information during the deployment process in real time;

[0012] Providing multiple deployment strategies for users to choose from, including quick deployment, customized deployment, and rolling deployment, to meet the deployment requirements in different scenarios;

[0013] Integrated fault recovery mechanism, which automatically restarts or rolls back to the previous stable version when encountering problems, ensuring the reliability and stability of the deployment.

[0014] Preferably, the design of the control unit follows the following principles and has the following functions:

[0015] Ensure the stability and reliability of the control unit, while providing sufficient flexibility and scalability to support different deployment scenarios;

[0016] Core component functions: Command line parser: Parse the deployment instructions input by the user into executable operations; Deployment engine: Call the corresponding modular deployment scripts to execute operations, realizing automated tasks such as software installation, configuration generation, and resource allocation; Configuration manager: Manage the configuration information during the deployment process, including node information, network configuration, and storage configuration; Logger: Real-time record the key information and error information during the deployment process, providing support for troubleshooting and subsequent optimization.

[0017] Preferably, each component in the method supports the following actions:

[0018] Network component: Utilize SDN technology to realize the dynamic division and management of VLANs, improving network isolation and security; Design a DHCP server to realize the automatic allocation and recycling of IP addresses, simplifying the network configuration process; Adopt advanced routing algorithms to dynamically adjust the routing path according to the network topology and traffic patterns, optimizing data transmission efficiency;

[0019] Storage component: Develop an intelligent storage management system that supports the online creation, expansion, and deletion of virtual disks, improving the flexibility of storage resources; Design a data layout optimization algorithm to automatically adjust the distribution of data on physical storage according to the data access pattern and storage performance; Provide multiple recovery type options, such as RAID and Erasure Coding, to meet the storage reliability requirements of different scenarios;

[0020] Computing resource component: Design a resource scheduler to dynamically allocate CPU and memory computing resources according to application requirements and node performance, realizing the maximization of resource utilization; Integrate containerization technology to support the rapid deployment and isolation of applications, improving system scalability; Implement a resource monitoring and warning mechanism to real-time monitor the resource usage situation and prevent resource bottlenecks and overload;

[0021] Security component: Adopt TLS encrypted communication to ensure the security and privacy protection of data during transmission; Design a role-based access control model to realize fine-grained permission management and prevent unauthorized access; Integrate a security audit system to record and analyze security events, and timely discover and respond to potential security threats.

[0022] Preferably, the strategy design plan covers three aspects: deployment, fault recovery, and optimization, which are specifically as follows:

[0023] Deployment strategy: Quick deployment strategy: Preset templates and scripts, integration of automation tools, quick verification and feedback; Customized deployment strategy: Requirement analysis and customization, flexible configuration options, simulation testing and adjustment; Rolling deployment strategy: Gradual replacement and upgrade, health check and migration, rollback and recovery;

[0024] Fault recovery strategy: Automatic restart strategy: Fault detection and monitoring, quick restart and recovery; Configuration rollback strategy: Historical configuration management, one-key rollback function; Resource reallocation strategy: Dynamic resource scheduling, load balancing and optimization; Backup and recovery strategy: Regular backup mechanism, quick recovery function; Fault warning and diagnosis strategy: Intelligent warning system, fault diagnosis and location;

[0025] Optimization strategy: Load balancing strategy: Dynamic load balancing, multi-dimensional load balancing; Performance tuning strategy: Parameter optimization and adjustment, performance monitoring and analysis; Energy efficiency optimization strategy: Power management optimization, green energy-saving technology; Intelligent scheduling strategy: Resource intelligent scheduling, cross-node collaborative work; Capacity planning and expansion strategy: Capacity assessment and prediction, seamless expansion and upgrade.

[0026] Preferably, the specific implementation of the quick deployment strategy includes:

[0027] Preset templates and scripts: The system builds in a series of common deployment templates and scripts, covering various typical application scenarios; After the user selects the corresponding template and enters the necessary parameters, the deployment can be completed with one key, shortening the deployment cycle;

[0028] Integration of automation tools: Integrate automation deployment tools, supporting cross-platform and cross-environment automation deployment; Improve the efficiency and consistency of deployment;

[0029] Quick verification and feedback: After the deployment is completed, the system automatically conducts quick verification, including network connectivity, storage availability, and computing resource allocation; Provide detailed deployment reports and feedback to facilitate subsequent configuration and optimization by users.

[0030] A system for a method of deploying a hyper-converged all-in-one machine based on a console program includes:

[0031] Control unit: Used to parse the deployment instructions input by the user, call the corresponding deployment script to execute operations, and the control unit includes a command-line parser, a deployment engine, a configuration manager, and a logger;

[0032] Modular deployment script: Decompose the deployment process into multiple independent and reusable components according to network configuration, storage management, computing resource allocation, etc., and each component supports parameterized configuration;

[0033] Logging and Monitoring Module: Integrated into the system, it is used to track key information and error information during the deployment process in real time;

[0034] Multiple Deployment Strategy Module: Provides multiple deployment strategies such as rapid deployment and customized deployment for users to choose from;

[0035] Fault Recovery Mechanism Module: Can automatically restart or roll back to the previous stable version when encountering problems, ensuring the reliability and stability of the deployment.

[0036] Preferably, the design of the control unit follows the following principles and structure:

[0037] Design Principle: Ensure the stability and reliability of the control unit, while providing sufficient flexibility and scalability to support different deployment scenarios;

[0038] Core Components: Command Line Parser: Parses the deployment instructions input by the user through the console program into executable operations; Deployment Engine: Invokes the corresponding deployment scripts to execute the parsed operations; Configuration Manager: Manages the configuration information during the deployment process; Logger: Records the key information and error information during the deployment process;

[0039] The user inputs deployment instructions through the console program. The command line parser parses the instructions into executable operations. The deployment engine invokes the corresponding deployment scripts to execute the operations. The configuration manager manages the configuration information during the deployment process. The logger records the key information and error information.

[0040] Preferably, the components in the system have the following functions:

[0041] Network Component: Utilizes SDN technology to achieve dynamic partitioning and management of VLANs; Designs a DHCP server to achieve automatic allocation and recycling of IP addresses; Adopts advanced routing algorithms to dynamically adjust the routing path according to the network topology and traffic patterns;

[0042] Storage Component: Develops an intelligent storage management system that supports online creation, expansion, and deletion of virtual disks; Designs a data layout optimization algorithm to automatically adjust the distribution of data on physical storage according to the data access pattern and storage performance; Provides multiple restoration type options, such as RAID and Erasure Coding;

[0043] Computing Resource Component: Designs a resource scheduler to dynamically allocate computing resources such as CPU and memory according to application requirements and node performance; Integrates containerization technology to support rapid deployment and isolation of applications; Implements a resource monitoring and warning mechanism to monitor the resource usage situation in real time;

[0044] Security component: Adopt TLS encrypted communication; Design a role-based access control model to achieve fine-grained permission management; Integrate a security audit system to record and analyze security events.

[0045] Preferably, the deployment strategy includes:

[0046] Quick deployment strategy: The system builds in a series of common deployment templates and scripts. Users can complete the deployment with one click by selecting the corresponding template and inputting necessary parameters; Integrate an automated deployment tool to support cross-platform and cross-environment automated deployment; After the deployment is completed, the system automatically conducts a quick verification, including network connectivity, storage availability, and computing resource allocation, and provides a detailed deployment report and feedback;

[0047] Customized deployment strategy: Provide customized deployment solutions for specific business scenarios. Design by deeply analyzing user requirements and combining system resources and performance requirements; During the customized deployment process, users can select different components, configuration parameters, and deployment modes according to needs; Support the simulation test function to preview and verify the customized deployment plan, and users can adjust the plan in a timely manner according to the test results;

[0048] Rolling deployment strategy: Support gradually replacing or upgrading nodes in the cluster without affecting business operations; Conduct a health check on each node to ensure that the node is in a normal state before the migration task, and use load balancing and resource scheduling algorithms to smoothly migrate the task to other nodes; If problems are encountered during the rolling deployment process, support quickly rolling back to the previous stable version.

[0049] Preferably, the fault recovery and optimization strategy includes:

[0050] Fault recovery strategies: Automatic restart strategy: The system monitors the running status and performance metrics of each node in real time. Once an anomaly or fault is detected, it immediately triggers the automatic restart mechanism, quickly restarts the faulty node or service through pre-set restart scripts and processes, and ensures the integrity and consistency of data during the restart process; Configuration rollback strategy: The system saves the historical configuration versions of each node. Users can view and roll back to previous configuration versions at any time. In case of configuration errors or update failures, users only need to click the one-key rollback button to restore the node to its previous stable configuration state; Resource reallocation strategy: When a node fails, the system can dynamically adjust the resource configuration, migrate tasks to other healthy nodes, and use load balancing algorithms and optimization strategies to ensure the even distribution and efficient execution of tasks among nodes; Backup and recovery strategy: The system supports regular automatic backup functions, including data backup, configuration backup, etc. Users can set the backup period and backup strategy according to their needs. In case of data loss or damage, users can quickly restore data or configuration through backup files; Fault warning and diagnosis strategy: The system uses machine learning algorithms and data analysis techniques to predict and warn about the running status of nodes. Once potential fault risks are detected, it immediately notifies users and takes corresponding measures. When a fault occurs, the system provides detailed fault diagnosis reports and location information;

[0051] Optimization strategies: Load balancing strategy: The system dynamically adjusts the distribution of tasks among nodes according to the load conditions and performance metrics of nodes, achieving load balancing and performance optimization, and supporting multi-dimensional load balancing strategies based on CPU, memory, network bandwidth, etc.; Performance tuning strategy: The system provides rich performance parameter setting options. Users can optimize and adjust the parameters according to business requirements and system performance requirements, and use performance monitoring tools and analysis algorithms to monitor the performance metrics and bottleneck points of the system in real time, providing users with performance optimization suggestions; Energy efficiency optimization strategy: The system dynamically adjusts the power management strategy of nodes according to the load conditions and energy efficiency requirements, such as CPU frequency scaling, sleep mode, etc., reduces system energy consumption, and adopts advanced green energy-saving technologies to improve the energy efficiency level of the system and reduce carbon emissions; Intelligent scheduling strategy: The system uses machine learning algorithms and prediction models to intelligently schedule and optimize resources, dynamically adjusts resource allocation and task execution plans according to business requirements and system status, and supports cross-node collaborative working mechanisms to achieve efficient sharing and collaborative processing of resources; Capacity planning and expansion strategy: The system evaluates and predicts the capacity of the system based on historical data and business requirements, provides users with capacity planning suggestions to ensure that the system can meet future business needs, and supports the seamless expansion and upgrade functions of the system. Users can flexibly add nodes or upgrade system configurations according to business needs and development situations.

[0052] Compared with the prior art, the beneficial effects of the present invention are:

[0053] The method and system for deploying a hyper-converged all-in-one machine based on a console program proposed by the present invention significantly reduce the deployment complexity by providing pre-set deployment templates and scripts, as well as an automated deployment tool, ensuring the consistency and efficiency of the deployment process, making the deployment of large-scale clusters simple and fast; mechanisms such as real-time monitoring, automatic restart, one-key rollback, dynamic resource scheduling, and regular backup together constitute a powerful fault recovery system, effectively shortening the fault recovery time, reducing the risk of service interruption, and improving the stability and availability of the system; strategies such as dynamic load balancing, performance parameter tuning, performance monitoring and analysis, energy efficiency optimization, and intelligent resource scheduling together enhance the performance and resource utilization efficiency of the system, reduce the operation cost, and improve the business processing capacity and response speed; customized deployment options, rolling deployment strategies, capacity planning and expansion suggestions, etc., enable the system to flexibly adapt to changing business requirements, easily achieve seamless expansion and upgrade of the system, and extend the system's life cycle; through intelligent management tools and strategies, it reduces manual intervention and operation and maintenance workload, reduces the operation and maintenance cost, and at the same time provides rich monitoring, alarm, and diagnosis functions, making the operation and maintenance work simpler and more efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] In order to clearly and completely describe the objectives, technical solutions of the present invention, and make the advantages more clear, the following further details the embodiments of the present invention with reference to the drawings. It should be understood that the specific embodiments described herein are some embodiments of the present invention, rather than all embodiments, and are only used to explain the embodiments of the present invention, not to limit the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present invention.

[0056] Embodiment 1, please refer to Figure 1 , the present invention provides a technical solution: a method for deploying a hyper-converged all-in-one machine based on a console program, including:

[0057] The overall optimization of the deployment process is achieved through the console program (CLI). The solution adopts a modular design, decomposing the deployment process into multiple independent and reusable components, such as network configuration, storage management, computing resource allocation, etc. Each component supports parameterized configuration to meet personalized requirements in different scenarios.

[0058] As the core of the solution, the control unit is responsible for parsing the deployment instructions input by the user and calling the corresponding deployment scripts to execute operations. These scripts are carefully designed to automate cumbersome tasks such as software installation, configuration generation, and resource allocation, thus significantly improving the deployment efficiency. At the same time, the solution also integrates powerful logging and monitoring functions, which can track key information and error information during the deployment process in real time, providing strong support for troubleshooting and subsequent optimization.

[0059] To ensure the flexibility and scalability of the deployment, this solution provides multiple deployment strategies for users to choose from, such as rapid deployment, customized deployment, etc. Users can flexibly adjust according to actual needs. In addition, the solution also designs a perfect fault recovery mechanism, which can automatically restart or roll back to the previous stable version when problems occur, ensuring the reliability and stability of the deployment.

[0060] (I) Design solution:

[0061] Provide an efficient and automated method for deploying hyper-converged all-in-one machines based on console programs, aiming to simplify the deployment process, improve the deployment efficiency, enhance the deployment flexibility, and reduce the operation and maintenance costs. Core idea:

[0062] Use the console program (CLI) to implement the input and execution of deployment instructions.

[0063] Design modular deployment scripts to achieve automated deployment.

[0064] Adopt parameterized configuration to improve the flexibility and customizability of the deployment.

[0065] Integrate logging and monitoring functions to ensure the transparency and controllability of the deployment process.

[0066] (II) Control unit and model design

[0067] Control unit design

[0068] a. Design principle: Ensure the stability and reliability of the control unit, while providing sufficient flexibility and scalability to support different deployment scenarios.

[0069] b. Core components: Include command line parser, deployment engine, configuration manager, logger, etc.

[0070] c. Workflow: The user inputs deployment instructions through the console program, the command line parser parses the instructions into executable operations, the deployment engine calls the corresponding deployment scripts to execute operations, the configuration manager manages the configuration information during the deployment process, and the logger records key information and error information.

[0071] Model design

[0072] a. Deployment model: Adopt a modular design, breaking down the deployment process into multiple independent modules, such as network configuration module, storage configuration module, computing resource configuration module, etc.

[0073] b. Data model: Design a unified data model for storing and managing various data involved in the deployment process, such as node information, network configuration, storage configuration, etc.

[0074] (3) Component support actions

[0075] Network component: Design functions for VLAN configuration, dynamic IP address allocation, and precise routing setting to ensure seamless and secure data transmission between cluster nodes. Specifically:

[0076] a. Utilize SDN (Software-Defined Network) technology to achieve dynamic partitioning and management of VLANs, improving network isolation and security.

[0077] b. Design a DHCP (Dynamic Host Configuration Protocol) server to achieve automatic allocation and recycling of IP addresses, simplifying the network configuration process.

[0078] c. Adopt advanced routing algorithms to dynamically adjust the routing path according to the network topology and traffic pattern, optimizing data transmission efficiency.

[0079] Storage component: Achieve rapid creation of virtual disks, intelligent configuration of storage data layout, and flexible selection of recovery types to meet diverse storage requirements. Specifically:

[0080] a. Develop an intelligent storage management system to support online creation, expansion, and deletion of virtual disks, improving the flexibility of storage resources.

[0081] b. Design an algorithm for optimizing data layout to automatically adjust the distribution of data on physical storage according to the data access pattern and storage performance.

[0082] c. Provide multiple recovery type options, such as RAID (Redundant Array of Independent Disks), Erasure Coding, etc., to meet the storage reliability requirements in different scenarios.

[0083] Computing resource component: Provide precise allocation and adjustment of computing resources such as CPU and memory to ensure that performance matches the load requirements. Specifically:

[0084] a. Design a resource scheduler to dynamically allocate computing resources such as CPU and memory according to application requirements and node performance, achieving maximum utilization of resources.

[0085] b. Integrate containerization technology (such as Docker) to support rapid deployment and isolation of applications, improving system scalability.

[0086] c. Implement a resource monitoring and warning mechanism to monitor resource usage in real time and prevent resource bottlenecks and overloads.

[0087] Security components: Integrate security mechanisms such as encrypted communication and access control to ensure the security and data integrity of the deployment environment. Specifically:

[0088] a. Adopt TLS (Transport Layer Security Protocol) for encrypted communication to ensure the security and privacy protection of data during transmission.

[0089] b. Design a role-based access control (RBAC) model to achieve fine-grained permission management and prevent unauthorized access.

[0090] c. Integrate a security audit system to record and analyze security events, and promptly detect and respond to potential security threats.

[0091] (IV) Strategy Design Scheme

[0092] The strategy design scheme is the core of the deployment of the hyper-converged all-in-one machine of the present invention. It covers three key aspects: deployment, fault recovery, and optimization. Through refined strategy design, it ensures the efficient, stable, and sustainable development of the system. The following is a detailed elaboration of the strategies for these three aspects:

[0093] Deployment Strategy

[0094] The deployment strategy aims to simplify the deployment process, improve deployment efficiency, and ensure deployment flexibility and scalability. The present invention designs three strategies: rapid deployment, customized deployment, and rolling deployment to meet the deployment requirements in different scenarios.

[0095] a. Rapid Deployment Strategy:

[0096] Pre-set templates and scripts: The system has a series of commonly used deployment templates and scripts built-in, covering various typical application scenarios. Users only need to select the corresponding template and input the necessary parameters to complete the deployment with one click, greatly shortening the deployment cycle.

[0097] Integration of automation tools: Integrate automation deployment tools (such as Ansible, Terraform, etc.) to support cross-platform and cross-environment automated deployment, improving deployment efficiency and consistency.

[0098] Quick verification and feedback: After the deployment is completed, the system automatically conducts quick verification, including network connectivity, storage availability, computing resource allocation, etc., to ensure the success of the deployment. At the same time, detailed deployment reports and feedback are provided to facilitate users' subsequent configuration and optimization.

[0099] b. Customized Deployment Strategy:

[0100] Requirement Analysis and Customization: For specific business scenarios, the system provides customized deployment solutions. By deeply analyzing user requirements and combining system resources and performance requirements, a deployment solution that meets user expectations is designed.

[0101] Flexible Configuration Options: During the customized deployment process, users can select different components, configuration parameters, and deployment modes according to their needs to achieve highly personalized deployment.

[0102] Simulation Testing and Adjustment: Before the formal deployment, the system supports the simulation testing function to pre-run and verify the customized deployment solution. According to the test results, users can adjust the solution in a timely manner to ensure the smooth progress of the deployment.

[0103] c. Rolling Deployment Strategy:

[0104] Gradual Replacement and Upgrade: The rolling deployment strategy supports gradually replacing or upgrading nodes in the cluster without affecting business operations. Through batch-by-batch updates, smooth business transition and upgrade are achieved.

[0105] Health Check and Migration: During the rolling deployment process, the system performs a health check on each node to ensure that the node is in a normal state before the migration task. At the same time, using load balancing and resource scheduling algorithms, tasks are smoothly migrated to other nodes to avoid business interruption.

[0106] Rollback and Recovery: If any problems occur during the rolling deployment process, the system supports quickly rolling back to the previous stable version to ensure business continuity and stability.

[0107] Fault Recovery Strategy

[0108] The fault recovery strategy is the key to ensuring the high availability and reliability of the system. The present invention designs five strategies: automatic restart, configuration rollback, resource reallocation, backup and recovery, and fault early warning and diagnosis to cope with various possible fault situations.

[0109] a. Automatic Restart Strategy:

[0110] Fault Detection and Monitoring: The system monitors the running status and performance metrics of each node in real time. Once an anomaly or fault is detected, the automatic restart mechanism is immediately triggered.

[0111] Quick Restart and Recovery: Through pre-set restart scripts and processes, the system can quickly restart the faulty node or service and restore its normal running state. At the same time, ensure the integrity and consistency of data during the restart process.

[0112] b. Configuration Rollback Strategy:

[0113] Historical Configuration Management: The system stores the historical configuration versions of each node, including configuration files, parameter settings, etc. Users can view and roll back to previous configuration versions at any time.

[0114] One - click Rollback Function: When there is a configuration error or update failure, users can simply click the one - click rollback button to restore the node to its previous stable configuration state, reducing the risk of configuration errors.

[0115] c. Resource Re - allocation Strategy:

[0116] Dynamic Resource Scheduling: When a node fails, the system can dynamically adjust the resource configuration, migrate tasks to other healthy nodes, and ensure business continuity.

[0117] Load Balancing and Optimization: During the resource re - allocation process, the system uses load - balancing algorithms and optimization strategies to ensure the even distribution and efficient execution of tasks among nodes.

[0118] d. Backup and Recovery Strategy:

[0119] Regular Backup Mechanism: The system supports regular automatic backup functions, including data backup, configuration backup, etc. Users can set the backup period and backup strategy according to their needs.

[0120] Quick Recovery Function: When data is lost or damaged, users can quickly restore data or configuration through backup files, reducing the risk of data loss and business interruption.

[0121] e. Fault Warning and Diagnosis Strategy:

[0122] Intelligent Warning System: The system uses machine - learning algorithms and data - analysis techniques to predict and warn about the operating status of nodes. Once potential fault risks are detected, it immediately notifies users and takes corresponding measures.

[0123] Fault Diagnosis and Location: When a fault occurs, the system provides detailed fault diagnosis reports and location information to help users quickly locate the cause of the fault and take repair measures.

[0124] Optimization Strategy

[0125] The optimization strategy aims to improve the system's performance, resource utilization, and energy efficiency. The present invention designs five strategies: load balancing, performance tuning, energy - efficiency optimization, intelligent scheduling, and capacity planning and expansion to achieve the efficient operation and sustainable development of the system.

[0126] a. Load Balancing Strategy:

[0127] Dynamic Load Balancing: The system dynamically adjusts the distribution of tasks among nodes according to the load conditions and performance metrics of the nodes, achieving load balancing and performance optimization.

[0128] Multi-dimensional load balancing: It supports load balancing strategies based on multiple dimensions such as CPU, memory, network bandwidth, etc., ensuring the efficient operation of the system under different loads.

[0129] b. Performance tuning strategy:

[0130] Parameter optimization and adjustment: The system provides a rich set of performance parameter settings. Users can optimize and adjust the parameters according to business requirements and system performance requirements.

[0131] Performance monitoring and analysis: Through performance monitoring tools and analysis algorithms, the system can monitor the performance metrics and bottleneck points of the system in real time, providing performance optimization suggestions for users.

[0132] c. Energy efficiency optimization strategy:

[0133] Power management optimization: The system dynamically adjusts the power management strategy of nodes according to the load situation and energy efficiency requirements, such as CPU frequency scaling, sleep mode, etc., to reduce system energy consumption.

[0134] Green energy-saving technology: Adopt advanced green energy-saving technologies (such as energy efficiency ratio optimization, cooling system improvement, etc.) to improve the energy efficiency level of the system and reduce carbon emissions.

[0135] d. Intelligent scheduling strategy:

[0136] Intelligent resource scheduling: The system uses machine learning algorithms and prediction models to perform intelligent scheduling and optimization of resources. According to business requirements and system status, it dynamically adjusts resource allocation and task execution plans.

[0137] Cross-node collaborative work: It supports a cross-node collaborative work mechanism to achieve efficient resource sharing and collaborative processing. Through intelligent scheduling strategies, the overall performance and scalability of the system are improved.

[0138] e. Capacity planning and expansion strategy:

[0139] Capacity assessment and prediction: The system assesses and predicts the capacity of the system based on historical data and business requirements. It provides capacity planning suggestions for users to ensure that the system can meet future business needs.

[0140] Seamless expansion and upgrade: It supports the seamless expansion and upgrade functions of the system. Users can flexibly add nodes or upgrade system configurations according to business needs and development situations to achieve the sustainable development of the system.

[0141] Embodiment 2, based on Embodiment 1, proposes a system for a method of deploying a hyper-converged all-in-one machine using a console program, including:

[0142] Control Unit: It is used to parse the deployment instructions input by the user and call the corresponding deployment scripts to execute operations. The control unit includes a command-line parser, a deployment engine, a configuration manager, and a logger;

[0143] Modular Deployment Script: The deployment process is decomposed into multiple independent and reusable components according to network configuration, storage management, computing resource allocation, etc. Each component supports parameterized configuration;

[0144] Logging and Monitoring Module: Integrated into the system, it is used to track key information and error information during the deployment process in real time;

[0145] Multiple Deployment Strategy Modules: Provide multiple deployment strategies such as quick deployment and customized deployment for users to choose;

[0146] Fault Recovery Mechanism Module: It can automatically restart or roll back to the previous stable version when problems occur, ensuring the reliability and stability of the deployment.

[0147] The design of the control unit follows the following principles and structures: Design Principle: Ensure the stability and reliability of the control unit, while providing sufficient flexibility and scalability to support different deployment scenarios; Core Components: Command-line Parser: Parse the deployment instructions input by the user through the console program into executable operations; Deployment Engine: Call the corresponding deployment scripts to execute the parsed operations; Configuration Manager: Manage the configuration information during the deployment process; Logger: Record the key information and error information during the deployment process. The user inputs deployment instructions through the console program, the command-line parser parses the instructions into executable operations, the deployment engine calls the corresponding deployment scripts to execute the operations, the configuration manager manages the configuration information during the deployment process, and the logger records the key information and error information.

[0148] The components in the system have the following functions: Network components: Utilize SDN technology to achieve dynamic partitioning and management of VLANs; design a DHCP server to achieve automatic allocation and recycling of IP addresses; adopt advanced routing algorithms to dynamically adjust routing paths according to network topology and traffic patterns; Storage components: Develop an intelligent storage management system to support online creation, expansion, and deletion of virtual disks; design a data layout optimization algorithm to automatically adjust the distribution of data on physical storage according to data access patterns and storage performance; provide multiple recovery type options, such as RAID and Erasure Coding; Computing resource components: Design a resource scheduler to dynamically allocate computing resources such as CPU and memory according to application requirements and node performance; integrate containerization technology to support rapid deployment and isolation of applications; implement a resource monitoring and warning mechanism to monitor resource usage in real time; Security components: Adopt TLS for encrypted communication; design a role-based access control model to achieve fine-grained permission management; integrate a security audit system to record and analyze security events.

[0149] Deployment strategies include: Quick deployment strategy: The system has a series of built-in common deployment templates and scripts. Users can select the corresponding template and enter the necessary parameters to complete the deployment with one key; integrate automated deployment tools to support cross-platform and cross-environment automated deployment; after deployment, the system automatically conducts a quick verification, including network connectivity, storage availability, and computing resource allocation, and provides a detailed deployment report and feedback; Customized deployment strategy: Provide customized deployment solutions for specific business scenarios. Through in-depth analysis of user requirements, design is carried out in combination with system resources and performance requirements; during the customized deployment process, users can select different components, configuration parameters, and deployment modes according to their needs; support the simulation test function to preview and verify the customized deployment plan, and users can adjust the plan in a timely manner according to the test results; Rolling deployment strategy: Support gradually replacing or upgrading nodes in the cluster without affecting business operations; conduct a health check on each node to ensure that the node is in a normal state before the migration task, and use load balancing and resource scheduling algorithms to smoothly migrate the task to other nodes; if problems are encountered during the rolling deployment process, support quickly rolling back to the previous stable version.

[0150] Fault recovery and optimization strategies include:

[0151] Fault recovery strategies: Automatic restart strategy: The system monitors the running status and performance metrics of each node in real time. Once an anomaly or fault is detected, it immediately triggers the automatic restart mechanism, quickly restarts the faulty node or service through pre-set restart scripts and procedures, and ensures the integrity and consistency of data during the restart process; Configuration rollback strategy: The system saves the historical configuration versions of each node. Users can view and roll back to previous configuration versions at any time. In case of configuration errors or update failures, users only need to click the one-key rollback button to restore the node to its previous stable configuration state; Resource reallocation strategy: When a node fails, the system can dynamically adjust the resource configuration, migrate tasks to other healthy nodes, and use load balancing algorithms and optimization strategies to ensure the even distribution and efficient execution of tasks among nodes; Backup and recovery strategy: The system supports regular automatic backup functions, including data backup, configuration backup, etc. Users can set the backup period and backup strategy according to their needs. In case of data loss or corruption, users can quickly restore data or configuration through backup files; Fault warning and diagnosis strategy: The system uses machine learning algorithms and data analysis techniques to predict and warn about the running status of nodes. Once potential fault risks are detected, it immediately notifies users and takes corresponding measures. When a fault occurs, the system provides detailed fault diagnosis reports and location information;

[0152] Optimization strategies: Load balancing strategy: The system dynamically adjusts the distribution of tasks among nodes according to the load conditions and performance metrics of nodes, achieving load balancing and performance optimization, and supporting multi-dimensional load balancing strategies based on CPU, memory, network bandwidth, etc.; Performance tuning strategy: The system provides rich performance parameter setting options. Users can optimize and adjust the parameters according to business requirements and system performance requirements, and use performance monitoring tools and analysis algorithms to monitor the performance metrics and bottleneck points of the system in real time, providing users with performance optimization suggestions; Energy efficiency optimization strategy: The system dynamically adjusts the power management strategy of nodes according to the load conditions and energy efficiency requirements, such as CPU frequency scaling, sleep mode, etc., to reduce system energy consumption, and adopts advanced green energy-saving technologies to improve the energy efficiency level of the system and reduce carbon emissions; Intelligent scheduling strategy: The system uses machine learning algorithms and prediction models to intelligently schedule and optimize resources, dynamically adjusts resource allocation and task execution plans according to business requirements and system status, and supports cross-node collaborative working mechanisms to achieve efficient sharing and collaborative processing of resources; Capacity planning and expansion strategy: The system evaluates and predicts the capacity of the system based on historical data and business requirements, provides users with capacity planning suggestions to ensure that the system can meet future business needs, and supports the seamless expansion and upgrade functions of the system. Users can flexibly add nodes or upgrade system configurations according to business needs and development situations.

[0153] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for deploying a hyper-converged all-in-one machine based on a console program, characterized in that: It includes: Receiving the deployment instructions input by the user using a console program; Parsing the deployment instructions through a control unit, which includes core components such as a command-line parser, a deployment engine, a configuration manager, and a logger; The command-line parser parses the deployment instructions into executable operations; The deployment engine calls the corresponding modular deployment scripts to execute operations. The modular deployment scripts cover independent and reusable components for network configuration, storage management, and computing resource allocation, and each component supports parameterized configuration; The configuration manager manages the configuration information during the deployment process; The logger records the key information and error information during the deployment process in real time; Providing multiple deployment strategies for users to choose from, including quick deployment, customized deployment, and rolling deployment, to meet the deployment requirements in different scenarios; Integrating a fault recovery mechanism to automatically restart or roll back to the previous stable version when problems occur, ensuring the reliability and stability of the deployment.

2. The method for deploying a hyper-converged all-in-one machine based on a console program according to claim 1, wherein: The design of the control unit follows the following principles and has the following functions: Ensuring the stability and reliability of the control unit, while providing sufficient flexibility and scalability to support different deployment scenarios; Core component functions: Command-line parser: Parsing the deployment instructions input by the user into executable operations; Deployment engine: Calling the corresponding modular deployment scripts to execute operations to automate tasks such as software installation, configuration generation, and resource allocation; Configuration manager: Managing the configuration information during the deployment process, including node information, network configuration, and storage configuration; Logger: Recording the key information and error information during the deployment process in real time to provide support for troubleshooting and subsequent optimization.

3. A method for deploying a hyper-converged all-in-one machine based on a console program according to claim 2, characterized in that: Each component in the method supports the following actions: Network component: Using SDN technology to achieve dynamic partitioning and management of VLANs, improving network isolation and security; Designing a DHCP server to achieve automatic allocation and recycling of IP addresses, simplifying the network configuration process; Adopting an advanced routing algorithm to dynamically adjust the routing path according to the network topology and traffic pattern to optimize data transmission efficiency; Storage component: Developing an intelligent storage management system to support the online creation, expansion, and deletion of virtual disks, improving the flexibility of storage resources; Designing a data layout optimization algorithm to automatically adjust the distribution of data on physical storage according to the data access pattern and storage performance; Providing multiple recovery type options, such as RAID and Erasure Coding, to meet the storage reliability requirements in different scenarios; Computing resource component: Designing a resource scheduler to dynamically allocate CPU and memory computing resources according to application requirements and node performance to achieve the maximum utilization of resources; Integrating containerization technology to support the rapid deployment and isolation of applications, improving system scalability; Implementing a resource monitoring and warning mechanism to monitor the resource usage situation in real time to prevent resource bottlenecks and overload; Security component: Adopting TLS encrypted communication to ensure the security and privacy protection of data during transmission; Designing a role-based access control model to achieve fine-grained permission management to prevent unauthorized access; Integrating a security audit system to record and analyze security events to detect and respond to potential security threats in a timely manner.

4. The method for deploying a hyper-converged all-in-one machine based on a console program according to claim 3, wherein: The strategy design plan covers three aspects: deployment, fault recovery, and optimization, which are as follows: Deployment strategies: Quick deployment strategy: Preset templates and scripts, integrated with automation tools, quick verification and feedback; Customized deployment strategy: Requirement analysis and customization, flexible configuration options, simulation testing and adjustment; Rolling deployment strategy: Gradual replacement and upgrade, health check and migration, rollback and recovery; Fault recovery strategies: Automatic restart strategy: Fault detection and monitoring, quick restart and recovery; Configuration rollback strategy: Historical configuration management, one-key rollback function; Resource reallocation strategy: Dynamic resource scheduling, load balancing and optimization; Backup and recovery strategy: Regular backup mechanism, quick recovery function; Fault warning and diagnosis strategy: Intelligent warning system, fault diagnosis and location; Optimization strategies: Load balancing strategy: Dynamic load balancing, multi-dimensional load balancing; Performance tuning strategy: Parameter optimization and adjustment, performance monitoring and analysis; Energy efficiency optimization strategy: Power management optimization, green energy-saving technology; Intelligent scheduling strategy: Intelligent resource scheduling, cross-node collaborative work; Capacity planning and expansion strategy: Capacity assessment and prediction, seamless expansion and upgrade.

5. A method for deploying a hyper-converged all-in-one machine based on a console program according to claim 4, characterized in that: The specific implementation of the quick deployment strategy includes: Preset templates and scripts: The system has a series of commonly used deployment templates and scripts built-in, covering various typical application scenarios; After the user selects the corresponding template and enters the necessary parameters, the deployment can be completed with one key, shortening the deployment cycle; Integration of automation tools: Integrate automation deployment tools to support cross-platform and cross-environment automation deployment; Improve the efficiency and consistency of deployment; Quick verification and feedback: After the deployment is completed, the system automatically performs quick verification, including network connectivity, storage availability, and computing resource allocation; Provide detailed deployment reports and feedback to facilitate subsequent configuration and optimization by users.

6. A system for the method of deploying a hyper-converged all-in-one machine based on a console program according to claim 5, characterized in that: Include: Control unit: Used to parse the deployment instructions input by the user and call the corresponding deployment scripts to execute operations. The control unit includes a command-line parser, a deployment engine, a configuration manager, and a logger; Modular deployment scripts: Decompose the deployment process into multiple independent and reusable components according to network configuration, storage management, and computing resource allocation. Each component supports parameterized configuration; Logging and monitoring module: Integrated into the system to track key information and error information during the deployment process in real time; Multiple deployment strategy modules: Provide multiple deployment strategies such as quick deployment and customized deployment for users to choose; Fault recovery mechanism module: Can automatically restart or roll back to the previous stable version when problems occur, ensuring the reliability and stability of the deployment.

7. A system according to claim 6, characterized in that: The design of the control unit follows the following principles and structures: Design principle: Ensure the stability and reliability of the control unit, while providing sufficient flexibility and scalability to support different deployment scenarios; Core components: Command-line parser: Parse the deployment instructions input by the user through the console program into executable operations; Deployment engine: Call the corresponding deployment scripts to execute the parsed operations; Configuration manager: Manage the configuration information during the deployment process; Logger: Record the key information and error information during the deployment process; The user inputs deployment instructions through the console program. The command-line parser parses the instructions into executable operations. The deployment engine calls the corresponding deployment scripts to execute the operations. The configuration manager manages the configuration information during the deployment process. The logger records key information and error information.

8. A system according to claim 7, wherein: The components in the system have the following functions: Network component: Utilize SDN technology to achieve dynamic partitioning and management of VLANs; design a DHCP server to achieve automatic allocation and recycling of IP addresses; adopt advanced routing algorithms to dynamically adjust the routing path according to the network topology and traffic patterns. Storage component: Develop an intelligent storage management system to support online creation, expansion, and deletion of virtual disks; design a data layout optimization algorithm to automatically adjust the distribution of data on physical storage according to the data access pattern and storage performance; provide multiple restoration type options, such as RAID and Erasure Coding. Computing resource component: Design a resource scheduler to dynamically allocate CPU and memory computing resources according to application requirements and node performance; integrate containerization technology to support rapid deployment and isolation of applications. Implement a resource monitoring and warning mechanism to monitor the resource usage situation in real time. Security component: Adopt TLS encrypted communication; design a role-based access control model to achieve fine-grained permission management; integrate a security audit system to record and analyze security events.

9. A system according to claim 8, wherein: The deployment strategies include: Quick deployment strategy: The system has a series of built-in common deployment templates and scripts. Users can select the corresponding templates and input the necessary parameters to complete the deployment with one key; integrate automated deployment tools to support cross-platform and cross-environment automated deployment; after the deployment is completed, the system automatically conducts a quick verification, including network connectivity, storage availability, and computing resource allocation, and provides a detailed deployment report and feedback. Customized deployment strategy: Provide customized deployment solutions for specific business scenarios. Through in-depth analysis of user requirements, design in combination with system resources and performance requirements; during the customized deployment process, users can select different components, configuration parameters, and deployment modes according to their needs; support the simulation test function to preview and verify the customized deployment solution, and users can adjust the solution in a timely manner according to the test results. Rolling deployment strategy: Support gradually replacing or upgrading the nodes in the cluster without affecting business operations; conduct a health check on each node to ensure that the node is in a normal state before the migration task, and use load balancing and resource scheduling algorithms to smoothly migrate the tasks to other nodes; if problems are encountered during the rolling deployment process, support quickly rolling back to the previous stable version.

10. A system according to claim 9, characterized in that: The fault recovery and optimization strategies include: Fault Recovery Strategies: Automatic Restart Strategy: The system monitors the running status and performance metrics of each node in real time. Once an anomaly or fault is detected, the automatic restart mechanism is immediately triggered. Through the pre-set restart scripts and procedures, the faulty node or service is quickly restarted, and the integrity and consistency of data during the restart process are ensured; Configuration Rollback Strategy: The system saves the historical configuration versions of each node. Users can view and roll back to previous configuration versions at any time. When there is a configuration error or update failure, users only need to click the one-key rollback button to restore the node to the previous stable configuration state; Resource Reallocation Strategy: When a node fails, the system can dynamically adjust the resource configuration, migrate tasks to other healthy nodes, and use load balancing algorithms and optimization strategies to ensure the uniform distribution and efficient execution of tasks among nodes; Backup and Recovery Strategy: The system supports regular automatic backup functions, including data backup and configuration backup. Users can set the backup period and backup strategy according to their needs. When data is lost or damaged, users can quickly restore data or configuration through backup files; Fault Warning and Diagnosis Strategy: The system uses machine learning algorithms and data analysis techniques to predict and warn about the running status of nodes. Once potential fault risks are detected, users are immediately notified and corresponding measures are taken. When a fault occurs, the system provides a detailed fault diagnosis report and location information; Optimization Strategies: Load Balancing Strategy: The system dynamically adjusts the distribution of tasks among nodes according to the load conditions and performance metrics of nodes, achieving load balancing and performance optimization, and supporting load balancing strategies based on multiple dimensions such as CPU, memory, and network bandwidth; Performance Tuning Strategy: The system provides a rich set of performance parameter setting options. Users can optimize and adjust the parameters according to business requirements and system performance requirements, and through performance monitoring tools and analysis algorithms, real-time monitor the performance metrics and bottleneck points of the system, providing users with performance optimization suggestions; Energy Efficiency Optimization Strategy: The system dynamically adjusts the power management strategy of nodes according to the load conditions and energy efficiency requirements, such as CPU frequency scaling and sleep mode, to reduce system energy consumption, and adopts advanced green energy-saving technologies to improve the energy efficiency level of the system and reduce carbon emissions; Intelligent Scheduling Strategy: The system uses machine learning algorithms and prediction models to perform intelligent scheduling and optimization of resources. According to business requirements and system status, it dynamically adjusts resource allocation and task execution plans, and supports a cross-node collaborative working mechanism to achieve efficient sharing and collaborative processing of resources; Capacity Planning and Expansion Strategy: The system evaluates and predicts the capacity of the system based on historical data and business requirements, provides users with capacity planning suggestions to ensure that the system can meet future business needs, and supports the seamless expansion and upgrade functions of the system. Users can flexibly add nodes or upgrade system configurations according to business needs and development situations.

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