Modularization-based Kubernetes cluster automatic deployment system

Abstract

The invention relates to a Kubernetes cluster automatic deployment system based on modularization, and the system comprises a modularized cluster mirror image system which is used for providing a mirror image containing all assemblies of cluster operation and the optimal practice configuration of each assembly, and is used for flexibly customizing the assembly combination of a cluster according to different demands, and rapidly generating a customized cluster mirror image, the standardization of the delivery of the Kubernetes cluster is realized; the automatic deployment engine is used for defining a topological structure and configuration parameters of the cluster by adopting a declarative API (Application Program Interface), completing full-life-cycle automatic management through an architecture mode of a controller, and providing a monitoring alarm function which is ready to use after box opening; the application management system is used for providing application store based on Web, templated application configuration management and application real-time monitoring management; and the multi-tenant management system is used for providing resource isolation management, tenant authority management and security isolation management. According to the invention, the complexity of cluster deployment and management is greatly reduced, and the application efficiency and reliability of the cloud native technology are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of cloud computing, and in particular relates to a modularized Kubernetes cluster automated deployment system. Background Art

[0002] With the development of cloud-native technologies, Kubernetes has become the de facto standard in the field of container orchestration. However, in actual applications, the deployment and management of Kubernetes clusters still face the following problems:

[0003] 1. High deployment complexity: Traditional Kubernetes cluster deployment requires the configuration of a large number of components and the steps are cumbersome.

[0004] 2. High maintenance cost: Cluster updates and expansions require the intervention of professional operation and maintenance personnel, which results in high labor costs.

[0005] 3. Low application delivery efficiency: Distributed applications need to be repeatedly configured when deployed to different environments, and there is a lack of unified delivery standards.

[0006] 4. Lack of modularity: The components are highly coupled, making it difficult to customize on demand.

[0007] Therefore, there is an urgent need for a Kubernetes cluster automated deployment system that can simplify deployment, reduce maintenance costs, and improve delivery efficiency. Summary of the invention

[0008] The purpose of the present invention is to provide a modular Kubernetes cluster automated deployment system, which solves the problems of complex Kubernetes cluster deployment, high maintenance cost, and low delivery efficiency through modular design and automated deployment.

[0009] The present invention provides a modular Kubernetes cluster automatic deployment system, comprising:

[0010] The modular cluster image system is used to provide images that contain all components of cluster operation and the best practice configuration of each component. It can be used to flexibly customize the component combination of the cluster according to different needs, quickly generate customized cluster images, and realize the standardization of Kubernetes cluster delivery;

[0011] The automated deployment engine is used to define the cluster topology and configuration parameters using a declarative API interface, complete automated management of the entire life cycle through the controller architecture model, and provide out-of-the-box monitoring and alarm functions;

[0012] Application management system, which is used to provide a Web-based application store, templated application configuration management, and real-time application monitoring management;

[0013] Multi-tenant management system, used to provide resource isolation management, tenant rights management, and security isolation management.

[0014] Furthermore, the modular cluster mirroring system includes:

[0015] The image packaging module is used to package the various components required by the Kubernetes cluster, including container runtime, network plug-in, and storage plug-in, into a standardized OCI image; the image contains the binary files of each component, as well as the configuration files and startup scripts of the components, forming a complete cluster operating environment;

[0016] The component management module is used to manage component versions and handle dependencies through a component management mechanism based on version control and declarative APIs;

[0017] The image warehouse module is used to store the newly created cluster images pushed by the image packaging module through the image warehouse, and in the subsequent cluster deployment process, each node can directly pull the image from the image warehouse without accessing the external network;

[0018] Furthermore, the image packaging module is specifically used for:

[0019] 1) Configuration parsing: Parse the user-defined cluster configuration and extract the component list and dependencies;

[0020] 2) Resource preparation: download the binary files and dependencies of the components and generate the corresponding configuration files;

[0021] 3) Image building: Use the container engine API to build the OCI image, optimize the image size and set the version tag;

[0022] 4) Image distribution: push images to the specified image repository, record version information, and support offline export.

[0023] Furthermore, the image repository module is also used to:

[0024] Image version control: Each image pushed to the image repository is labeled with a unique version number so that users can distinguish and manage different versions of images;

[0025] Image incremental update: When the cluster image is updated, the image pushed to the image repository is the changed part after automatically calculating the difference between the new and old versions of the image, so as to avoid the full transmission of image data and speed up the image update;

[0026] Image security scanning: The built-in security scanning module of the image repository automatically scans newly pushed images for vulnerabilities, and promptly issues warnings and intercepts images with risks.

[0027] Furthermore, the automated deployment engine includes:

[0028] Deploy the configuration management module to ensure the legitimacy of user-defined configurations through the configuration verification algorithm. The steps are as follows:

[0029] 1) Parameter extraction: Extract key parameters of the cluster from the Cluster definition file, including version, node, and network plug-in;

[0030] 2) Dependency verification: check compatibility between component versions;

[0031] 3) Constraint verification: Verify whether the configuration complies with the agreed resource quota limit. Verification formula:

[0032]

[0033] Where: E is the error score of the configuration; Ci is the error coefficient of the i-th configuration item; Wi is the weight of the corresponding configuration item;

[0034] 3) Output diagnosis: Generate error reports for configurations that do not meet constraints;

[0035] Cluster lifecycle management module, used for cluster initialization, deployment, expansion, contraction, upgrade, and backup and recovery;

[0036] The monitoring and alarm module is used to monitor the running status of the cluster in real time, collect performance indicators and generate alarm information. The steps are as follows:

[0037] 1) Metrics collection: Collect the CPU, memory, and disk usage of the node through Node Exporter;

[0038] 2) Data processing: store and aggregate indicator data, calculate the average and maximum values, the formula is as follows:

[0039] Average formula:

[0040]

[0041] Used to smooth the fluctuations of indicators and provide a reference for the overall trend.

[0042] Alarm triggering formula:

[0043]

[0044] Among them: Mavg is the average value of a certain indicator; Mi is the indicator value collected for the i-th time; T is the alarm threshold; A indicates whether an alarm is triggered;

[0045] 3) Alarm triggering: When an indicator exceeds the preset threshold, an alarm is triggered and relevant personnel are notified.

[0046] 6. The modular Kubernetes cluster automated deployment system according to claim 5, wherein the cluster lifecycle management module is specifically used for:

[0047] Initialization: Automatically configure the basic environment of cluster nodes, including installing Docker and Kubeadm;

[0048] Deployment: According to the Cluster configuration file, the Kubernetes control plane and workload components are automatically deployed on the nodes to achieve one-click deployment;

[0049] Scaling: Horizontally scale the cluster, and automatically perform configuration and cleanup operations when adding or deleting nodes;

[0050] Upgrade: Perform rolling upgrades on the cluster and ensure uninterrupted business by controlling the upgrade process;

[0051] Backup and recovery: Provides a regular backup and recovery mechanism for cluster metadata and Etcd data to ensure high availability of the cluster.

[0052] Furthermore, the application management system includes:

[0053] The application store module is used to provide an application catalog and deployment interface for users to deploy applications to the Kubernetes cluster with one click; the applications include middleware, database, big data, and AI;

[0054] The application deployment module is used to provide templated application configuration functions, allowing users to declare configurable application parameters through customized templates, flexibly modify application configurations according to actual needs, and support unified deployment across environments;

[0055] The runtime management module is used to monitor the running status of the application in real time and provide configuration update, log collection, event tracking, and alarm functions.

[0056] Furthermore, the multi-tenant management system includes:

[0057] The resource isolation module is used to implement tenant isolation based on Kubernetes namespaces, and to set quota limits and default values ​​for namespace resource usage through two core resource objects: ResourceQuota and LimitRange.

[0058] The permission management module is used to implement permission management within and between tenants based on the Kubernetes RBAC mechanism;

[0059] The security isolation module is used to provide multi-level isolation of runtime, storage, and network.

[0060] Furthermore, the resource isolation module is specifically used for:

[0061] ResourceQuota allows cluster administrators to set CPU, memory, and storage usage quotas for each tenant's namespace. Once a tenant's resource usage exceeds the quota limit, Kubernetes automatically rejects new resource creation requests to ensure that the tenant does not occupy too many cluster resources and affect the normal use of other tenants.

[0062] LimitRange is used to set default resource requests and limits for Pods in a namespace to avoid resource abuse caused by Pods not setting resource limits.

[0063] Furthermore, the safety isolation module is specifically used for:

[0064] At the computing level, using the container runtime, a lightweight virtual machine environment is provided for each tenant's container, and the CPU and memory resources of the container are strictly limited within the virtual machine to avoid cross-tenant resource theft;

[0065] At the storage level, the storage space of different tenants is allocated to different storage devices to isolate storage IO performance to avoid mutual impact of different tenant applications, and encryption technology is used to encrypt storage volume data to ensure data security;

[0066] In terms of network, a multi-level network isolation solution is adopted: each tenant's application runs in an independent overlay network, network access between tenant applications is restricted through Network Policy, and VLAN or VPC is used to isolate tenant networks at a higher level to avoid cross-segment intrusions.

[0067] Through the above solution, the modular Kubernetes cluster automatic deployment system, modular cluster image system and automatic deployment engine are used to achieve efficient deployment and operation of Kubernetes clusters; standardized application delivery and management functions are provided through the application management system; and the isolation and security of tenant resources are ensured through the multi-tenant management system. The overall solution greatly reduces the complexity of cluster deployment and management, and improves the application efficiency and reliability of cloud native technology.

[0068] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is the overall architecture diagram of the system of the present invention;

[0070] Figure 2 This is a schematic diagram of a modular cluster mirroring system of the present invention;

[0071] Figure 3 This is a flow chart of automatic deployment of the present invention;

[0072] Figure 4 This is a schematic diagram of a multi-tenant management system of the present invention. DETAILED DESCRIPTION

[0073] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0074] This embodiment provides a modular Kubernetes cluster automatic deployment system, which includes:

[0075] The modular cluster image system is used to provide images that contain all components of cluster operation and the best practice configuration of each component. It can be used to flexibly customize the component combination of the cluster according to different needs, quickly generate customized cluster images, and realize the standardization of Kubernetes cluster delivery;

[0076] The automated deployment engine is used to define the cluster topology and configuration parameters using a declarative API interface, complete automated management of the entire life cycle through the controller architecture model, and provide out-of-the-box monitoring and alarm functions;

[0077] Application management system, which is used to provide a Web-based application store, templated application configuration management, and real-time application monitoring management;

[0078] Multi-tenant management system, used to provide resource isolation management, tenant rights management, and security isolation management.

[0079] The present invention is further described in detail below. Figures 1 to 4 shown.

[0080] 1. Modular cluster mirroring system

[0081] Traditional Kubernetes cluster deployment methods usually start from scratch based on tools such as Kubeadm, or use automated deployment solutions such as Kops and Kubespray. However, these methods generally have problems such as low customization and difficulty in upgrading.

[0082] In order to change this situation, this embodiment packages various components of the Kubernetes cluster, such as Etcd, APIServer, ControllerManager, Scheduler, and network plug-ins Flannel and Calico, into a standard OCI (Open Container Initiative) image, forming a new cluster distribution method. This image not only contains all the components required for cluster operation, but also has built-in best practice configurations for each component.

[0083] Based on this image, users can flexibly customize the component combination of the cluster according to their needs. For example, they can choose different versions of Kubernetes core components, different types of network solutions, whether to enable Ingress Controller, etc. An interactive cluster customization interface is provided, and users can quickly generate a customized cluster image by simply checking the required components.

[0084] At the same time, thanks to the immutability and version management mechanism of container images, clusters can be easily upgraded and rolled back. Each customized cluster image has a unique version number that can be pushed to the image repository for storage. When the cluster needs to be upgraded, just pull the latest image version and restart the cluster; if problems occur after the upgrade, you can also quickly roll back to the previous stable version. This greatly reduces the complexity of cluster maintenance.

[0085] In addition, it also supports exporting cluster images as offline packages for deploying clusters in environments that cannot connect to the external network. In this way, the delivery standardization of Kubernetes clusters can be achieved, significantly improving the efficiency of deployment and operation and maintenance.

[0086] In a specific implementation, the modular cluster image system is used to solve the problems of environment dependency and version inconsistency during Kubernetes cluster deployment, and to achieve standardization and automation of cluster delivery. The system mainly includes the following steps:

[0087] (1) Image packaging

[0088] Image packaging is the core step of the modular cluster image system. Its purpose is to package the various components required by the Kubernetes cluster, such as container runtime, network plug-in, storage plug-in, etc., into a standardized OCI image. This image not only contains the binary files of each component, but also the configuration files and startup scripts of the components, which can be regarded as a complete cluster operating environment.

[0089] In Kubernetes cluster deployment, inconsistent component environments and complex configuration processes can lead to inefficient deployment. The modular cluster image system provides rapid deployment capabilities through standardized component images.

[0090] The main steps of image packaging are as follows:

[0091] 1) Configuration parsing: Parse the user-defined cluster configuration and extract the component list and dependencies.

[0092] 2) Resource preparation: Download the binary files and dependencies of the components and generate the corresponding configuration files.

[0093] 3) Image building: Use the container engine API to build the OCI image, optimize the image size and set the version tag.

[0094] 4) Image distribution: push images to designated warehouses, record version information, and support offline export.

[0095] The total image build time formula is as follows:

[0096] T total =T download +T config +T build ;

[0097] in:

[0098] T total The total build time of the image.

[0099] T download The time when the component was downloaded.

[0100] T config Generates time for configuration.

[0101] T build The image build time.

[0102] The specific contents of each stage of image packaging are as follows:

[0103] 1) Configuration parsing stage:

[0104] Parse cluster image resource definition;

[0105] Extract component list information;

[0106] Verify component version validity;

[0107] Check dependencies between components.

[0108] 2) Resource preparation stage:

[0109] Download component binaries;

[0110] Prepare component configuration files;

[0111] Create a temporary build directory;

[0112] Gather the required dependency files.

[0113] 3) Image building phase:

[0114] Generate Dockerfile file:

[0115] Select a base image;

[0116] Add component binary files;

[0117] Configuration file path mapping;

[0118] Set the startup command.

[0119] Execute image building:

[0120] Call the container engine API;

[0121] Perform hierarchical builds;

[0122] Optimize image size.

[0123] 4) Image distribution stage:

[0124] Tag the image version;

[0125] Push to the image repository;

[0126] Clean up temporary files;

[0127] Update the mirror status.

[0128] Mirror structure design:

[0129] 1) File system layout:

[0130] Binary file directory ( / usr / bin);

[0131] Configuration file directory ( / etc);

[0132] Plugin directory ( / opt);

[0133] Data directory ( / var).

[0134] 2) Configuration file management:

[0135] Centralized storage of configuration files;

[0136] Support configuration file templates;

[0137] Runtime configuration injection;

[0138] Multi-environment configuration adaptation.

[0139] Image packaging implements modular component management, declarative configuration management, standardized image building, automated distribution mechanism, and versioned dependency management.

[0140] Exception handling mechanism:

[0141] 1) Build exception handling:

[0142] Component download failure processing;

[0143] The build process is restored abnormally;

[0144] Space shortage early warning mechanism;

[0145] Version conflict detection.

[0146] 2) Distribution exception handling:

[0147] Network timeout retry;

[0148] Warehouse authentication failure processing;

[0149] Image integrity check;

[0150] State rollback mechanism.

[0151] The standardized image packaging process enables unified management and distribution of Kubernetes cluster components. The system allows users to define cluster images in a declarative way through custom resources, greatly simplifying the cluster delivery process. At the same time, the modular design makes the system easy to expand and maintain.

[0152] (2) Component Management

[0153] When making a cluster image, in addition to defining which components are included in the image, you also need to manage versions and handle dependencies for these components. This is because there are complex version compatibility issues between Kubernetes components, and some components require specific versions to ensure cluster stability. In actual use, components often need to be upgraded and rolled back to fix bugs or introduce new features.

[0154] To solve these problems, the system's modular cluster image system introduces a declarative component management mechanism. Specifically, a custom resource called ComponentDefinition is used to describe the properties of each component, such as name, version, dependencies, etc., and then these definitions are saved in a version control system (such as Git) for management.

[0155] The following is an example of a ComponentDefinition:

[0156] Processing flow:

[0157] 1) Version definition stage

[0158] Define components through ComponentDefinition resources;

[0159] Specify the list of versions supported by the component;

[0160] Declare dependencies between versions;

[0161] Set version specific configuration parameters.

[0162] 2) Dependency checking phase

[0163] Parse the component version selected by the user;

[0164] Get the dependency list of this version;

[0165] Verify the compatibility of dependent component versions;

[0166] Generates a complete component dependency tree.

[0167] 3) Upgrade execution phase

[0168] Receive component upgrade request;

[0169] Verify the validity of the new version;

[0170] Automatically pull the new version image;

[0171] Rebuild the cluster image;

[0172] Perform the upgrade operation.

[0173] 4) Rollback processing phase

[0174] Save the version information before upgrading;

[0175] Monitor the status of the upgraded system;

[0176] Start rollback when an exception is found;

[0177] Revert to a historical version.

[0178] This component management implements a declarative version management mechanism, automated dependency resolution, version compatibility verification, a reliable upgrade rollback mechanism, and complete status tracking.

[0179] Exception handling:

[0180] 1) Version conflict handling;

[0181] Detect version incompatibility;

[0182] Generate detailed conflict reports;

[0183] Provide solution suggestions.

[0184] 2) Upgrade failure handling

[0185] Record detailed reasons for failure;

[0186] Automatically trigger the rollback mechanism;

[0187] Maintain system state consistency.

[0188] 3) Dependency missing processing

[0189] Prompts missing dependent components;

[0190] Suggest available version choices;

[0191] Prevent incomplete deployments.

[0192] Through this component management mechanism based on version control and declarative API, the system realizes flexible management and automated operation and maintenance of Kubernetes cluster components, significantly reducing the complexity and operation and maintenance costs of cluster maintenance.

[0193] (3) Mirror repository

[0194] When deploying Kubernetes clusters on a large scale within an enterprise, a private image repository is usually required to store and distribute the container images required by the cluster. On the one hand, this can avoid frequently pulling images from the public network and speed up image distribution; on the other hand, it can also ensure the use of self-verified security images to prevent backdoors and vulnerabilities.

[0195] The modular cluster image system also supports the construction of private image repositories. When a user creates a new cluster image through ClusterImage, the system automatically pushes it to the built-in image repository for storage. In the subsequent cluster deployment process, each node can directly pull the image from this internal repository without accessing the external network.

[0196] At the same time, in order to facilitate the management and use of images, the system's image repository also provides some advanced features, such as:

[0197] Image version control: Each image pushed to the repository will be labeled with a unique version number, so users can easily distinguish and manage different versions of images.

[0198] Incremental image update: When a cluster image is updated, the system automatically calculates the difference between the old and new versions of the image and pushes only the changed parts to the warehouse, avoiding the full transmission of image data and speeding up image updates.

[0199] Image security scanning: In order to improve the security of images, the system's image repository has a built-in security scanning function, which can automatically scan newly pushed images for vulnerabilities and promptly warn and intercept risky images.

[0200] Processing flow:

[0201] 1) Image push phase

[0202] Verify the integrity of image information;

[0203] Construct API request parameters;

[0204] Add authentication information;

[0205] Read the contents of the image file;

[0206] Execute push operation;

[0207] Verify the push result.

[0208] 2) Construct a pull request during the image pull phase;

[0209] Verify warehouse authentication information;

[0210] Get image metadata;

[0211] Download mirror content;

[0212] Verify image integrity;

[0213] Save to local storage; 3) Verify deletion permissions during the image deletion phase;

[0214] Construct a delete request;

[0215] Execute the delete operation;

[0216] Clean up related resources;

[0217] Update the mirror index. Other technical points:

[0218] 1) Interface abstraction design defines standard operation interfaces;

[0219] Support multiple warehouse implementations;

[0220] Facilitates functional expansion;

[0221] Unified error handling.

[0222] 2) The authentication security mechanism supports multiple authentication methods;

[0223] Secure credential management;

[0224] Request signature verification;

[0225] Access permission control.

[0226] 3) Data transmission management

[0227] Large file transfer optimization;

[0228] Breakpoint resume support;

[0229] Data integrity check;

[0230] Transfer progress tracking.

[0231] Exception handling:

[0232] 1) Network exception handling

[0233] Connection timeout retry;

[0234] Resume and restore the data from breakpoint;

[0235] Adaptive bandwidth adjustment;

[0236] Reschedule failed tasks.

[0237] 2) Authentication exception handling

[0238] Renewal of expired credentials;

[0239] Retry if authentication fails;

[0240] Insufficient permissions prompt;

[0241] Security logging.

[0242] 3) Storage exception handling

[0243] Insufficient space check;

[0244] Concurrency conflict handling;

[0245] File corruption repair;

[0246] Junk data cleaning.

[0247] Through this plug-in image warehouse implementation, the system can flexibly connect to different image warehouse services to meet the personalized needs of users. In actual deployment, users only need to specify the address, authentication information and other parameters of the image warehouse through the configuration file, and the system can automatically push the cluster image to the corresponding warehouse to achieve unified distribution and management of images.

[0248] In short, the modular cluster image system is a core component of the system. Through a series of mechanisms such as image packaging, component management, and image distribution, it realizes the standardization and automation of Kubernetes cluster delivery, solves problems such as cluster environment dependence, version inconsistency, and difficulty in management, and provides strong support for enterprises to deploy Kubernetes clusters on a large scale.

[0249] 2. Automated deployment engine

[0250] After customizing the cluster image, an automated deployment engine is needed to quickly pull the image into a complete and available Kubernetes cluster. Different from traditional command-line deployment and Ansible-based declarative deployment, a declarative API+controller architecture is used to achieve automated cluster delivery.

[0251] First, a set of declarative API interfaces are exposed to define the cluster topology and configuration parameters. These APIs are implemented based on Kubernetes' CRD (Custom Resource Definition) mechanism, which treats the cluster as a custom resource and describes its parameter specifications through YAML files, such as cluster name, Pod and Service network segments, host nodes, image versions, etc. Git is also introduced as a version control system for cluster configuration, so users can manage cluster configuration files like managing code.

[0252] Secondly, an automated deployment controller is implemented. It continuously watches the API Server to obtain changes in cluster resources. Based on the cluster parameters submitted by the user, it automatically completes the automated management of various life cycle links, from image pulling, configuration file rendering to cluster startup and shutdown. In the event of a node failure, the controller can also automatically try to restore the cluster based on the declared status of the cluster resources to ensure high availability of the cluster.

[0253] At the same time, thanks to the declarative API + controller architecture, multiple Kubernetes clusters can be easily managed. Users only need to submit a CRD definition file for each cluster to perform parallel automatic deployment and configuration management for multiple clusters at the same time. Combined with the cluster mirroring function, even hundreds or thousands of clusters can achieve fast delivery.

[0254] In addition, a simple and easy-to-use UI interface is provided to visually display the cluster topology, monitor the running status of each component, and view cluster resources such as Pod and Service. By combining with the declarative API, users can edit cluster configurations in a visual way on the UI interface and trigger cluster upgrades.

[0255] In short, the automated deployment engine greatly simplifies the deployment and operation of Kubernetes clusters, and realizes truly fully automated, full-lifecycle cluster management. This not only lowers the threshold for using Kubernetes, but also provides an efficient and reliable means for enterprises to implement Kubernetes.

[0256] In a specific implementation, the automated deployment engine is used to simplify and standardize the deployment process of the Kubernetes cluster. The engine mainly includes the following modules:

[0257] (1) Deployment configuration management module:

[0258] When deploying a Kubernetes cluster, we need to set a large number of configuration parameters for the cluster, such as cluster version, number of nodes, network plug-ins, etc. The system's automated deployment engine introduces a declarative deployment configuration management mechanism. In the declarative API, configuration errors may cause cluster deployment failure. The system uses a configuration verification algorithm to ensure the legitimacy of the user-defined configuration. The steps are as follows:

[0259] 1) Parameter extraction: Extract key parameters such as cluster version, nodes, network plug-ins, etc. from the Cluster definition file.

[0260] 2) Dependency verification: Check the compatibility between component versions.

[0261] 3) Constraint verification: Verify whether the configuration complies with the agreed resource quota limit. Verification formula:

[0262]

[0263] Where: E is the error score of the configuration; Ci is the error coefficient of the i-th configuration item; Wi is the weight of the corresponding configuration item.

[0264] 4) Output diagnostics: Generate error reports for configurations that do not meet constraints.

[0265] (2) Cluster lifecycle management module:

[0266] The system's automated deployment engine provides comprehensive cluster lifecycle management capabilities, supporting full-process automation from cluster creation to destruction. The main lifecycle stages include:

[0267] Initialization: Automatically configure the basic environment of the cluster nodes, such as installing necessary components such as Docker and Kubeadm.

[0268] Deployment: According to the Cluster configuration file, the Kubernetes control plane and workload components are automatically deployed on the nodes to achieve one-click deployment.

[0269] Scaling: Supports horizontal expansion and contraction of clusters. When adding or deleting nodes, the engine automatically performs necessary configuration and cleanup operations.

[0270] Upgrade: Support rolling upgrade of clusters. The engine will control the upgrade process to ensure uninterrupted business.

[0271] Backup and recovery: Provides a regular backup and recovery mechanism for cluster metadata and Etcd data to ensure high availability of the cluster.

[0272] The specific contents of each stage of cluster lifecycle management are as follows:

[0273] 1) Cluster initialization phase

[0274] Verify environment dependencies;

[0275] Prepare system configuration;

[0276] Initialize the network environment;

[0277] Configure storage systems;

[0278] Deploy basic components;

[0279] Verify the initialization result.

[0280] 2) Cluster deployment phase

[0281] Deploy the master node:

[0282] Initialize the control plane;

[0283] Deploy core components;

[0284] Configure high availability mechanisms;

[0285] Verify the master node status.

[0286] Deploy the working nodes:

[0287] Configure the node environment;

[0288] Join the cluster network;

[0289] Deploy necessary components;

[0290] Verify node readiness status.

[0291] 3) Cluster expansion stage

[0292] Verify expansion requirements;

[0293] Prepare the new node environment;

[0294] Execute node initialization;

[0295] Join an existing cluster;

[0296] Update cluster configuration;

[0297] Verify the expansion result.

[0298] 4) Select nodes to be removed during cluster shrinking;

[0299] evacuate workloads;

[0300] Clean up node resources;

[0301] Remove from the cluster;

[0302] Update cluster status;

[0303] Verify the scaling-down result.

[0304] 5) Verify the upgrade path during the cluster upgrade phase;

[0305] Back up critical data;

[0306] Upgrade the control plane;

[0307] Upgrade working nodes;

[0308] Update cluster components;

[0309] Verify the upgrade results.

[0310] 6) Determine the backup scope during the cluster backup phase;

[0311] Create a data snapshot;

[0312] Compress backup data;

[0313] Storing backup files;

[0314] Record backup metadata; verify backup integrity.

[0315] 7) Verify backup files during cluster recovery phase;

[0316] Prepare to restore the environment;

[0317] Restore cluster data;

[0318] Rebuild cluster services;

[0319] Verify the recovery results;

[0320] Update the cluster status.

[0321] Related technical points:

[0322] 1) State management mechanism

[0323] A clearly defined state model;

[0324] Ensure the atomicity of state transitions;

[0325] Support state rollback;

[0326] Provides status query interface.

[0327] 2) High availability design

[0328] Multiple replicas of the master node are deployed;

[0329] Data storage redundancy backup;

[0330] Automatic service failover;

[0331] Node automatic recovery mechanism.

[0332] 3) Security Control

[0333] Fine-grained permission management;

[0334] Encrypted storage of sensitive data;

[0335] Operational audit logs;

[0336] Secure communication guarantee.

[0337] (3) Monitoring and alarm module:

[0338] The system's automated deployment engine has a built-in monitoring and alarm system based on VictoriaMetrics and Grafana, providing out-of-the-box cluster monitoring capabilities.

[0339] On each node in the cluster, the engine will automatically deploy the Node Exporter component to collect various system indicators of the node, such as CPU, memory, disk and other resource usage. At the same time, VictoriaMetricsSidecar is deployed on the master node to regularly capture indicator data from the Node Exporter and push it to the central VictoriaMetrics storage. The system needs to monitor the running status of the cluster in real time, collect performance indicators and generate alarm information. The steps are as follows:

[0340] 1) Metrics collection: Collect the CPU, memory, and disk usage of the node through Node Exporter;

[0341] 2) Data processing: store and aggregate indicator data, calculate the average and maximum values, the formula is as follows:

[0342] Average formula:

[0343]

[0344] Used to smooth the fluctuations of indicators and provide a reference for the overall trend.

[0345] Alarm triggering formula:

[0346]

[0347] Where: Mavg is the average value of a certain indicator; Mi is the indicator value collected for the i-th time; T is the alarm threshold; A indicates whether an alarm is triggered.

[0348] 3) Alarm triggering: When a certain indicator exceeds the preset threshold, an alarm is triggered and relevant personnel are notified of the deployment process:

[0349] 1) Node monitoring deployment phase

[0350] Identify all nodes in the cluster

[0351] Verification node environment requirements

[0352] Deploy Node Exporter:

[0353] Configure acquisition parameters;

[0354] Set the collection interval;

[0355] Define collection indicators;

[0356] Optimize resource usage.

[0357] Verify the collection status

[0358] Configuring data push

[0359] 2) Data collection, transfer and deployment stage

[0360] Select the master node to deploy

[0361] Deploy VictoriaMetrics Sidecar

[0362] Configuring Data Cache

[0363] Set push strategy

[0364] Optimize transmission performance

[0365] Configuring the retry mechanism

[0366] Create a collection task

[0367] Verify data flow

[0368] 3) Deploy VictoriaMetrics service during the monitoring platform deployment phase:

[0369] Configure storage capacity;

[0370] Set data retention policies;

[0371] Optimize query performance;

[0372] Configure high availability mechanism.

[0373] Deploy the Grafana service:

[0374] Configure data source connection;

[0375] Import monitoring dashboard;

[0376] Set access permissions;

[0377] Configure theme styles.

[0378] 4) Deploy the alarm manager during the alarm system deployment phase

[0379] Configuring Alarm Rules

[0380] Define trigger conditions;

[0381] Set the alarm level;

[0382] Configure alarm description;

[0383] Set the alarm period.

[0384] Configure the notification channel:

[0385] Support enterprise WeChat;

[0386] Support DingTalk integration;

[0387] Support Slack notifications;

[0388] Support email notification;

[0389] Verify the alarm functionality.

[0390] Related technical points:

[0391] 1) Distributed monitoring architecture

[0392] Multi-node data collection

[0393] Centralized data storage

[0394] Unified query interface

[0395] Scalable storage mechanism

[0396] 2) Data processing optimization

[0397] Data compression storage

[0398] Query performance optimization

[0399] Data Sharding Management

[0400] Historical data cleaning

[0401] 3) High availability design

[0402] Component multi-copy deployment

[0403] Data persistence protection

[0404] Automatic service recovery

[0405] Load balancing mechanism

[0406] This complete monitoring and alarm system enables all-round monitoring of the Kubernetes cluster, helping users quickly discover and locate cluster problems, and improving cluster observability and operation and maintenance efficiency. The system's modular design and perfect exception handling mechanism ensure the reliability and stability of the monitoring system itself.

[0407] By using the system's automated deployment engine, users can greatly simplify the deployment and operation of Kubernetes clusters. Based on declarative configuration management, automated management of the entire life cycle, and out-of-the-box monitoring and alarm features, the system provides users with a flexible, reliable, and easy-to-use cluster management solution.

[0408] 3. Application management system:

[0409] After the Kubernetes cluster is built, how to quickly deliver and manage the applications on it is another important issue that needs to be solved. Traditional application deployment usually requires hand-written complex YAML files, which lacks management of the entire application life cycle. Although package management tools such as Helm simplify the deployment process, they still have problems such as poor customization and difficulty in cross-environment deployment in actual use.

[0410] To this end, a comprehensive cloud-native application management system has been implemented. The core of the system is a web-based application store that provides an application catalog and deployment interface similar to the App Store. The application store contains various cloud-native applications such as commonly used middleware, databases, big data, and AI. Users can deploy applications to the Kubernetes cluster with one click, just like installing apps on a mobile phone.

[0411] At the same time, the application management system also introduces templated application configuration capabilities. In addition to providing default deployment parameters, each application can also declare the application's configurable parameters through a custom CUE template. When users deploy applications with one click, they can flexibly modify the application's configuration according to their needs, such as resource specifications, environment variables, external dependencies, etc. These configurations can be saved as application parameter templates for rapid reuse in different clusters and environments.

[0412] After the application is deployed, it also provides powerful application operation and monitoring capabilities. Based on cloud-native monitoring tools such as Prometheus and Grafana, real-time monitoring and alerting of applications are achieved. Users can view the application's CPU, memory, network and other resource usage in real time through the Web console, and set automatic alert rules to respond promptly when an application or cluster is abnormal.

[0413] In addition, to address the pain point of difficult debugging of Kubernetes applications, it also provides functions such as application log collection and event tracking. Through integration with cloud-native log and tracking tools such as Elasticsearch, Fluentd, and Jaeger, users can easily query application operation logs and track requests in distributed applications, greatly improving the efficiency of locating application problems.

[0414] In general, the application management system provides an end-to-end solution from multiple dimensions, including application delivery, configuration, monitoring, and troubleshooting. This enables users to manage applications in Kubernetes clusters in a standardized and automated way, significantly reducing the implementation cost of cloud-native applications.

[0415] In a specific implementation, the application management system is designed to simplify the application delivery and operation and maintenance process in the Kubernetes environment. The system mainly includes three modules: application store, application deployment and runtime management, providing users with full-process automation capabilities from application selection, deployment to operation and maintenance monitoring.

[0416] (1) App Store Module:

[0417] The application store is one of the core components of the application management system, providing one-click deployment capabilities for commonly used applications. Users can browse and search for various applications in the application store, such as Web services, databases, big data components, AI frameworks, etc. Each application is packaged in the form of a Helm Chart, which contains the application's deployment template, configuration files, dependencies and other meta-information. Users only need to select the required application in the application store, set the corresponding parameters, and deploy it to the Kubernetes cluster with one click, without having to pay attention to the underlying deployment details.

[0418] At the same time, the App Store also provides the function of application version management. For the same application, there may be multiple versions in the store, and users can select the appropriate version for deployment according to actual needs. The system will record the version change history of each application to facilitate user tracking and management. In addition, the App Store also supports users to upload customized application templates to meet specific business needs.

[0419] In the application deployment process, the application management system introduces the concept of configuration templates. By extracting the configurable items of the application as parameters and defining the default values ​​and data types of the parameters, the application configuration is standardized and structured. When deploying the application, the user only needs to fill in or select the corresponding parameter values ​​according to the prompts, and the system will automatically inject them into the application configuration file to generate the final deployment file. This method greatly simplifies the application configuration process and reduces the risk of configuration errors.

[0420] (2) Application deployment module:

[0421] Application deployment is another important function of the application management system, which is used to convert application templates in the application store into actual workloads running in the Kubernetes cluster.

[0422] Application deployment process:

[0423] 1) Application analysis stage

[0424] Receive Application resource definition

[0425] Verify resource format integrity

[0426] Analyze basic application information

[0427] Extract the application name and version;

[0428] Identify the component list;

[0429] Get configuration parameters.

[0430] Check application dependencies:

[0431] Verify component version compatibility;

[0432] Check resource dependency integrity;

[0433] Build a dependency graph.

[0434] 2) Configuration processing phase

[0435] Processing Configuration Parameters

[0436] Resolve variable references;

[0437] Get the Secret content;

[0438] Processing environment variables;

[0439] Verify parameter validity.

[0440] Generate configuration files:

[0441] Create a ConfigMap resource;

[0442] Handling sensitive configurations;

[0443] Generate Secret resources;

[0444] Set access permissions.

[0445] 3) Resource generation stage

[0446] Generate deployment resources:

[0447] Create a Deployment definition; configure the container image;

[0448] Set resource limits;

[0449] Configure health checks.

[0450] Generate service resources:

[0451] Create a Service definition;

[0452] Configure the service port;

[0453] Set access policies;

[0454] Configure load balancing.

[0455] Generate other resources:

[0456] Create a PersistentVolume;

[0457] Configure network policies;

[0458] Set resource quotas;

[0459] Create a ServiceAccount.

[0460] 4) Deployment and execution phase

[0461] Determine the deployment order

[0462] Analyze component dependencies;

[0463] Build deployment queues;

[0464] Set the deployment interval.

[0465] Submit resource deployment:

[0466] Call the Kubernetes API.

[0467] Monitor deployment progress;

[0468] Handle deployment exceptions;

[0469] Record deployment logs.

[0470] Update application status:

[0471] Record deployment results;

[0472] Update component status;

[0473] Save access information;

[0474] Record version information.

[0475] Related technical points:

[0476] 1) Declarative API design

[0477] Standardized resource definitions;

[0478] Simplified configuration method;

[0479] Versioned resource management;

[0480] Scalable model design.

[0481] 2) Dependency processing

[0482] Automatic dependency detection;

[0483] Version compatibility verification;

[0484] Deployment order optimization;

[0485] Circular dependency checking.

[0486] 3) State management mechanism

[0487] Real-time status updates;

[0488] Deployment progress tracking;

[0489] Abnormal state handling;

[0490] Persistent state storage.

[0491] Through this application deployment management mechanism based on custom resources, the system realizes the standardization and automation of application delivery. Users only need to provide simple application definitions, and the system can automatically complete complex deployment and orchestration work, greatly improving the efficiency and reliability of application deployment. At the same time, the perfect exception handling mechanism ensures the stability and controllability of the deployment process.

[0492] (3) Runtime management module:

[0493] After the application is deployed, the application management system also provides a series of runtime management functions to help users monitor the running status of the application in real time and perform operations such as configuration updates and log collection.

[0494] Application runtime management module processing flow:

[0495] 1) Status monitoring stage

[0496] Collect basic status information:

[0497] Get the application namespace;

[0498] Get application identification information;

[0499] Get the application version number;

[0500] Collect application tags.

[0501] Get the deployment status:

[0502] Query Deployment resources;

[0503] Get replica set information;

[0504] Statistics on Pod running status;

[0505] Check container health.

[0506] Get service status:

[0507] Query Service resources;

[0508] Get the service type;

[0509] Record port information;

[0510] Get the access address.

[0511] Assembly Status Report:

[0512] Aggregate component status;

[0513] Calculate health indicators;

[0514] Generate status summary;

[0515] Update status record.

[0516] 2) Configuration management phase

[0517] To receive configuration updates:

[0518] Verify the configuration format;

[0519] Compare configuration differences;

[0520] Check configuration dependencies;

[0521] Verify the validity of the configuration.

[0522] Perform a configuration update:

[0523] Update ConfigMap;

[0524] Modify the Secret content;

[0525] Trigger configuration reload;

[0526] Verify the update results.

[0527] The monitoring configuration takes effect:

[0528] Track configuration delivery;

[0529] Check application response;

[0530] Verify that the configuration is effective;

[0531] Roll back failed updates.

[0532] 3) Determine the log scope during the log collection phase:

[0533] Set the time range;

[0534] Select the log level;

[0535] Determine the source of the logs;

[0536] Set the filter conditions.

[0537] Execute log collection:

[0538] Get the Pod list;

[0539] Collect container logs;

[0540] Get standard output;

[0541] Get the error log.

[0542] Log data processing:

[0543] Merge logs from multiple sources;

[0544] Parsing log format;

[0545] Extract key information;

[0546] Standardized processing.

[0547] Log storage management:

[0548] Compress log data;

[0549] Set retention policies;

[0550] Manage storage space;

[0551] Clean up expired logs.

[0552] Related technical points:

[0553] 1) Real-time monitoring mechanism

[0554] Periodic status collection

[0555] Asynchronous data processing

[0556] Incremental state updates

[0557] Threshold alarm trigger

[0558] 2) Configure hot update

[0559] Lossless configuration update

[0560] Atomic operation guarantee

[0561] Configuration version management

[0562] Rollback mechanism support

[0563] 3) Log management optimization

[0564] Distributed log collection

[0565] Efficient storage mechanism

[0566] Real-time log transmission

[0567] Intelligent log analysis

[0568] Through this complete runtime management mechanism, the system realizes all-round monitoring and management of applications. Users can grasp the application status in real time, flexibly adjust application configuration, and quickly troubleshoot operation problems, which greatly improves the efficiency and reliability of application operation and maintenance. At the same time, the perfect exception handling mechanism ensures the stability and security of management operations.

[0569] In summary, the application management system covers all aspects of application delivery, deployment, operation and maintenance, and realizes the automation of the entire application management process through cloud native technologies such as application stores, custom resources, and declarative APIs. Users can quickly complete application selection, deployment, configuration, monitoring and other operations through a simple graphical interface and configuration files without having to deeply understand the complex details of Kubernetes, which greatly reduces the threshold for cloud native application management and improves the efficiency and quality of application delivery.

[0570] 4. Multi-tenant management system

[0571] In the era of cloud computing, multi-tenancy is an important means to maximize the use of computing resources. However, as a general container orchestration platform, Kubernetes still has shortcomings in multi-tenant isolation and management. In order to turn Kubernetes into a truly multi-tenant cloud operating system, a full-featured multi-tenant management system is designed.

[0572] The basis of multi-tenant management is the Namespace mechanism of Kubernetes. Namespace is essentially a logical division of cluster resources. By allocating resources of different tenants in different Namespaces, resource isolation between tenants can be achieved to a certain extent. However, this isolation is not thorough enough and there is a risk of "cross-border" access to resources.

[0573] Therefore, a more fine-grained resource isolation mechanism is introduced based on Namespace. At the computing level, when using container runtimes such as Kata Containers and gVisor, a lightweight virtual machine environment is provided for each tenant's container, and the container's CPU, memory and other resources are strictly limited within the virtual machine, avoiding cross-tenant resource theft.

[0574] At the storage level, it supports allocating storage space of different tenants on different storage devices, isolating storage IO performance and avoiding mutual impact of different tenant applications. At the same time, encryption technology is used to encrypt storage volume data, ensuring data security even if the storage device is illegally taken over.

[0575] In terms of network, a multi-level network isolation solution is adopted. First, each tenant's application runs in an independent overlay network, and network access between tenant applications is restricted through Network Policy. Second, VLAN or VPC is supported to isolate tenant networks at a higher level, avoiding cross-segment intrusion.

[0576] In addition to resource isolation, a fine-grained tenant permission management system is also built in. Based on the Kubernetes RBAC mechanism, the system can finely control each tenant's access rights to cluster resources, such as limiting tenants to access only specified Namespaces, or only having read and write permissions to certain resources (such as Pods and Services). You can also customize roles for tenants and manage tenant permissions in batches.

[0577] In addition, it also provides a rich multi-tenant billing module. Administrators can set different packages for different tenants, define resource quotas such as CPU, memory, storage, bandwidth, etc., or charge on-demand for the resources actually used by tenants. This not only achieves the effective allocation of cluster resources, but also brings a new profit model for enterprises.

[0578] In a specific implementation, the multi-tenant management system is designed to provide secure, isolated, and flexible tenant management capabilities for Kubernetes clusters. In a multi-tenant scenario, multiple users or organizations share the same Kubernetes cluster, and each tenant requires independent resource space, access rights, and security guarantees. The system mainly includes three modules: resource isolation, permission management, and security isolation, which together build a complete multi-tenant management solution.

[0579] (1) Resource isolation module:

[0580] In Kubernetes, resource isolation is the basis for achieving multi-tenancy. The system's multi-tenant management system is based on the Kubernetes namespace mechanism to achieve resource isolation between different tenants. Each tenant is assigned an independent namespace, and all of the tenant's resources, such as Pod, Service, Deployment, etc., are created in this namespace, thereby achieving logical isolation between different tenants.

[0581] However, namespace isolation alone is not enough, because Kubernetes allows Pods to consume unlimited resources such as CPU and memory of nodes by default, which may cause the resource usage of a tenant to affect other tenants. To this end, the system introduces two core resource objects, ResourceQuota and LimitRange, to set quota limits and default values ​​for resource usage in namespaces.

[0582] Through ResourceQuota, cluster administrators can set CPU, memory, storage and other resource usage quotas for each tenant's namespace. Once a tenant's resource usage exceeds the quota limit, Kubernetes will automatically reject new resource creation requests to ensure that the tenant does not occupy too many cluster resources and affect the normal use of other tenants. LimitRange is used to set default resource requests and limit values ​​for Pods in a namespace to avoid resource abuse caused by Pods not setting resource limits.

[0583] Here are the steps:

[0584] 1) Create ResourceQuota and LimitRange to limit resource usage within the namespace. The formula is as follows:

[0585]

[0586] in:

[0587] R quotaIndicates whether the resource quota meets the limit. When it is equal to 1, all resource usage is within the quota range. When it is equal to 0, at least one resource exceeds the quota limit.

[0588] j is a resource, such as CPU or memory.

[0589] U j is the actual usage of resource j.

[0590] Q j is the quota limit of resource j.

[0591] 2) Configure NetworkPolicy to isolate tenant network traffic.

[0592] 3) Enable storage encryption and independent storage volumes to ensure data security.

[0593] In addition to computing resource isolation, network isolation is also an important requirement in multi-tenant scenarios. To this end, the system supports configuring independent network policies for each tenant's namespace. Through network policies, administrators can finely control network traffic between Pods within a tenant's namespace, as well as communication with Pods outside the namespace. This not only prevents network interference between tenants, but also reduces the risk of network attacks.

[0594] (2) Rights Management Module:

[0595] In addition to resource isolation, multi-tenant scenarios also require fine-grained control of tenants' access rights. In Kubernetes, access control is based on the RBAC (role-based access control) model. The system's multi-tenant management system makes full use of the Kubernetes RBAC mechanism to implement permission management within and between tenants.

[0596] Implementation process of tenant rights management:

[0597] 1) Tenant rights management stage

[0598] Create a tenant management role:

[0599] Define role identification;

[0600] Set the role scope;

[0601] Configure resource permissions;

[0602] Specify operation permissions;

[0603] Set namespace restrictions;

[0604] Marks the role attributes.

[0605] Configure permission rules:

[0606] Set up API group access; define resource scope;

[0607] Configure operation permissions;

[0608] Add resource limits;

[0609] Set access policies;

[0610] Manage special permissions.

[0611] Assign tenant permissions:

[0612] Identify the authorized subject;

[0613] Create a role binding;

[0614] Set the binding scope;

[0615] Update permission relations;

[0616] Verify that permissions are in effect.

[0617] 2) Create a cluster role during the cross-tenant access control phase:

[0618] Define global permissions;

[0619] Set resource scope;

[0620] Configure access rules;

[0621] Manage cluster resources;

[0622] Control tenant boundaries.

[0623] To manage role bindings:

[0624] Bind user or group;

[0625] Set the scope of action;

[0626] Control access levels;

[0627] Management life cycle;

[0628] Handles permission inheritance.

[0629] Permission verification control:

[0630] Verify access requests;

[0631] Check permission levels;

[0632] Control cross-tenant access;

[0633] Record access logs;

[0634] Handle permissions conflicts.

[0635] Related technical points:

[0636] 1) Multi-level permission system

[0637] Tenant-level permission management;

[0638] Cluster-level permission control;

[0639] Fine-grained access control;

[0640] Flexible permission extension.

[0641] 2) Permission isolation mechanism

[0642] Tenant resource isolation;

[0643] Namespace management;

[0644] Cross-tenant access control;

[0645] Permission boundary definition.

[0646] 3) Permission inheritance system

[0647] Role inheritance relationship;

[0648] Permission transfer rules;

[0649] Multi-level authorization support;

[0650] Dynamic permission adjustment.

[0651] Through this complete permission management mechanism, the system realizes autonomous management within tenants and secure isolation between tenants. Tenant administrators can flexibly manage internal permissions, while cluster administrators can control higher-level access permissions through cluster roles, ensuring the security and manageability of the entire system. At the same time, the perfect exception handling and security management mechanism ensures the reliable operation of the permission system.

[0652] (3) Safety isolation module:

[0653] Security isolation is another important aspect of multi-tenant management. Kubernetes' namespace isolation and RBAC permission control alone are not enough to ensure complete isolation and security between tenants. To this end, the system's multi-tenant management system also integrates a variety of security isolation technologies to build a comprehensive security isolation protection system from multiple levels such as storage, network, and runtime.

[0654] Multi-tenant security isolation implementation process:

[0655] 1) Storage isolation implementation stage

[0656] Storage resource initialization:

[0657] Configure the storage engine;

[0658] Create a storage pool;

[0659] Set access permissions;

[0660] Define isolation boundaries;

[0661] Configure performance parameters;

[0662] Enable data protection.

[0663] Storage Type Management:

[0664] Create StorageClass;

[0665] Configure storage parameters;

[0666] Set access mode;

[0667] Define quota limits;

[0668] Manage storage policies;

[0669] Control resource allocation.

[0670] Dynamic storage allocation:

[0671] Processing storage requests;

[0672] Create a storage volume;

[0673] Bind storage resources;

[0674] Verify access rights;

[0675] Monitor usage;

[0676] Manage storage lifecycle.

[0677] 2) Network isolation implementation stage

[0678] Network policy configuration:

[0679] Deploy Cilium components;

[0680] Configure network policies;

[0681] Set access rules;

[0682] Define flow control;

[0683] Enable security protection;

[0684] Configure monitoring alarms.

[0685] Flow Control Management:

[0686] L3 / L4 layer flow control;

[0687] L7 application control;

[0688] Set bandwidth limits;

[0689] Configure load balancing;

[0690] Implement access controls;

[0691] Record traffic logs.

[0692] Security policy implementation:

[0693] Configure isolation rules;

[0694] Set up firewall policies;

[0695] Enable intrusion detection;

[0696] Manage security groups;

[0697] Control cross-tenant access;

[0698] Monitor security events.

[0699] 3) Runtime isolation implementation stage Runtime environment configuration:

[0700] Create a container runtime class;

[0701] Configure the node selector;

[0702] Set resource limits;

[0703] Define security policies;

[0704] Configure isolation parameters;

[0705] Enable monitoring.

[0706] Lightweight virtualization management:

[0707] Create a virtualized environment;

[0708] Configure resource allocation;

[0709] Manage device mapping;

[0710] Control startup parameters;

[0711] Monitor operating status;

[0712] Handling unusual situations;

[0713] Scheduling strategy implementation:

[0714] Node affinity configuration;

[0715] Resource reservation management;

[0716] Load balancing control;

[0717] Failure migration processing;

[0718] Performance optimization and adjustment;

[0719] Maintain the operating environment.

[0720] Related technical points:

[0721] 1) Multi-level isolation architecture physical resource isolation;

[0722] Logical boundary demarcation;

[0723] Dynamic resource allocation;

[0724] Security policy linkage.

[0725] 2) High-performance network control

[0726] eBPF technology application;

[0727] XDP acceleration support;

[0728] Intelligent traffic management;

[0729] Real-time security protection.

[0730] 3) Lightweight virtualization Lightweight virtualization technology;

[0731] Quick start capability;

[0732] Optimization of resource utilization;

[0733] Safety isolation guarantee;

[0734] Through this multi-dimensional tenant isolation mechanism, the system achieves secure isolation and efficient management of tenant resources. The isolation technologies at the storage, network, and runtime levels work together to build a secure and reliable multi-tenant environment. At the same time, the perfect exception handling and performance optimization mechanism ensures the stable operation of the system and efficient use of resources.

[0735] In short, the multi-tenant management system provides a complete set of multi-tenant isolation and management solutions from multiple dimensions such as resources, permissions, and security. Through namespace isolation and resource quotas, resource isolation between tenants is achieved; through RBAC permission control, intra-tenant autonomy and inter-tenant access control are achieved; through security technologies such as OpenEBS, Cilium, and Firecracker, storage, network, and runtime security isolation is achieved. The organic combination of these mechanisms builds a secure, flexible, and efficient Kubernetes multi-tenant operating environment that meets the multi-tenant management needs of enterprise-level users.

[0736] The present invention has the following beneficial effects:

[0737] 1) Reduce deployment complexity: Through modular design and automated deployment, the deployment difficulty of the Kubernetes cluster is significantly reduced.

[0738] 2) Improve operation and maintenance efficiency: Automated lifecycle management reduces manual intervention and improves operation and maintenance efficiency.

[0739] 3) Enhanced scalability: The modular architecture supports on-demand customization to meet the needs of different scenarios.

[0740] 4) Improve user experience: Provide a unified operation interface and lower the usage threshold.

[0741] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the technical principles of the present invention, and these improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A modular Kubernetes cluster automated deployment system, characterized in that: include: The modular cluster image system is used to provide images that contain all components of cluster operation and the best practice configuration of each component. It can be used to flexibly customize the component combination of the cluster according to different needs, quickly generate customized cluster images, and realize the standardization of Kubernetes cluster delivery; The automated deployment engine is used to define the cluster topology and configuration parameters using a declarative API interface, complete automated management of the entire life cycle through the controller architecture model, and provide out-of-the-box monitoring and alarm functions; Application management system, which is used to provide a Web-based application store, templated application configuration management, and real-time application monitoring management; Multi-tenant management system, used to provide resource isolation management, tenant rights management, and security isolation management.

2. The modular Kubernetes cluster automated deployment system according to claim 1, characterized in that: The modular cluster mirroring system comprises: The image packaging module is used to package the various components required by the Kubernetes cluster, including container runtime, network plug-in, and storage plug-in, into a standardized OCI image; the image contains the binary files of each component, as well as the configuration files and startup scripts of the components, forming a complete cluster operating environment; The component management module is used to manage component versions and handle dependencies through a component management mechanism based on version control and declarative APIs; The image warehouse module is used to store the newly created cluster images pushed by the image packaging module through the image warehouse, and in the subsequent cluster deployment process, each node can directly pull the image from the image warehouse without accessing the external network.

3. The modular Kubernetes cluster automated deployment system according to claim 2, characterized in that: The image packaging module is specifically used for: 1) Configuration parsing: Parse the user-defined cluster configuration and extract the component list and dependencies; 2) Resource preparation: download the binary files and dependencies of the components and generate the corresponding configuration files; 3) Image building: Use the container engine API to build the OCI image, optimize the image size and set the version tag; 4) Image distribution: push images to the specified image repository, record version information, and support offline export.

4. The modular Kubernetes cluster automated deployment system according to claim 2, characterized in that: The image repository module is also used to: Image version control: Each image pushed to the image repository is labeled with a unique version number so that users can distinguish and manage different versions of images; Image incremental update: When the cluster image is updated, the image pushed to the image repository is the changed part after automatically calculating the difference between the new and old versions of the image, so as to avoid the full transmission of image data and speed up the image update; Image security scanning: The built-in security scanning module of the image repository automatically scans newly pushed images for vulnerabilities, and promptly issues warnings and intercepts risky images.

5. The modular Kubernetes cluster automated deployment system according to claim 1, characterized in that: The automated deployment engine includes: Deploy the configuration management module to ensure the legitimacy of user-defined configurations through the configuration verification algorithm. The steps are as follows: 1) Parameter extraction: Extract key parameters of the cluster from the Cluster definition file, including version, node, and network plug-in; 2) Dependency verification: check compatibility between component versions; 3) Constraint verification: Verify whether the configuration complies with the agreed resource quota limit. Verification formula: Where: E is the error score of the configuration; Ci is the error coefficient of the i-th configuration item; Wi is the weight of the corresponding configuration item; 3) Output diagnosis: Generate error reports for configurations that do not meet constraints; Cluster lifecycle management module, used for cluster initialization, deployment, expansion, contraction, upgrade, and backup and recovery; The monitoring and alarm module is used to monitor the running status of the cluster in real time, collect performance indicators and generate alarm information. The steps are as follows: 1) Metrics collection: Collect the CPU, memory, and disk usage of the node through Node Exporter; 2) Data processing: store and aggregate indicator data, calculate the average and maximum values, the formula is as follows: Average formula: Used to smooth the fluctuations of indicators and provide a reference for the overall trend; Alarm triggering formula: Among them: Mavg is the average value of a certain indicator; Mi is the indicator value collected for the i-th time; T is the alarm threshold; A indicates whether an alarm is triggered; 3) Alarm triggering: When an indicator exceeds the preset threshold, an alarm is triggered and relevant personnel are notified.

6. The modular Kubernetes cluster automated deployment system according to claim 5, characterized in that: The cluster lifecycle management module is specifically used for: Initialization: Automatically configure the basic environment of cluster nodes, including installing Docker and Kubeadm; Deployment: According to the Cluster configuration file, the Kubernetes control plane and workload components are automatically deployed on the nodes to achieve one-click deployment; Scaling: Horizontally scale the cluster, and automatically perform configuration and cleanup operations when adding or deleting nodes; Upgrade: Perform rolling upgrades on the cluster and ensure uninterrupted business by controlling the upgrade process; Backup and recovery: Provides a regular backup and recovery mechanism for cluster metadata and Etcd data to ensure high availability of the cluster.

7. The modular Kubernetes cluster automated deployment system according to claim 1, characterized in that: The application management system comprises: The application store module is used to provide an application catalog and deployment interface for users to deploy applications to the Kubernetes cluster with one click; the applications include middleware, database, big data, and AI; The application deployment module is used to provide templated application configuration functions, allowing users to declare configurable application parameters through customized templates, flexibly modify application configurations according to actual needs, and support unified deployment across environments; The runtime management module is used to monitor the running status of the application in real time and provide configuration update, log collection, event tracking, and alarm functions.

8. The modular Kubernetes cluster automated deployment system according to claim 1, characterized in that: The multi-tenant management system includes: The resource isolation module is used to implement tenant isolation based on Kubernetes namespaces, and to set quota limits and default values ​​for namespace resource usage through two core resource objects: ResourceQuota and LimitRange. The permission management module is used to implement permission management within and between tenants based on the Kubernetes RBAC mechanism; The security isolation module is used to provide multi-level isolation of runtime, storage, and network.

9. The modular Kubernetes cluster automated deployment system according to claim 8, characterized in that: The resource isolation module is specifically used for: ResourceQuota allows cluster administrators to set CPU, memory, and storage usage quotas for each tenant's namespace. Once a tenant's resource usage exceeds the quota limit, Kubernetes automatically rejects new resource creation requests to ensure that the tenant does not occupy too many cluster resources and affect the normal use of other tenants. LimitRange is used to set default resource requests and limits for Pods in a namespace to avoid resource abuse caused by Pods not setting resource limits.

10. The modular Kubernetes cluster automated deployment system according to claim 8, characterized in that: The safety isolation module is specifically used for: At the computing level, using the container runtime, a lightweight virtual machine environment is provided for each tenant's container, and the CPU and memory resources of the container are strictly limited within the virtual machine to avoid cross-tenant resource theft; At the storage level, the storage space of different tenants is allocated to different storage devices to isolate storage IO performance to avoid mutual impact of different tenant applications, and encryption technology is used to encrypt storage volume data to ensure data security; In terms of network, a multi-level network isolation solution is adopted: each tenant's application runs in an independent overlay network, network access between tenant applications is restricted through Network Policy, and VLAN or VPC is used to isolate tenant networks at a higher level to avoid cross-segment intrusions.

Images