Method and system for quickly building server cluster

Abstract

The invention discloses a method and a system for quickly building a server cluster. The method comprises the following steps of: coding an infrastructure, and realizing automatic creation and configuration of resources by using a declarative configuration language and an automatic deployment tool; deploying containerized services, and dynamically adjusting service instances through a container arrangement platform and an automatic expansion and contraction device; carrying out service templating and arranging, and rapidly instantiating an application component based on a structured template; dynamically configuring an authority strategy; task scheduling and resource reservation are carried out, and fine-grained scheduling is realized by combining a reinforcement learning scheduler with a resource reservation and preemption mechanism; intelligent monitoring and self-healing are carried out, multi-dimensional data are collected, and abnormal early warning and fault self-healing are carried out through a prediction model and root cause analysis; and integrating a data service gateway. According to the method, cluster minute-level delivery, fine-grained resource scheduling, dynamic authority control, intelligent operation and maintenance and cross-environment unified management are realized, and the deployment efficiency, the resource utilization rate and the system availability are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of cloud computing and distributed computing technology, specifically to a method and system for rapidly building a server cluster. Background Technology

[0002] With the acceleration of global digitalization and intelligentization, enterprise and organizational information systems are facing multiple challenges, including surging business volume, increased demands for real-time service response, rising architectural complexity, and growing cost control pressures. Against this backdrop, agile, resilient, intelligent, and secure IT infrastructure has become crucial for supporting business innovation.

[0003] Traditional methods of building and managing server clusters, such as static allocation based on physical machines, manual management of virtualized resource pools, or static configuration relying on early automation tools (such as Puppet and Chef), generally suffer from the following problems:

[0004] Deployment efficiency is low: from resource application and environment configuration to service launch, the cycle can take several days or even weeks, making it difficult to cope with rapid business changes.

[0005] Coarse resource scheduling: Resource allocation is mostly based on virtual machines or physical machines, which is coarse-grained and cannot achieve precise scheduling at the container or task level, resulting in low resource utilization.

[0006] Operation and maintenance are passive responses: the monitoring system mainly relies on threshold alarms and lacks predictive capabilities; fault handling depends on manual investigation and intervention, the average repair time is long, and the system availability is not guaranteed.

[0007] Rigid security policies: Access control is mostly based on static roles, lacking the ability to perceive and respond to dynamic factors such as user behavior and access context, and is poorly adaptable to internal threats or complex attacks.

[0008] Poor environmental compatibility: The solutions are usually designed for data center environments and are difficult to extend seamlessly to public clouds, private clouds or edge nodes, making it impossible to achieve true unified management across environments.

[0009] In existing technologies, such as patent document CN111752539B, a BI service cluster system and its construction method are disclosed. This system allows tenants to manage user permissions themselves, reducing the user management burden on BI tools. However, this technical solution still has limitations: its permission management is based on static roles, lacking context awareness and dynamic adjustment capabilities; resource scheduling is tenant-based, resulting in coarse granularity; it lacks infrastructure-as-code and modern container orchestration support, leading to low deployment efficiency; and it does not integrate intelligent operation and maintenance and self-healing mechanisms, resulting in high operation and maintenance costs. Essentially, this solution is a centralized management system geared towards specific application scenarios, making it difficult to adapt to the common needs for rapid elasticity, intelligent operation and maintenance, and fine-grained security control in diverse scenarios such as big data analytics, IoT, and microservice architectures.

[0010] Therefore, there is an urgent need in this field for an integrated cluster construction method and system that can achieve minute-level delivery, fine-grained resource scheduling, dynamic adaptive security, and intelligent predictive operation and maintenance, in order to systematically solve the above problems. Summary of the Invention

[0011] The primary objective of this invention is to provide a method and system for rapidly building server clusters, enabling minute-level cluster delivery, fine-grained resource scheduling, dynamic access control, intelligent operation and maintenance, and unified management across environments. This fundamentally solves the problems of low resource utilization, poor elasticity, rigid permissions, complex deployment, and passive operation and maintenance in existing technologies.

[0012] To achieve the above objectives, the present invention provides the following technical solution: a method for rapidly building a server cluster, comprising the following steps:

[0013] Step 1: Infrastructure as code. Use a declarative configuration language to write infrastructure definition scripts, and execute them through version control systems and automated deployment tools to achieve automated creation and configuration of resources.

[0014] Step 2: Containerized service deployment. The application service is encapsulated into a container image, deployed and managed through a container orchestration platform, and an autoscaler is configured to dynamically adjust the service instances.

[0015] Step 3: Service templating and orchestration. Based on structured templates, application components, resource configurations, dependencies, and health checks are defined, and the application is quickly instantiated through an intelligent service orchestration engine.

[0016] Step 4: Dynamic configuration of permission policies, integrating a context-aware policy engine to dynamically adjust access permissions based on multi-dimensional signals such as user identity, time, source, and risk score;

[0017] Step 5: Task scheduling and resource reservation. A task scheduler based on reinforcement learning is used, combined with resource reservation and preemption mechanisms, to achieve fine-grained task scheduling.

[0018] Step 6: Intelligent monitoring and self-healing. Collect multi-dimensional operation and maintenance data, use predictive models for anomaly warning, locate faults through root cause analysis models, and trigger self-healing scripts to execute repair actions.

[0019] Step 7: Data service gateway integration, providing a unified API entry point, implementing security control, request routing, and data format conversion.

[0020] Preferably, the declarative configuration language includes HCL or YAML, and the automated deployment tool includes Terraform, Ansible, or Pulumi.

[0021] Preferably, the container orchestration platform is Kubernetes, and the autoscaler is a horizontal Pod autoscaler that scales up or down based on CPU utilization, memory usage, or a custom QPS metric.

[0022] Preferably, the template is in YAML or JSON format, and the intelligent service orchestration engine supports dependency resolution, service discovery, and health check initialization.

[0023] Preferably, the strategy engine incorporates blockchain technology to ensure the immutability of audit logs.

[0024] Preferably, the reinforcement learning scheduler adopts the DQN or PPO algorithm, and the reward function comprehensively considers the task completion rate, average waiting time and resource fragmentation rate.

[0025] Preferably, the prediction model is LSTM or ARIMA, the root cause analysis model is a Bayesian network, and the self-healing script includes restarting instances, expanding resources, migrating Pods, or isolating faults.

[0026] Preferably, it supports RESTful or GraphQL APIs, integrates API Key, OAuth 2.0 or JWT authentication methods, and implements rate limiting and circuit breaking strategies.

[0027] Preferably, the method also supports hybrid cloud, multi-cloud and edge computing scenarios, and achieves seamless scheduling and consistent management of heterogeneous resources through a unified abstraction layer.

[0028] A system for implementing the rapid server cluster setup method described in any of the above claims, comprising:

[0029] Infrastructure is a code module used to execute the writing, version management, and automated deployment of infrastructure definition scripts;

[0030] The containerized service deployment module is used for building, orchestrating, and automatically scaling container images.

[0031] The service templating and orchestration module is used for parsing application templates, managing dependencies, and instantiating services.

[0032] The permission policy dynamic configuration module is used for evaluating context-aware policies and dynamically adjusting permissions;

[0033] The task scheduling and resource reservation module is used for task scheduling and resource management based on reinforcement learning.

[0034] The intelligent monitoring and self-healing module is used for data acquisition, anomaly prediction, root cause analysis, and self-healing execution.

[0035] The data service gateway module is used for unified API exposure, security control, and request routing.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] 1. By using Infrastructure as Code and containerized orchestration, the entire process of cluster building is automated, reducing deployment time from several days to minutes. Based on real-time metrics, the automatic scaling mechanism significantly improves resource utilization and deployment efficiency.

[0038] 2. By using containers and tasks as the smallest scheduling units and combining reinforcement learning-based scheduling algorithms and preemption mechanisms, precise allocation and efficient utilization of resources are achieved, resulting in a qualitative improvement in the overall resource utilization of the cluster.

[0039] 3. A context-aware dynamic permission policy is introduced, which can respond to security threats in real time, reduce the complexity and security risks of permission management, and, combined with blockchain auditing, significantly improve the speed of security incident response and audit reliability.

[0040] 4. By integrating AI / ML technologies, a closed-loop operation and maintenance system has been achieved, from predictive alerts to automated root cause analysis and self-healing, which greatly improves system availability and reduces operation and maintenance manpower costs and average fault repair time.

[0041] 5. From underlying resource supply to upper-level service deployment, permission allocation, monitoring and self-healing, the entire process is automated, significantly reducing manual intervention points and improving operational efficiency.

[0042] 6. Through a unified abstraction layer and data service gateway, it natively supports hybrid cloud, multi-cloud and edge computing scenarios, and realizes seamless scheduling and consistent management of global heterogeneous resources. Attached Figure Description

[0043] Figure 1This is a flowchart illustrating the overall process of the server cluster rapid setup method of the present invention.

[0044] Figure 2 This is a schematic diagram illustrating the infrastructure, i.e., the code, of the present invention;

[0045] Figure 3 This is a schematic diagram illustrating the containerized service deployment of the present invention;

[0046] Figure 4 This is a schematic diagram illustrating the dynamic configuration of the permission policy of the present invention;

[0047] Figure 5 This is a schematic diagram illustrating the task scheduling and resource reservation of the present invention;

[0048] Figure 6 This is a schematic diagram of the intelligent monitoring and self-healing technology of the present invention;

[0049] Figure 7 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] The core of the methods and systems described in this invention lies in a complete technical architecture and execution process, the specific implementation of which can rely on different software ecosystems and hardware environments. The following embodiments are intended to clearly demonstrate the technical feasibility and application effects of this invention, rather than limiting it to specific software brands or configuration parameters. Those skilled in the art can select functionally equivalent tools and components for implementation based on actual needs.

[0052] like Figures 1-6 As shown in Example 1: A general cluster rapid setup and elastic management process based on the present invention.

[0053] This embodiment aims to systematically illustrate, without considering specific business scenarios, how the core steps of the method of the present invention work together to achieve a general process for rapid cluster setup and intelligent management.

[0054] Step 1: Infrastructure as Code

[0055] Administrators use declarative configuration languages ​​(such as HCL, YAML) to write infrastructure definition scripts. These scripts describe the complete topology of the target cluster, including but not limited to: the specifications and number of virtual machine clusters, the configuration of container orchestration platforms (such as Kubernetes), network planning (VPC, subnets, firewall rules), and the creation and mounting of storage resources (block storage, file storage, object storage).

[0056] The scripts are managed through a version control system (such as Git), enabling traceability, review, and rollback of infrastructure changes. By executing commands from automated deployment tools (such as Terraform, Ansible, and Pulumi), the system automatically creates and configures all resources in the specified cloud platform or data center.

[0057] This step transforms the traditional manual, discrete infrastructure preparation process into a fully automated, repeatable software delivery process, fundamentally eliminating human error and reducing infrastructure preparation time from hours or days to minutes.

[0058] Step 2: Deploy containerized services

[0059] Once the infrastructure is in place, all application services (such as web servers, databases, middleware, data analytics engines, etc.) are packaged into standardized container images (such as Docker images). These images are then deployed and managed through container orchestration platforms (such as Kubernetes).

[0060] The orchestration platform defines the desired state of services through declarative resource objects (such as Deployment and StatefulSet). The platform's built-in controller continuously compares the current state with the desired state and automatically corrects course to ensure continuous service operation.

[0061] Configure a horizontal Pod autoscaler, which can automatically increase or decrease the number of service instances based on preset rules (such as CPU utilization, memory usage exceeding thresholds, or custom metrics such as application QPS). This ensures that the service can handle traffic fluctuations smoothly, optimizing resource costs while maintaining service quality.

[0062] Step 3: Service Templating and Orchestration

[0063] To improve the deployment efficiency and consistency of complex applications, this invention introduces a service templating mechanism. Templates use structured formats such as YAML or JSON to abstractly define the components of a complete application, typically including:

[0064] Component image: The container image address of each service component;

[0065] Resource configuration: CPU and memory requirements and limits for each component;

[0066] Dependencies: Startup order and network dependencies between components;

[0067] Health check: Probe configuration used to determine whether a service instance is healthy;

[0068] Configuration and Storage: Declarations of application configuration files and persistent storage.

[0069] The intelligent service orchestration engine parses the template, automatically handling dependency resolution, service discovery configuration, and health check initialization. Users or automation scripts only need to provide a few parameters (such as instance name and environment variables) to quickly instantiate a complete, ready-to-run application stack within minutes.

[0070] Step 4: Dynamically Configure Permission Policies

[0071] The access control system of this invention goes beyond traditional role-based access control. It integrates a context-aware policy engine that can ingest and analyze various signals in real time, including but not limited to: user identity and role, access time, request source IP / geographical location, device security status, user historical behavior patterns, and real-time risk scoring.

[0072] The policy rules adopt a "When-Condition-Then" logical format. For example: "WHEN User requests data export THEN IF Time is outside working hours AND Risk score > 75 THEN DENY and issue an alert". The policy engine evaluates each access request in real time, dynamically granting, downgrading, or denying permissions when conditions are met, and recording audit logs in real time. This mechanism can effectively prevent risks such as credential theft and insider threats.

[0073] Step 5: Task Scheduling and Resource Reservation

[0074] For computational tasks such as batch processing and machine learning training, this invention employs a task scheduler based on reinforcement learning.

[0075] Specific implementation of the reinforcement learning task scheduler:

[0076] State space: includes CPU utilization, memory usage, network bandwidth, GPU usage, task queue length, etc. of each node.

[0077] Action space: includes task assignment to a node, task suspension, task preemption, resource reservation, etc.

[0078] Reward function: designed as follows:

[0079] R = α·task completion rate - β·average waiting time - γ·resource fragmentation rate

[0080] Where α, β, and γ are weight coefficients, which are adjusted through training. The learning algorithm employs Deep Q-Network (DQN) or Proximal Policy Optimization (PPO), combined with experience replay and the target network's stable training process.

[0081] The scheduler treats the cluster as a dynamic environment, continuously trying different task placement strategies and learning the optimal scheduling strategy based on a comprehensive reward function. This reward function takes into account the average waiting time, execution time, resource utilization balance, and task priority of each task.

[0082] Meanwhile, the system supports resource reservation and preemption mechanisms. High-priority tasks (such as real-time data analysis and emergency patch deployment) can pre-declare their required resources, and the scheduler will reserve them; or, when resources are insufficient, low-priority tasks can be gracefully terminated or migrated to ensure the resource supply and service level agreement of critical businesses.

[0083] Step 6: Intelligent Monitoring and Self-Healing

[0084] The intelligent operation and maintenance monitoring platform collects multi-dimensional indicators, logs, and link data from infrastructure, platform services, and application layers.

[0085] Prediction and Detection: Use time series prediction models (such as LSTM, ARIMA) to predict key indicators and issue predictive alerts before the indicators deviate abnormally.

[0086] The specific construction of the LSTM anomaly prediction model:

[0087] Input: Time series of historical monitoring data (such as CPU, memory, network I / O), with a time window of T.

[0088] Model structure:

[0089] Two layers of LSTM, each with 128 hidden units;

[0090] The output layer is a fully connected layer that predicts resource utilization at the next time step.

[0091] Training objective: Minimize the mean squared error (MSE) between the predicted and true values.

[0092] Anomaly detection: When the prediction error continuously exceeds the threshold ϵ, an early warning is triggered.

[0093] Root cause analysis: When a failure occurs, graph algorithms or probabilistic models (such as Bayesian networks) are used to quickly analyze massive amounts of operational data to pinpoint the most likely root cause.

[0094] Bayesian network root cause analysis model:

[0095] Node variables include service status, node load, network latency, configuration changes, and the health of dependent services.

[0096] Conditional probability table (CPT): trained based on historical fault data.

[0097] Inference mechanism: Using variable elimination or MCMC sampling, calculate the posterior probability of each root cause when the current anomaly is observed.

[0098] Output: A list of root causes sorted by probability, for the self-healing system to process first.

[0099] The system predefines self-healing scripts for common failure scenarios. Once the root cause is identified, corresponding remedial actions will be automatically triggered, such as restarting the abnormal instance, scaling up resources vertically, migrating the Pod to a healthy node, or performing fault isolation. This achieves a fully automated closed loop of operations and maintenance from "perception-decision-execution".

[0100] Step 7: Data Service Gateway Integration

[0101] To ensure a unified and secure exposure of data capabilities within the cluster, all data access requests pass through a unified data service gateway. This gateway provides the following core functionalities:

[0102] Unified API entry point: Provides a standard RESTful or GraphQL API to the outside world, shielding the complexity of the backend data source.

[0103] Security Management: Integrates multiple authentication methods (API Key, OAuth 2.0, JWT) and authorization logic, and implements fine-grained rate limiting and circuit breaker policies to prevent API abuse or overload.

[0104] Request routing and transformation: Intelligently routes front-end API requests to various back-end data processing services (such as Presto, Spark SQL), and can perform simple data format transformations.

[0105] like Figures 1-6 As shown in Example 2: Rapid Deployment and Elastic Scaling of Big Data Analytics Clusters

[0106] This embodiment uses an e-commerce promotional scenario as an example to demonstrate how the method of the present invention can achieve cluster setup and elastic scaling within minutes. By applying steps 1 to 7 of the method of the present invention, the system can quickly deploy clusters in high-concurrency scenarios and automatically respond to traffic fluctuations, improving resource utilization and operational efficiency.

[0107] Step 1: Define cluster resources using Terraform scripts and manage them via Git. Resource creation can be completed in minutes.

[0108] Step 2: Containerize the data collection and query engine, deploy it via Kubernetes, and configure HPA to automatically scale up or down based on CPU or QPS.

[0109] Step 3: Predefine a Presto cluster template and use the intelligent orchestration engine to quickly instantiate services.

[0110] Step 4: Integrate a context-aware policy engine to dynamically adjust permissions based on roles, time, and risk scores.

[0111] Step 5: Use the Q-learning algorithm to optimize task scheduling, supporting resource reservation and preemption.

[0112] Step 6: Use LSTM models to predict anomalies, perform Bayesian network root cause analysis, and automatically trigger self-healing (such as Pod migration).

[0113] Step 7: Expose the API through the unified data service gateway and implement authentication and rate limiting.

[0114] Cluster setup time has been reduced to minutes, resource utilization has been significantly improved, and operational efficiency has been increased.

[0115] like Figures 1-6 As shown in Example 3: Unified Management of Heterogeneous Resources through Cloud-Edge-Device Collaboration

[0116] This embodiment uses a smart factory as an example to demonstrate the universality of the system under a hybrid architecture.

[0117] Step 1: Use a unified script to define cloud and edge resources (such as K3s clusters) to achieve consistent management.

[0118] Steps 2-3: Containerize and deploy edge services (such as data filtering and AI detection); service templates support cross-environment orchestration.

[0119] Steps 4-5: The permission policy dynamically adapts to the edge environment; the task scheduling center monitors edge resources and supports load balancing and task offloading.

[0120] Step 6: The monitoring platform collects data from the cloud, edge, and device. The AI ​​model predicts node failures and triggers self-healing (such as resource reallocation).

[0121] Step 7: The data service gateway synchronizes policies and data through encrypted tunnels and provides a unified API entry point.

[0122] Edge decision latency is reduced to milliseconds, bandwidth usage is significantly reduced, and unified scheduling of cloud, edge, and device resources is achieved.

[0123] like Figure 7 As shown in Example 4: A system for the above-described method for rapidly building a server cluster, the system includes the following modules:

[0124] 1. Infrastructure as a Code Module

[0125] Configure the script editor to support HCL / YAML syntax highlighting and validation;

[0126] Integrated with Git version control system, supporting branch management and code review;

[0127] Deployment execution engine, supporting multiple tool-driven methods such as Terraform and Ansible;

[0128] Outputs resource topology diagrams and deployment logs, supporting visual auditing.

[0129] 2. Containerized Service Deployment Module

[0130] Image repository integration (such as Docker Hub, Harbor);

[0131] The Kubernetes cluster management interface supports resource objects such as Deployment, StatefulSet, and DaemonSet.

[0132] Automatic scaler controller, supporting elastic policies based on CPU, memory, and QPS;

[0133] Service mesh integration (such as Istio) supports traffic governance and canary deployments.

[0134] 3. Service Templating and Orchestration Module

[0135] Template library management, supporting public / private template categorization and version control;

[0136] The intelligent orchestration engine supports dependency topology sorting and service discovery configuration.

[0137] A parameterized configuration interface that supports environment variable injection and key management;

[0138] Health check probes are automatically generated and displayed on a status dashboard.

[0139] 4. Dynamic configuration module for permission policies

[0140] Context-aware engine collects user, device, network, and behavior data in real time;

[0141] The policy rule library supports editing and testing of "When-Condition-Then" logic;

[0142] Blockchain audit nodes record all permission changes and access logs;

[0143] Risk scoring model, integrating third-party threat intelligence sources.

[0144] 5. Task scheduling and resource reservation module

[0145] A reinforcement learning scheduler that supports online training and inference of the DQN / PPO algorithms;

[0146] Resource reservation pool management, supporting priority queues and preemption strategies;

[0147] The task monitoring panel displays the queuing, execution, and completion status, as well as resource usage.

[0148] Supports scheduling of heterogeneous resources such as GPUs and FPGAs.

[0149] 6. Intelligent monitoring and self-healing module

[0150] Data collection agent, supporting Prometheus, Fluentd, and Jaeger;

[0151] Predictive analytics engine with built-in LSTM, ARIMA and other models;

[0152] Root cause analysis workbench, supporting Bayesian network inference and visualization of causal graphs;

[0153] A self-healing script library that supports low-code editing and automated execution.

[0154] 7. Data Service Gateway Module

[0155] A unified API gateway that supports RESTful / GraphQL protocol conversion;

[0156] An authentication and authorization middleware that integrates OAuth 2.0, JWT, and mTLS;

[0157] The traffic control engine supports rate limiting, circuit breaking, and degradation strategies.

[0158] Data format converter, supporting JSON / XML / Protobuf conversion.

[0159] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for rapidly building a server cluster, characterized in that, Includes the following steps: Step 1: Infrastructure as code. Use a declarative configuration language to write infrastructure definition scripts, and execute them through version control systems and automated deployment tools to achieve automated creation and configuration of resources. Step 2: Containerized service deployment. The application service is encapsulated into a container image, deployed and managed through a container orchestration platform, and an autoscaler is configured to dynamically adjust the service instances. Step 3: Service templating and orchestration. Based on structured templates, application components, resource configurations, dependencies, and health checks are defined, and the application is quickly instantiated through an intelligent service orchestration engine. Step 4: Dynamic configuration of permission policies, integrating a context-aware policy engine to dynamically adjust access permissions based on multi-dimensional signals such as user identity, time, source, and risk score; Step 5: Task scheduling and resource reservation. A task scheduler based on reinforcement learning is used, combined with resource reservation and preemption mechanisms, to achieve fine-grained task scheduling. Step 6: Intelligent monitoring and self-healing. Collect multi-dimensional operation and maintenance data, use predictive models for anomaly warning, locate faults through root cause analysis models, and trigger self-healing scripts to execute repair actions. Step 7: Data service gateway integration, providing a unified API entry point, implementing security control, request routing, and data format conversion.

2. The method for rapidly building a server cluster according to claim 1, characterized in that, In the infrastructure i.e., the code-based step, the declarative configuration language includes HCL or YAML, and the automated deployment tool includes Terraform, Ansible, or Pulumi.

3. The method for rapidly building a server cluster according to claim 1, characterized in that, In the containerized service deployment steps, the container orchestration platform is Kubernetes, and the autoscaler is a horizontal Pod autoscaler that scales up or down based on CPU utilization, memory usage, or a custom QPS metric.

4. The method for rapidly building a server cluster according to claim 1, characterized in that, In the service templating and orchestration steps, the template is in YAML or JSON format, and the intelligent service orchestration engine supports dependency resolution, service discovery, and health check initialization.

5. The method for rapidly building a server cluster according to claim 1, characterized in that, In the dynamic configuration step of the permission policy, the policy engine combines blockchain technology to record audit logs in an immutable manner.

6. The method for rapidly building a server cluster according to claim 1, characterized in that, In the task scheduling and resource reservation steps, the reinforcement learning scheduler adopts the DQN or PPO algorithm, and the reward function comprehensively considers the task completion rate, average waiting time and resource fragmentation rate.

7. The method for rapidly building a server cluster according to claim 1, characterized in that, In the intelligent monitoring and self-healing steps, the prediction model is LSTM or ARIMA, the root cause analysis model is a Bayesian network, and the self-healing scripts include restarting instances, expanding resources, migrating Pods, or isolating faults.

8. The method for rapidly building a server cluster according to claim 1, characterized in that, The data service gateway integration step supports RESTful or GraphQL APIs, integrates API Key, OAuth 2.0 or JWT authentication methods, and implements rate limiting and circuit breaking strategies.

9. The method for rapidly building a server cluster according to claim 1, characterized in that, The method also supports hybrid cloud, multi-cloud and edge computing scenarios, and achieves seamless scheduling and consistent management of heterogeneous resources through a unified abstraction layer.

10. A system for implementing the rapid server cluster setup method according to any one of claims 1-9, characterized in that, include: Infrastructure is a code module used to execute the writing, version management, and automated deployment of infrastructure definition scripts; The containerized service deployment module is used for building, orchestrating, and automatically scaling container images. The service templating and orchestration module is used for parsing application templates, managing dependencies, and instantiating services. The permission policy dynamic configuration module is used for evaluating context-aware policies and dynamically adjusting permissions; The task scheduling and resource reservation module is used for task scheduling and resource management based on reinforcement learning. The intelligent monitoring and self-healing module is used for data acquisition, anomaly prediction, root cause analysis, and self-healing execution. The data service gateway module is used for unified API exposure, security control, and request routing.

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