Enterprise-level cloud native PaaS management platform

Abstract

The invention provides an enterprise-level cloud native PaaS management platform, and effectively solves the existing technical problems. The platform integrates a multi-cluster unified management module, and realizes cross-cluster resource dynamic optimization configuration and efficient collaboration by means of a federated cluster technology and an intelligent scheduling algorithm; according to the full-life-cycle application management system, tool chain and code detection are synchronously provided during development, automation of test deployment is achieved, and intelligent monitoring self-healing of operation and maintenance is achieved, so that service iteration is greatly accelerated. Meanwhile, the heterogeneous environment adaptation mechanism eliminates bottom layer difference through a resource abstraction layer and a self-adaptive plug-in, and seamless operation of the application is guaranteed. The security, stability and observability module constructs a multi-level protection, redundant architecture and a log, index and link comprehensive monitoring system. According to the platform, the resource utilization rate and the management efficiency are remarkably improved, the operation and maintenance cost is reduced, and a perfect solution is provided for safe, stable and efficient operation of enterprise-level applications in a cloud native environment.

Description

Technical Field

[0001] This invention relates to the field of cloud computing technology, specifically to an enterprise-level cloud-native PaaS management platform. Background Technology

[0002] With the acceleration of enterprise digital transformation, cloud-native technologies have been widely adopted due to their advantages in improving application agility, elasticity, and scalability. Existing enterprise-level PaaS management platforms meet some of the needs of enterprises to a certain extent, but some shortcomings still exist.

[0003] For example, in multi-cluster management, resource coordination and unified scheduling between different clusters are challenging, leading to low resource utilization. In application lifecycle management, the transitions between development, testing, deployment, and operation phases are not smooth enough, hindering rapid business iteration. For complex heterogeneous environments, the lack of efficient adaptation and management mechanisms makes it difficult to fully leverage the potential of various resources. Furthermore, existing platforms need further improvement in security, stability, and observability to better meet the stringent requirements of enterprise applications. Summary of the Invention

[0004] The purpose of this invention is to provide an enterprise-level cloud-native PaaS management platform to solve problems such as difficulties in multi-cluster management, lack of smooth application lifecycle management, insufficient adaptation to heterogeneous environments, and deficiencies in security, stability, and observability in existing technologies.

[0005] To achieve the above objectives, the present invention provides an enterprise-level cloud-native PaaS management platform comprising the following core modules: S1: Multi-Cluster Unified Management Module: Utilizing federated cluster management technology, this module integrates multiple Kubernetes clusters into a unified management framework. Intelligent resource scheduling algorithms monitor the real-time utilization of CPU, memory, and storage resources in each cluster, as well as application resource requirements and priority information. Based on a combination of greedy and simulated annealing algorithms, it calculates the optimal resource allocation scheme, dynamically allocating and migrating workloads to achieve cross-cluster resource optimization. Simultaneously, it establishes a high-speed UDP-based communication channel and a Raft-based data synchronization mechanism to ensure data consistency and real-time performance between different clusters, improving multi-cluster management efficiency and business continuity. S2: Full Lifecycle Application Management System 1. Development Phase: Provides a rich development toolchain, covering support for multiple programming languages ​​and frameworks, as well as various development templates to help developers quickly build application frameworks. A code quality inspection tool integrating static code analysis technology can automatically perform syntax checks, code style checks, and potential vulnerability detection; automated testing tools support unit testing, integration testing, and functional testing, promptly identifying and fixing problems during development to improve development quality. 2. Testing Phase: Utilize container technology to quickly create isolated test environments, enabling rapid setup and destruction of test environments and shortening the testing cycle. Deep integration with Continuous Integration / Continuous Delivery (CI / CD) systems automatically triggers build, test, and deployment processes when code changes occur, ensuring seamless application transition from development to production and accelerating business iteration. 3. Operation and Maintenance Phase: By injecting probes into containers, real-time performance metrics such as CPU, memory, and network speeds, as well as application logs and event data, are collected. Machine learning algorithms are used to analyze this data and build an application performance model. When abnormal performance metrics are detected or error messages appear in the logs, a self-healing mechanism is automatically triggered, such as automatically restarting faulty containers and adjusting resource allocation, to ensure stable application operation. S3: Heterogeneous Environment Adaptation and Management Mechanism: The general resource abstraction layer defines a unified resource interface and data structure, encapsulating and abstracting parameters such as CPU, memory, and disk for virtual machine resources, as well as images and runtime parameters for container resources. This shields the differences in underlying resources, enabling applications to run seamlessly in different heterogeneous environments. The adaptive network plugin automatically selects appropriate network transmission protocols and optimization strategies based on the underlying network type (such as traditional Ethernet or SDN) and performance metrics (bandwidth, latency, etc.). The adaptive storage plugin automatically adjusts storage strategies based on storage device type (HDD or SSD) and storage requirements (read / write frequency, data capacity, etc.), ensuring efficient and stable network and storage services in various heterogeneous environments. S4: Security, Stability and Observability Module 1. Security: A multi-layered security protection system is constructed. At the network layer, firewalls and intrusion detection systems are deployed to restrict unauthorized external access and monitor attack behavior in real time. At the application layer, multi-factor authentication (combining username / password, SMS verification code, and hardware token) is used to ensure the authenticity of user identities. Role-based access control (RBAC) is used to assign permissions to different users and roles to achieve fine-grained access control. At the same time, SSL / TLS protocols are used to encrypt network transmission data, and AES and other encryption algorithms are used to encrypt stored data. Security vulnerability scanning tools are used to regularly detect and fix security vulnerabilities. 2. Stability: Key components are deployed with multiple replicas, and traffic is distributed through a load balancer to ensure system redundancy. A health check mechanism periodically sends heartbeat requests to each component to monitor its operational status in real time. When a component fails, the load balancer automatically switches traffic to other healthy replicas and initiates a fault recovery process, such as automatically restarting the failed component and restoring data from backups, ensuring the platform's continuous and stable operation. 3. Observability: Integrates log management, metric monitoring, and distributed tracing functions. The log management system collects and analyzes various logs generated by the platform and applications, extracting key information; the metric monitoring system collects performance metrics of the platform and applications in real time and displays trends through visualization tools; the distributed tracing system tracks the call paths of requests between various services by embedding tracers in applications, helping operations personnel quickly locate and resolve problems, improving operational efficiency and accuracy.

[0006] In summary, due to the adoption of the above-mentioned technologies, the beneficial effects of this invention are: 1. The enterprise-level cloud-native PaaS management platform of this invention significantly improves resource utilization and multi-cluster management efficiency through a unified multi-cluster management module; the full lifecycle application management system realizes seamless integration of application development, testing, deployment and operation and maintenance, accelerating business iteration.

[0007] 2. The heterogeneous environment adaptation and management mechanism in the enterprise-level cloud-native PaaS management platform of this invention reduces the management cost of enterprises for complex heterogeneous environments and improves application compatibility; the security, stability and observability modules provide comprehensive protection for enterprise-level applications, effectively resist security threats, ensure stable system operation, and facilitate operation and maintenance management. Attached Figure Description

[0008] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention, making other features, objects, and advantages of the invention more apparent. The illustrative embodiments of the invention illustrated in the drawings and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0009] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 a part of the embodiments of the present invention, not all of them. 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. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to represent selected embodiments of the invention. 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.

[0010] In the description of this invention, it should be understood that the terms indicating orientation or positional relationship are based on the orientation or positional relationship shown in the drawings and are only for the convenience of describing the invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention.

[0011] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific context of the specification.

[0012] This invention provides an enterprise-level cloud-native PaaS management platform, comprising the following steps: (I) Implementation method of multi-cluster unified management module When deploying the federated cluster management module, an agent component is first installed on each node of the Kubernetes cluster. This component is responsible for communicating with the management platform, collecting cluster resource information, and receiving scheduling instructions from the management platform. Within the management platform, connections are established with each cluster by configuring parameters such as the API Server address and authentication information for each cluster. The intelligent resource scheduling algorithm runs in the management platform backend, periodically (e.g., every 5 minutes) collecting resource usage and application demand information from each cluster. The algorithm uses a greedy algorithm to prioritize task allocation to clusters with lower resource utilization, while simultaneously optimizing the allocation scheme using simulated annealing to avoid getting trapped in local optima and ensure globally optimal resource allocation. For example, if cluster A's CPU utilization reaches 80% while cluster B's CPU utilization is only 30%, and a new computing task is submitted, the algorithm will prioritize allocating the task to cluster B. The high-speed inter-cluster communication channel is developed based on the UDP protocol, leveraging the low-latency characteristics of the User Datagram Protocol (UDP) to achieve fast data transmission. SDN technology is used to optimize the network topology, dynamically adjusting routes based on network traffic between clusters to ensure efficient data transmission. The data synchronization mechanism employs the Raft algorithm, electing a master cluster from among multiple clusters, with the others acting as slave clusters. The master cluster handles data update operations and synchronizes the updated information to the slave clusters. Heartbeat detection and log replication mechanisms ensure data consistency across clusters. (II) Implementation Method of Full Lifecycle Application Management System 1. Development Phase: Developers access the platform's development environment using the provided Web IDE or local development tools. The platform automatically generates a project framework based on the application template selected by the developer and provides the necessary development tools and dependency libraries. During code writing, code quality monitoring tools perform real-time static analysis. If syntax errors, non-compliance with coding standards, or potential vulnerabilities are found, the platform immediately prompts the developer to make corrections on the development interface. Automated testing tools are integrated with the code repository. When developers submit code, pre-written test cases, including unit tests, integration tests, and functional tests, are automatically run. Only code that passes all tests can proceed to the next phase. 2. Testing Phase: When code submission triggers the CI / CD process, the code is first built, packaged into a container image, and pushed to the image repository. Next, using container orchestration technology, an isolated test environment containing components such as the database and middleware is quickly created based on the application's dependencies. After the test environment is created, test scripts are automatically run to perform comprehensive testing of the application. Test results are fed back to the CI / CD system. If the test passes, the image is automatically deployed to the production environment for canary releases, gradually increasing the usage of the new version of the application. If the test fails, the CI / CD system notifies the developers to modify the code, and the modified code triggers the build and testing process again. 3. Operation and Maintenance Phase: Probes in the monitoring system continuously collect application performance metrics and log data, and send the data to the data analysis module of the management platform. Machine learning algorithms train and analyze this data to establish an application performance baseline. When actual performance metrics deviate from the baseline by more than a preset threshold, or when erroneous keywords appear in the logs, a self-healing mechanism is triggered. For example, if a container's CPU utilization is detected to be persistently high, exceeding 80%, for a duration of 10 minutes, the system automatically restarts the container and appropriately increases its resource quota based on historical data to prevent similar problems from recurring. (III) Implementation of Heterogeneous Environment Adaptation and Management Mechanism The general resource abstraction layer loads various resource drivers during platform startup, mapping resource parameters to a unified resource model based on the underlying resource type (virtual machine, physical machine, container, etc.) and hardware architecture (x86, ARM, etc.). During application deployment, the platform retrieves suitable resource instances from the abstraction layer based on the application's resource requirements and deploys the application to the corresponding infrastructure. For example, for an application requiring high computing performance, the platform will select a high-performance x86 architecture physical machine resource from the abstraction layer for deployment. The adaptive network plugin monitors the underlying network's performance metrics, such as bandwidth and latency, in real time. When sufficient network bandwidth and low latency (e.g., bandwidth greater than 1Gbps and latency less than 10ms) are detected, it automatically selects the TCP protocol for data transmission and enables TCP fast retransmission and congestion control optimization to improve the reliability and efficiency of data transmission. When the network environment is poor (e.g., bandwidth less than 100Mbps and latency greater than 50ms), it switches to the UDP protocol and uses forward error correction technology to encode the data, ensuring reliable data transmission under unstable network conditions. The adaptive storage plugin dynamically adjusts storage strategies based on the type of storage device and the storage needs of the application. For applications with frequent read / write operations on small files, it prioritizes solid-state drive (SSD) storage and sets up caching in memory to improve read / write speeds. For applications storing large amounts of unstructured data, it uses a distributed storage system combined with hard disk drives (HDDs) to reduce storage costs. Simultaneously, the storage plugin periodically performs health checks on storage devices. When a storage device failure is detected, it automatically migrates data to other healthy devices to ensure data availability. (iv) Implementation of the Security, Stability and Observability Module 1. Security: The firewall, based on the enterprise's security policies, configures access control rules to restrict external network access to the platform's internal services. For example, it may only allow specific IP address ranges to access the application's web service ports. The intrusion detection system analyzes network traffic characteristics and behavioral patterns, using machine learning algorithms to identify abnormal traffic and attack behaviors. Once an attack is detected, it immediately sends an alert to the administrator and automatically blocks the attack source's access. The identity authentication system supports multi-factor authentication. When logging in, users need to enter not only their username and password, but also a mobile SMS verification code or insert a hardware token for identity verification. The access control module is based on the RBAC model, assigning corresponding permissions to different users and roles. For example, the administrator role has management permissions for all platform resources, while the developer role can only operate on the applications they have developed. Regarding data encryption, SSL / TLS protocols are used to encrypt data during network transmission to ensure that data is not stolen or tampered with during network transmission. In the data storage stage, AES encryption algorithm is used to encrypt sensitive data, and only users with the correct key can decrypt and access the data. Security vulnerability scanning tools regularly (e.g., weekly) scan the platform's operating system, applications, and dependency libraries to detect known security vulnerabilities and generate detailed vulnerability reports. Administrators then use these reports to remediate vulnerabilities. 2. Stability: Key components (such as the API Server and database) are deployed with multiple replicas, each running on a different server node. The load balancer distributes user requests evenly across the replicas according to a preset load balancing algorithm (such as round-robin or weighted round-robin), preventing any single component from being overloaded. The health check mechanism detects the operational status of components by periodically sending heartbeat requests to each component (e.g., every minute). If a component fails to respond within three consecutive heartbeat cycles, the load balancer removes that component from the available list, stops distributing requests to it, and initiates a fault recovery process. The fault recovery process first attempts to automatically restart the failed component. If the restart fails, data is restored from backups, and the component is redeployed to ensure the system returns to normal operation as quickly as possible. 3. Observability: The log management system collects all logs generated by the platform and applications, including system logs, application runtime logs, and user operation logs. Log analysis tools are used to structure the logs, extracting key information (such as timestamps, operation types, and error messages), and supporting querying and analysis based on keywords, time ranges, and other conditions. The performance metrics monitoring system collects real-time performance metrics of the platform and applications (such as CPU utilization, memory utilization, network traffic, response time, etc.), stores the data in a time-series database, and displays the trends of these metrics through visualization tools (such as dashboards, line charts, bar charts, etc.). Operations and maintenance personnel can intuitively understand the system's operating status and promptly identify performance bottlenecks and anomalies. A distributed tracing system embeds a tracer in each service node of the application. When a user request enters the system, the tracer generates a unique tracing ID for the request and passes it between services. Using the tracing ID, the system can record information such as the call path, call time, and parameters of the request across various services, generating a complete call chain diagram. When the application experiences performance issues or failures, operations and maintenance personnel can quickly locate the problematic service node and code location using the call chain diagram, facilitating troubleshooting and repair.

Claims

1. An enterprise-grade cloud-native PaaS management platform, characterized in that: Includes the following steps: S1: Multi-cluster unified management module, used to manage multiple Kubernetes clusters in a unified manner. Through intelligent resource scheduling algorithms, it dynamically allocates and migrates workloads based on the load, resource type and application requirements of each cluster, realizes cross-cluster resource optimization configuration, and establishes high-speed communication channels and data synchronization mechanisms between clusters to ensure data consistency and real-time performance. S2: A full lifecycle application management system, including providing development toolchains, templates, code quality inspection, and automated testing tools during the development phase; During the testing phase, container technology is used to create isolated test environments and integrate them with the CI / CD system; during the operation and maintenance phase, application performance indicators, logs and event data are collected in real time, and machine learning algorithms are used for analysis to achieve intelligent monitoring and self-healing. S3: Heterogeneous environment adaptation and management mechanism, including a general resource abstraction layer for unified abstraction and management of different types of infrastructure resources and different architecture hardware, as well as adaptive network and storage plug-ins that automatically adjust data transmission and storage strategies according to the underlying network and storage characteristics; S4: Security, Stability and Observability Module. In terms of security, it adopts a multi-layered security protection system and a security vulnerability scanning and repair mechanism. In terms of stability, it builds a redundant and fault-tolerant architecture and sets up health check and fault recovery mechanisms. In terms of observability, it integrates log management, indicator monitoring and distributed link tracing functions.

2. The method for screening high-efficiency salt-tolerant rice according to claim 1, characterized in that, In the multi-cluster unified management module, the intelligent resource scheduling algorithm calculates the optimal resource allocation scheme based on the utilization rate of resources such as CPU, memory, and storage in each cluster, as well as application resource requirements and priority information, combined with greedy algorithms and simulated annealing algorithms.

3. The method for screening high-efficiency salt-tolerant rice according to claim 1, characterized in that, During the development phase of the application lifecycle management system, the code quality inspection tool uses static code analysis technology to perform syntax checks, code style checks, and potential vulnerability detection.

4. The method for screening high-efficiency salt-tolerant rice according to claim 1, characterized in that, During the operation and maintenance phase of the application lifecycle management system, application performance indicators and log data are collected by injecting probes into containers. An application performance model is established using machine learning algorithms. When abnormal performance indicators or log errors are detected, a self-healing mechanism is automatically triggered.

5. The method for efficiently screening salt-tolerant rice according to claim 1, characterized in that, In the heterogeneous environment adaptation and management mechanism, the general resource abstraction layer defines a unified resource interface and data structure, and encapsulates and abstracts the CPU, memory, and disk parameters of virtual machine resources, as well as the image and runtime parameters of container resources.

6. The method for efficiently screening salt-tolerant rice according to claim 1, characterized in that, In the heterogeneous environment adaptation and management mechanism, the adaptive network plugin, based on the underlying network type and performance indicators, adopts the TCP protocol and enables fast retransmission and congestion control optimization in high-bandwidth, low-latency environments, and switches to the UDP protocol and adopts forward error correction technology in poor network environments.

7. The method for efficiently screening salt-tolerant rice according to claim 1, characterized in that, In terms of security, stability, and observability, the module employs multi-factor authentication, combining username / password, SMS verification code, and hardware token to ensure user authenticity. It also uses a role-based access control model to assign permissions to different users and roles. Key components are deployed with multiple replicas, and traffic is distributed through a load balancer. A health check mechanism periodically sends heartbeat requests to each component to detect its operational status. In case of a failure, traffic is automatically switched and a fault recovery process is initiated.

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