Cross-domain PaaS cloud platform distributed architecture scheduling method and system

Abstract

The invention relates to the technical field of cloud computing, in particular to a cross-domain PaaS cloud platform distributed architecture scheduling method and system. The method comprises the following steps: carrying out architecture design and deployment, deploying a main node, a standby node and an arbitration node in different data centers, and configuring network connection; carrying out load balancing configuration, setting a load balancer and a health check mechanism, and regularly detecting the state of a service node; designing a scheduling strategy, and designing a scheduling algorithm according to the node load condition, the resource utilization rate and the response time; and carrying out distributed database deployment and formulating an availability strategy, including configuring an arbitration node and designing a database availability strategy. High availability of the system is achieved through multi-data center deployment, the overall performance of the system is improved through intelligent load balancing and dynamic resource scheduling, data consistency and service continuity are ensured through an arbitration mechanism, the technical problems of a traditional PaaS architecture in the aspects of cross-region deployment, resource scheduling and disaster recovery are solved, and the system is suitable for large-scale popularization and application. And cross-domain multi-activity, intelligent scheduling and automatic disaster recovery are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing, and particularly to a cross-domain PaaS cloud platform distributed architecture scheduling method and system. Background Art

[0002] With the in-depth promotion of enterprise digital transformation, cloud computing technology has become the infrastructure to support business development. Among them, Platform as a Service (PaaS), as an important service model of cloud computing, provides an application development, testing and deployment environment for enterprises, enabling developers to focus on application development without having to manage the underlying infrastructure. However, the traditional PaaS architecture still faces many challenges in cross-regional deployment and operation and maintenance.

[0003] In the prior art, PaaS platforms usually rely on a single data center and nodes for deployment, lacking standby nodes and data centers as disaster recovery guarantees. When the primary node or data center fails, the entire system may be severely affected, resulting in service interruption and unable to meet the high requirements of enterprises for business continuity. Especially for large enterprises with a wide geographical distribution, a PaaS platform deployed in a single region cannot effectively cope with the risk of system interruption caused by regional network failures or natural disasters.

[0004] In addition, the existing PaaS architecture also has deficiencies in resource scheduling. Due to the lack of an efficient cross-domain resource scheduling mechanism, the system cannot dynamically allocate computing resources according to the real-time load situation, resulting in overuse of resources on some nodes while other nodes are idle, which not only reduces the overall performance of the system but also increases the operation and maintenance costs. At the same time, the load balancing strategy of the traditional architecture is relatively simple and cannot fully consider multi-dimensional factors such as the geographical location of nodes, network latency, and resource utilization rate, making it difficult to achieve true intelligent scheduling.

[0005] A cross-domain distributed architecture refers to a distributed system architecture that spans multiple geographical locations or network regions. It can distribute computing resources, storage resources, and network resources in different data centers and provide highly available, reliable, and high-performance services by coordinating the work of these distributed components. However, to implement an efficient cross-domain PaaS distributed system, it is necessary to solve technical problems in many aspects such as inter-node communication latency, data consistency, fault detection and recovery.

[0006] Therefore, there is an urgent need for a cloud platform architecture scheduling method that can achieve cross-domain multi-live, dynamic resource scheduling, automatic disaster recovery, and data consistency to improve the reliability, continuity, and high availability of cloud services and meet the core requirements of enterprises for cloud platforms in the digital transformation. Summary of the Invention

[0007] In view of the problems existing in the prior art, the present invention is proposed.

[0008] Therefore, the problem to be solved by the present invention is a cloud platform architecture scheduling method that can achieve cross-domain multi-live, dynamic resource scheduling, automatic disaster recovery, and data consistency.

[0009] To solve the above technical problems, the present invention provides the following technical solutions:

[0010] In a first aspect, an embodiment of the present invention provides a distributed architecture scheduling method for a cross-domain PaaS cloud platform, which includes performing architecture design and deployment, including preparing data centers, node deployment, and preparing business clusters;

[0011] Performing load balancing configuration, including setting up a load balancer and health checks. The setting of the load balancer is to deploy the load balancer, configure it to receive requests from users, and configure load balancing policies for traffic distribution. At the same time, configure the health check mechanism of the load balancer to regularly detect the status of each business node;

[0012] Performing scheduling policy design, including formulating resource scheduling policies and setting task scheduling policies, and designing a resource scheduling algorithm based on the load situation, resource utilization rate, and response time of the current node, and performing dynamic scheduling;

[0013] Performing distributed database deployment and formulating availability policies, including deploying a distributed database, configuring arbitration nodes, and designing the availability policy of the database.

[0014] As a preferred solution of the cross-domain PaaS cloud platform distributed architecture scheduling method of the present invention, wherein: the determination of the data center location includes the physical locations of the primary node, standby node, and arbitration node, and is selected based on geographical location, disaster recovery ability, and bandwidth connection. At the same time, design the network architecture, including internal network and external network connections, and configure the network for bandwidth connection between different data centers, and configure firewall and security group rules;

[0015] The node deployment is to deploy cloud platform management nodes, control nodes, and distributed storage control nodes in each data center, and deploy infrastructure layer platform computing nodes and storage data nodes in the primary node and standby node. The preparation of the business cluster is to deploy platform-as-a-service control nodes and business clusters in the primary node and standby node.

[0016] As a preferred solution of the cross - domain PaaS cloud platform distributed architecture scheduling method of the present invention, wherein: the deployment of the cloud platform management node is to install and configure the cloud platform management software on the master node and the standby node, and at the same time configure the network interface and storage settings of the management node. The cloud platform management software includes an open - source cloud computing management platform;

[0017] The deployment of the control node is to install the control node service on the master node and the standby node, and configure the communication between the control node and the management node;

[0018] The deployment of the distributed storage control node is to deploy a distributed storage solution in each data center, and configure the network and storage pool of the storage node. The distributed storage solution includes an open - source distributed storage system;

[0019] The deployment of the infrastructure layer platform computing node is to install the computing node service on the master node and the standby node, and at the same time configure the network settings and resource management of the computing node;

[0020] The deployment of the storage data node is to configure the storage data node in the distributed storage system.

[0021] As a preferred solution of the cross - domain PaaS cloud platform distributed architecture scheduling method of the present invention, wherein: the deployment of the platform - as - a - service control node is to install the platform - as - a - service control platform on the master node and the standby node and configure the network and storage of the platform - as - a - service control node. The platform - as - a - service control platform includes an open - source container orchestration platform and an open - source application platform;

[0022] The deployment of the business cluster creates a business cluster environment on the master node and the standby node according to business requirements, and configures service discovery to assist subsequent load - balancing settings.

[0023] As a preferred solution of the cross - domain PaaS cloud platform distributed architecture scheduling method of the present invention, wherein: the load - balancing configuration includes:

[0024] Select the type of load - balancer according to usage requirements. The types of load - balancers include hardware load - balancers, software load - balancers, and load - balancers of cloud service providers;

[0025] Deploy the selected load - balancer. The deployment content includes the cloud environment and the in - house servers;

[0026] Configure the load - balancer, including setting the listening port and specifying the backend service, and at the same time select the load - balancing algorithm;

[0027] Configure the health - check mechanism. First, set up the health - check service, then specify the health - check protocol, then configure the health - check request path, and finally set the health - check frequency and timeout warning.

[0028] As a preferred solution of the cross - domain PaaS cloud platform distributed architecture scheduling method of the present invention, wherein: after the scheduling strategy is designed, according to the business requirements and resource conditions, tasks are assigned to the corresponding business nodes for execution, and dynamic migration and scheduling of tasks are performed.

[0029] As a preferred solution of the cross - domain PaaS cloud platform distributed architecture scheduling method of the present invention, wherein: the deployment of the distributed database includes selecting a database system, preparing the environment, installing database software, configuring the database cluster, and initializing the database;

[0030] The configuration of the arbitration node includes selecting an arbitration node, installing an arbitration service, and configuring arbitration rules;

[0031] The content of the availability strategy design of the database includes data replication strategy, failover mechanism, load balancing, monitoring and alarm, and regular backup;

[0032] The arbitration node is set as an independent node, and communication between the arbitration node and the data node is established for election in case of failure.

[0033] In a second aspect, an embodiment of the present invention provides a cross - domain PaaS cloud platform distributed architecture scheduling system, which includes an architecture design and deployment module for performing architecture design and deployment, including preparing a data center, node deployment, and preparing a business cluster;

[0034] A load - balancing configuration module for performing load - balancing configuration, including setting a load balancer and health check. The setting of the load balancer is to deploy a load balancer, configure it to receive requests from users, configure a load - balancing strategy for traffic distribution, and at the same time configure a health - check mechanism for the load balancer to regularly detect the status of each business node;

[0035] A scheduling strategy design module for performing scheduling strategy design, including formulating a resource scheduling strategy and setting a task scheduling strategy, and designing a resource scheduling algorithm according to the load condition, resource utilization rate, and response time of the current node for dynamic scheduling;

[0036] A database deployment module for performing distributed database deployment and formulating an availability strategy, including deploying a distributed database, configuring an arbitration node, and designing an availability strategy for the database.

[0037] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein: when the computer program instructions are executed by the processor, the steps of the cross - domain PaaS cloud platform distributed architecture scheduling method as described in the first aspect of the present invention are implemented.

[0038] Fourthly, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, wherein: when the computer program instructions are executed by a processor, the steps of the cross-domain PaaS cloud platform distributed architecture scheduling method described in the first aspect of the present invention are implemented.

[0039] The beneficial effects of the present invention are as follows: Through systematic architecture design and deployment, intelligent load balancing configuration, dynamic scheduling strategy design, and highly available distributed database deployment, the present invention constructs a complete cross-domain PaaS cloud platform distributed architecture scheduling method and system. This method and system solve the technical problems of traditional PaaS architectures in cross-regional deployment, resource scheduling, and disaster tolerance, and achieve key technical goals such as cross-domain multi-live, intelligent scheduling, automatic disaster tolerance, and data consistency.

[0040] Compared with the prior art, the present invention not only improves the reliability, continuity, and service quality of the cloud platform, but also reduces the operation and maintenance complexity and cost, and better meets the enterprise's demand for high-performance and highly available cloud services during the digital transformation process. This distributed architecture based on multiple data centers and multiple nodes provides enterprises with a more flexible and reliable cloud computing infrastructure, supporting the global deployment and high-speed growth of businesses. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0042] Figure 1 It is a flowchart of the cross-domain PaaS cloud platform distributed architecture scheduling method;

[0043] Figure 2 It is a computer device diagram of the cross-domain PaaS cloud platform distributed architecture scheduling method;

[0044] Figure 3 It is a schematic diagram of the framework composition of the cross-domain PaaS cloud platform distributed architecture scheduling method;

[0045] Figure 4 It is a schematic diagram of the cross-domain deployment of the IaaS platform for the cross-domain PaaS cloud platform distributed architecture scheduling method;

[0046] Figure 5 It is a schematic diagram of the cross-domain PaaS distributed deployment of the cross-domain PaaS cloud platform distributed architecture scheduling method;

[0047] Figure 6It is a schematic diagram of cross-cloud platform resource scheduling for the distributed architecture scheduling method of the cross-domain PaaS cloud platform. Specific Embodiments

[0048] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the specific embodiments of the present invention in detail with reference to the accompanying drawings of the specification.

[0049] In the following description, many specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0050] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selectively exclusive embodiment from other embodiments.

[0051] Embodiment 1

[0052] Referring to Figures 1 to 6 , this is the first embodiment of the present invention. This embodiment provides a distributed architecture scheduling method for a cross-domain PaaS cloud platform, including:

[0053] S100: Perform architecture design and deployment, including preparing the data center, node deployment, and preparing the business cluster;

[0054] In this embodiment, architecture design and deployment are the basic steps of the entire distributed architecture scheduling method for the cross-domain PaaS cloud platform, mainly including three key links: data center preparation, node deployment, and business cluster preparation. The reasonable planning and effective implementation of these links play a decisive role in ensuring the stability, reliability, and high availability of the entire distributed system.

[0055] Specifically, first, a comprehensive design of the system architecture needs to be carried out, including determining the overall structure of the system, node distribution, network topology, and the interaction method between components. During the design process, various factors such as business requirements, performance requirements, security, scalability, and maintainability need to be fully considered to ensure that the architecture design can meet the current and future business development needs.

[0056] In an optional embodiment, a layered design method can be adopted in the architecture design phase, dividing the entire system into three layers: the infrastructure layer, the platform service layer, and the application service layer. The infrastructure layer mainly includes physical and virtual resources such as servers, storage, and networks; the platform service layer mainly provides platform-level services such as middleware, databases, and message queues; and the application service layer is mainly responsible for implementing business logic and user interaction functions. Through this layered design, the coupling degree of each part of the system can be reduced, and the maintainability and scalability of the system can be improved.

[0057] It should be noted that when conducting architecture design, it is necessary to follow the design principle of "high cohesion and low coupling" to ensure that the interfaces between system modules are clear and the dependency relationships are simple. At the same time, fault tolerance design also needs to be considered. Through mechanisms such as redundant deployment, fault detection, and automatic recovery, ensure that the system can still operate normally in the face of partial component failures. In addition, performance evaluation and capacity planning are also required to ensure that the system can provide stable service quality during peak business periods.

[0058] S101: The determination of the data center location includes the physical locations of the primary node, standby node, and arbitration node, and is selected based on geographical location, disaster tolerance ability, and bandwidth connection. At the same time, design the network architecture, including internal and external network connections, and configure the network for bandwidth connection between different data centers, and configure firewall and security group rules;

[0059] Node deployment is to deploy cloud platform management nodes, control nodes, and distributed storage control nodes in each data center, and deploy infrastructure layer platform computing nodes and storage data nodes in the primary node and standby node. The preparation of the business cluster is to deploy platform-as-a-service control nodes and business clusters in the primary node and standby node.

[0060] S102: The deployment of the cloud platform management node is to install and configure cloud platform management software on the primary node and standby node, and at the same time configure the network interface and storage settings of the management node. The cloud platform management software includes an open-source cloud computing management platform;

[0061] The deployment of the control node is to install the control node service on the primary node and standby node, and configure the communication between the control node and the management node;

[0062] The deployment of the distributed storage control node is to deploy a distributed storage solution in each data center, and configure the network and storage pool of the storage node. The distributed storage solution includes an open-source distributed storage system;

[0063] The deployment of the infrastructure layer platform computing node is to install the computing node service on the primary node and standby node, and at the same time configure the network settings and resource management of the computing node;

[0064] The deployment of storage data nodes is to configure the storage data nodes in a distributed storage system.

[0065] S103: The deployment of the Platform as a Service (PaaS) control node is to install the PaaS control platform on the primary node and the standby node and configure the network and storage of the PaaS control node. The PaaS control platform includes an open-source container orchestration platform and an open-source application platform;

[0066] The business cluster deployment creates a business cluster environment on the primary node and the standby node according to business requirements, and configures service discovery to assist subsequent load balancing settings.

[0067] S200: Perform load balancing configuration, including setting up a load balancer and health checks. The setting of the load balancer is to deploy the load balancer, configure it to receive requests from users, and configure load balancing policies for traffic distribution. At the same time, configure the health check mechanism of the load balancer to periodically detect the status of each business node;

[0068] In this embodiment, the load balancing configuration is a key link to ensure the high availability and performance optimization of the system. The load balancer, as the front-end entry of the distributed system, is responsible for distributing user requests to different backend servers to achieve load dispersion and fault isolation. At the same time, through the health check mechanism, the load balancer can detect the availability of the backend servers, automatically shield faulty nodes, and ensure the overall availability of the system.

[0069] Specifically, the setting of the load balancer first requires selecting a suitable type of load balancer, then deploying and configuring it, and finally setting up the health check mechanism. The load balancer is configured to receive requests from users and distribute traffic to different business nodes according to the preset load balancing policy. At the same time, it periodically detects the status of each business node through the health check mechanism to ensure that only healthy nodes can receive traffic.

[0070] In an alternative embodiment, a multi-level load balancing architecture can be adopted, that is, load balancers are deployed at different levels. For example, a DNS load balancer is deployed at the global level to distribute user requests to the nearest data center; a hardware load balancer, such as F5 or Citrix NetScaler, etc., is deployed at the data center level to handle a large number of concurrent connections and complex traffic management; a software load balancer, such as Nginx or HAProxy, etc., is deployed at the application level to achieve more fine-grained request distribution and application-level health checks. This multi-level load balancing architecture can provide higher scalability and flexibility to adapt to different scales and types of business requirements.

[0071] It should be noted that when setting up a load balancer, factors such as performance, reliability, functional characteristics, and cost need to be comprehensively considered. Different types of load balancers have their own advantages and disadvantages, and need to be selected according to the actual situation. For example, hardware load balancers usually have higher performance and richer functions, but the cost is relatively high; software load balancers are more flexible and economical, but may have lower performance than hardware devices under extremely high loads. In addition, the redundant deployment of the load balancer needs to be considered. Usually, the primary-backup or cluster mode is adopted to ensure rapid switching in case of a failure of the load balancer itself and avoid single point of failure. Finally, the selection of the load balancing strategy also needs to be carefully considered. Different strategies are suitable for different business scenarios. For example, the round-robin strategy is suitable for servers with similar processing capabilities, the weighted round-robin strategy is suitable for servers with different processing capabilities, the least connections strategy is suitable for long connection services, and the IP hash strategy is suitable for services that require session persistence.

[0072] S201: The load balancing configuration includes:

[0073] Select the type of load balancer according to the usage requirements. The types of load balancers include hardware load balancers, software load balancers, and load balancers provided by cloud service providers;

[0074] Deploy the selected load balancer. The deployment content includes cloud environments and in-house servers;

[0075] Configure the load balancer, including setting the listening port and specifying the backend service, and at the same time select the load balancing algorithm;

[0076] Configure the health check mechanism. First, set the health check service, then specify the health check protocol, then configure the health check request path, and finally set the health check frequency and timeout warning.

[0077] S300: Conduct scheduling strategy design, including formulating resource scheduling strategies and setting task scheduling strategies, and designing resource scheduling algorithms according to the load conditions, resource utilization rates, and response times of the current nodes, and performing dynamic scheduling;

[0078] In this embodiment, the scheduling strategy design is the core link of the distributed architecture of the cross-domain PaaS cloud platform. It determines how the system efficiently allocates and manages resources such as computing, storage, and networks, and how to reasonably arrange and execute various tasks. An excellent scheduling strategy can maximize resource utilization, improve the system's response speed and throughput, and at the same time ensure the timely completion of critical tasks and the stable guarantee of service quality.

[0079] The scheduling strategy design includes formulating resource scheduling strategies and setting task scheduling strategies, and designing a resource scheduling algorithm based on the load situation, resource utilization rate, and response time of the current node, and performing dynamic scheduling. The resource scheduling strategy mainly focuses on the allocation and management of system resources, while the task scheduling strategy focuses on the allocation and execution order of specific tasks. These two strategies need to cooperate closely to jointly achieve the efficient operation of the system.

[0080] In this embodiment, the scheduling algorithm evaluates the status and performance of nodes based on multiple key metrics, including load score, utilization score, and response time score. This multi-dimensional evaluation method can more comprehensively and accurately reflect the actual operating conditions of nodes, providing a reliable decision-making basis for resource allocation and task scheduling.

[0081] Specifically, the load score (LoadScore) reflects the current workload of the node, usually related to indicators such as CPU usage, memory usage, and network traffic; the utilization score (UtilizationScore) reflects the resource utilization efficiency of the node, related to the ratio of resource allocation and actual consumption; the response time score (ResponseTimeScore) reflects the speed at which the node processes requests, directly related to user experience and service quality.

[0082] These three metrics calculate the total score through weighted average, and the weight coefficients w1, w2, and w3 can be adjusted according to specific requirements and scenarios. For example, for compute-intensive applications, the weight of the load score can be increased; for interactive applications, the weight of the response time score can be increased; for resource-constrained environments, the weight of the utilization score can be increased.

[0083] In an alternative embodiment, it is possible to set w1 = 0.4, w2 = 0.3, w3 = 0.3, that is, the load situation accounts for 40% of the weight, and resource utilization and response time each account for 30% of the weight. This configuration is suitable for most general scenarios, taking into account both the system's load balancing and the resource utilization efficiency and service response speed. In practical applications, these weight coefficients can also be adjusted regularly based on monitoring data and performance test results to adapt to changes in business requirements and fluctuations in system status.

[0084] It should be noted that the design of the scheduling algorithm should follow the following principles: First, the algorithm should be efficient and capable of making scheduling decisions within an acceptable time, especially in large-scale distributed systems; Second, the algorithm should be fair to ensure that each task and user can obtain a reasonable resource allocation; Third, the algorithm should be adaptable and capable of dynamically adjusting the scheduling strategy according to changes in system load and resource status; Finally, the algorithm should be scalable and able to maintain good performance and effects as the system scale expands. In addition, when designing the scheduling algorithm, fault tolerance and stability also need to be considered to ensure that the system can still operate normally in the case of partial component failures or load fluctuations.

[0085] S301: After the scheduling strategy is designed, tasks are assigned to the corresponding business nodes for execution according to business requirements and resource conditions, and dynamic migration and scheduling of tasks are carried out.

[0086] S400: Deploy a distributed database and formulate an availability strategy, including deploying a distributed database, configuring an arbitration node, and designing the availability strategy of the database.

[0087] In this embodiment, the deployment of the distributed database and the formulation of the availability strategy are important components of the distributed architecture of the cross-domain PaaS cloud platform, which provides highly available and high-performance data storage and access services for the entire system. Different from traditional single-machine databases, distributed databases store data dispersedly on multiple physical nodes and achieve high availability and scalability of data through mechanisms such as replication, sharding, and consistency protocols.

[0088] Deploying a distributed database and formulating an availability strategy include deploying a distributed database, configuring an arbitration node, and designing the availability strategy of the database. These three links are closely related and jointly ensure the reliable operation and high availability of the distributed database.

[0089] S401: The deployment of the distributed database includes selecting a database system, preparing the environment, installing database software, configuring the database cluster, and initializing the database;

[0090] The configuration of the arbitration node includes selecting an arbitration node, installing an arbitration service, and configuring arbitration rules;

[0091] The design content of the availability strategy of the database includes data replication strategy, failover mechanism, load balancing, monitoring and alarm, and regular backup;

[0092] The arbitration node is set as an independent node, and communication between the arbitration node and the data node is established for election in case of failure.

[0093] The deployment of a distributed database includes selecting a database system, preparing the environment, installing the database software, configuring the database cluster, and initializing the database. This series of steps needs to be systematically planned and executed to ensure the correct deployment and stable operation of the distributed database.

[0094] Selecting a database system is the first and one of the most crucial decisions in the deployment of a distributed database. Different distributed database systems have their own characteristics and applicable scenarios. Selecting the appropriate system is crucial for performance and reliability.

[0095] In this embodiment, optional distributed database systems include Apache Cassandra, MongoDB, CockroachDB, and Google Spanner, etc. Factors to be considered when selecting include: data model (relational, document-based, key-value, etc.), consistency model (strong consistency, eventual consistency, etc.), performance characteristics (read / write performance, scalability, etc.), operation and maintenance complexity (installation and configuration, monitoring and management, etc.), as well as community support and maturity, etc.

[0096] In an optional embodiment, CockroachDB can be selected as the distributed database system. CockroachDB is an open-source distributed SQL database that combines the distributed architecture of Google Spanner and the SQL interface of PostgreSQL, providing strong consistency, high availability, and horizontal scalability. CockroachDB uses the Raft consensus algorithm to ensure data consistency, supports automatic sharding and automatic replication, and is suitable for distributed applications that require ACID transactions and an SQL interface. Compared with other options, CockroachDB provides the advantages of a distributed system while maintaining SQL compatibility, making it easier to migrate from traditional relational databases.

[0097] Environment preparation is to create a suitable operating environment for database deployment, including hardware resource configuration, operating system selection and optimization, network environment setting, etc. Different database systems may have different requirements for the environment and need to be prepared according to the specific selection.

[0098] In an optional embodiment, high-performance physical servers or virtual machines can be prepared for the distributed database cluster, with each node equipped with a multi-core CPU, a large amount of memory, and SSD storage. The operating system can be selected from Linux distributions such as Ubuntu, CentOS, etc., and appropriate kernel parameter adjustments can be made, such as increasing the file descriptor limit and optimizing memory management parameters. In terms of the network environment, it is necessary to ensure stable and low-latency network connections between nodes. It is best to use a dedicated network or VLAN to isolate database traffic to improve performance and security. In addition, appropriate firewall rules need to be set to only allow necessary ports to be open to protect the database security.

[0099] Installing the database software is the process of deploying the selected database system into the prepared environment. The specific steps vary depending on the database system, but generally include downloading the software package, verifying the integrity, installing the dependency libraries, and executing the installation script, etc.

[0100] In an optional embodiment, taking CockroachDB as an example, the installation process can adopt the binary package installation method. First, download the latest version of the binary package from the official website; then, verify the SHA256 hash value of the package to ensure the file integrity; next, decompress the file to the specified directory, such as / opt / cockroach; finally, add the executable file path to the system PATH environment variable for convenient command-line operation. For the case where deployment on multiple nodes is required, automation tools such as Ansible can be used to write playbook scripts to achieve a batch and consistent installation process.

[0101] Configuring the database cluster is the process of connecting multiple independent database nodes into a cluster that works collaboratively. This process involves multiple aspects such as inter-node communication configuration, cluster topology design, and replication strategy setting. The correct cluster configuration is the basis for the high availability and high performance of the distributed database.

[0102] In an optional embodiment, still taking CockroachDB as an example, the cluster configuration can be carried out according to the following steps: First, create a data directory on each node, such as / data / cockroach; then, execute the initialization command on the first node to create the cluster; next, execute the join command on other nodes, specifying the address of the initial node to join the cluster; finally, verify the cluster status to ensure that all nodes are normally connected and working. During the configuration process, it is necessary to set an appropriate replication factor (usually 3 or 5) to ensure that there are enough copies of the data; set an appropriate sharding strategy to achieve uniform distribution of the data; configure an appropriate read-write consistency level to balance the consistency and performance requirements.

[0103] Initializing the database is the process of creating necessary database objects (such as databases, tables, indexes, etc.) and importing initial data after the cluster configuration is completed. This process provides the application with the basic data structure and initial data.

[0104] In an optional embodiment, SQL scripts can be used to initialize the database. The scripts contain SQL statements for creating databases, creating tables, creating indexes, setting permissions, etc. For the initial data that needs to be imported, batch import tools such as the COPY command or dedicated import tools can be used to import data from files in formats such as CSV and JSON. After initialization, basic tests also need to be performed to verify the correctness of the database objects and the compliance of performance metrics.

[0105] It should be noted that the deployment of a distributed database is a complex process that requires comprehensive consideration of multiple factors. In addition to the basic steps mentioned above, aspects such as security, monitoring, backup and recovery, and performance tuning also need to be considered. In terms of security, authentication and authorization mechanisms need to be configured to protect database access security; in terms of monitoring, a comprehensive monitoring system needs to be set up to track the running status and performance metrics of the database in real time; in terms of backup and recovery, a regular backup strategy needs to be implemented and the feasibility of the recovery process needs to be verified; in terms of performance tuning, database parameters need to be adjusted according to the actual workload to optimize query performance. Only by comprehensively considering these factors can a stable, efficient, and secure distributed database system be built.

[0106] Configuration of the arbitration node

[0107] The configuration of the arbitration node includes selecting the arbitration node, installing the arbitration service, and configuring the arbitration rules. The arbitration node plays an important role in the distributed system. It helps the cluster make correct decisions in the face of network partitions or node failures, maintaining the consistency and availability of the system.

[0108] Selecting the arbitration node is the first step in the configuration process, and factors such as the location, performance, and reliability of the node need to be considered. In this embodiment, the arbitration node is set as an independent node, and communication is established between the arbitration node and the data nodes for election in case of failure. This independent deployment method can improve the fairness of decision-making and the reliability of the system.

[0109] In an optional embodiment, it is possible to choose to deploy the arbitration node in a data center with a relatively neutral geographical location to ensure that the network latency between the arbitration node and each data node is relatively balanced. For example, if the primary data node is located in Beijing and the backup data node is located in Shanghai, the arbitration node can be deployed in a central city such as Wuhan or Zhengzhou. The hardware configuration of the arbitration node can be relatively simple because it is mainly responsible for arbitration decisions rather than data storage and processing, but it requires a reliable network connection and power supply.

[0110] Furthermore, this embodiment also provides a cross-domain PaaS cloud platform distributed architecture scheduling system, including:

[0111] An architecture design and deployment module for architecture design and deployment, including preparing the data center, node deployment, and preparing the business cluster;

[0112] A load balancing configuration module for load balancing configuration, including setting up the load balancer and health check. The setting of the load balancer is to deploy the load balancer, configure it to receive requests from users, and configure the load balancing policy for traffic distribution. At the same time, configure the health check mechanism of the load balancer to regularly detect the status of each business node;

[0113] A scheduling policy design module for scheduling policy design, including formulating resource scheduling policies and setting task scheduling policies, and designing a resource scheduling algorithm based on the load situation, resource utilization rate, and response time of the current node, and performing dynamic scheduling;

[0114] A database deployment module for distributed database deployment and formulating availability policies, including deploying a distributed database, configuring arbitration nodes, and designing the availability policy of the database.

[0115] In summary, by deploying master nodes, standby nodes, and arbitration nodes in different data centers and configuring the network for bandwidth connection between different data centers, cross-domain distributed deployment of the system is achieved, enabling the system to work collaboratively across geographical location limitations. This design enables the system to automatically switch to the backup node when a certain data center fails, ensuring business continuity and availability, effectively solving the single point of failure problem caused by the traditional PaaS platform's reliance on a single data center, and significantly improving the disaster tolerance and reliability of the system.

[0116] By deploying cloud platform management nodes, control nodes, and distributed storage control nodes in each data center, and deploying infrastructure layer platform computing nodes and storage data nodes in master nodes and standby nodes, a multi-level and highly available cloud platform infrastructure is constructed. This deployment method not only provides high availability of basic resources but also realizes cross-domain continuity of the management plane, avoiding business interruption caused by single computer room failures, and providing a stable and reliable operating environment for upper-layer applications.

[0117] By setting up a load balancer and configuring load balancing strategies, intelligent distribution of user requests is achieved, avoiding the situation where one node is overloaded while other nodes are idle. The load balancer can receive requests from users, reasonably allocate traffic to each business node according to the load balancing algorithm, and at the same time regularly detect the status of each node through a health check mechanism to ensure that traffic is only distributed to healthy nodes. This configuration effectively solves the performance bottleneck problem caused by uneven resource allocation in the prior art and improves the overall performance and response speed of the system.

[0118] Especially by configuring a health check mechanism, the system can monitor the status of each business node in real time, promptly detect and isolate faulty nodes, and avoid distributing requests to unhealthy nodes. This mechanism enhances the system's fault detection and automatic recovery capabilities, reduces the impact of faults on the user experience, and improves the reliability and stability of the service.

[0119] By formulating resource scheduling strategies and setting task scheduling strategies, and designing a resource scheduling algorithm based on the load conditions, resource utilization rates, and response times of nodes, dynamic allocation of system resources and intelligent scheduling of tasks are achieved. This scheduling algorithm based on multi-dimensional scoring comprehensively considers various performance indicators of nodes, can more accurately evaluate the node status, and make better scheduling decisions.

[0120] Especially through the dynamic scheduling mechanism, the system can dynamically adjust resource allocation and task allocation strategies according to real-time monitoring data, and achieve dynamic migration of tasks among nodes. This mechanism solves the problem of resource idleness or overload caused by the lack of an efficient cross-domain resource scheduling mechanism in the prior art, improves resource utilization rates, reduces operation and maintenance costs, and at the same time ensures the stable operation of the business under peak loads.

[0121] By deploying a distributed database, configuring arbitration nodes, and designing the availability strategy of the database, a highly available and highly consistent data storage and management system is constructed. The distributed database disperses data storage on multiple physical nodes, and through data replication and sharding mechanisms, achieves high availability and scalability of data.

[0122] Especially by setting up independent arbitration nodes and establishing a communication mechanism between arbitration nodes and data nodes, the system can conduct elections in case of failures to ensure data consistency and service continuity. At the same time, by designing a complete availability strategy including data replication strategies, failover mechanisms, load balancing, monitoring and alarming, and regular backups, the system can effectively handle various fault scenarios and ensure the security of data and the stability of the service.

[0123] The present invention constructs a complete distributed architecture scheduling method and system for a cross-domain PaaS cloud platform through systematic architecture design and deployment, intelligent load balancing configuration, dynamic scheduling strategy design, and highly available distributed database deployment. This method and system solve the technical problems of traditional PaaS architectures in cross-regional deployment, resource scheduling, and disaster recovery, and achieve key technical goals such as cross-domain multi-live, intelligent scheduling, automatic disaster recovery, and data consistency.

[0124] Compared with the prior art, the present invention not only improves the reliability, continuity, and service quality of the cloud platform, but also reduces the operation and maintenance complexity and cost, better meeting the needs of enterprises for high-performance and highly available cloud services during the digital transformation process. This distributed architecture based on multiple data centers and multiple nodes provides enterprises with a more flexible and reliable cloud computing infrastructure, supporting the global deployment and high-speed growth of businesses.

[0125] Example 2

[0126] Refer to Figure 2 - Figure 6 This is the second embodiment of the present invention.

[0127] In the first step, perform architecture design and deployment, including preparing data centers, deploying nodes, and preparing business clusters;

[0128] The preparation of the data center is to determine the data center locations of the primary node, standby node, and arbitration node, and at the same time configure the network for bandwidth connection between different data centers;

[0129] Specifically, the determination of the data center location includes the physical locations of the primary node, standby node, and arbitration node, and is selected based on geographical location, disaster tolerance ability, and bandwidth connection. At the same time, design the network architecture, including internal and external network connections, and configure firewall and security group rules.

[0130] It should be noted that the preparation of the data center is to select the locations of the primary node, standby node, and arbitration node, and choose data centers with dispersed geographical locations to prevent single-point failures caused by natural disasters or network failures. And determine the locations of the primary node, standby node, and arbitration node to ensure that each node is in different network regions. Then perform network configuration to configure the bandwidth connection between data centers to ensure low-latency and high-bandwidth network connections to support cross-domain data transmission and communication.

[0131] Node deployment is to deploy cloud platform management nodes, control nodes, and distributed storage control nodes in each data center, and deploy IaaS platform computing nodes and storage data nodes in the primary node and standby node;

[0132] It should be noted that a cloud platform management node is deployed in each data center to be responsible for the management and monitoring of the entire cloud environment, a control node is deployed to manage computing resources, and a distributed storage control node is deployed to manage storage resources. At the same time, computing nodes and storage data nodes of the IaaS platform are deployed on the primary node and the standby node to provide infrastructure services.

[0133] Specifically, the deployment of the cloud platform management node is to install and configure the cloud platform management software on the primary node and the standby node, and at the same time configure the network interface and storage settings of the management node. The cloud platform management software includes OpenStack and CloudStack.

[0134] It should be noted that OpenStack is an open-source cloud computing management platform designed to build and manage public and private clouds. It consists of multiple components and supports computing, storage, and network functions. OpenStack is composed of multiple services (including Nova, Swift, Cinder, and Neutron), and different modules are selected and deployed according to requirements. OpenStack has a developer community that provides documentation and support, and users can customize and extend functions according to their own needs.

[0135] CloudStack is an open-source cloud computing management platform mainly used to build and manage private and public clouds. The installation and configuration of CloudStack are simple, suitable for quickly building a cloud environment, and it provides an intuitive web interface for easy user management and operation. At the same time, it supports a multi-tenant environment and is suitable for service providers and internal enterprise use.

[0136] The deployment of the control node is to install the control node service on the primary node and the standby node, and configure the communication between the control node and the management node.

[0137] The deployment of the distributed storage control node is to deploy a distributed storage solution in each data center and configure the network and storage pool of the storage node. The distributed storage solution includes Ceph and GlusterFS.

[0138] It should be noted that Ceph is an extensible distributed storage system that supports object storage, block storage, and file system storage. It adopts a distributed architecture to provide high availability and high performance in large-scale clusters. Ceph has the ability to self-repair data and can automatically reconstruct data when a node fails to ensure the high availability of data. Ceph provides strong consistency storage guarantees, is suitable for application scenarios that require strict data consistency, and supports RADOS (object storage), RBD (block storage), and CephFS (file system) to meet different storage needs. At the same time, nodes can be dynamically added according to needs to support horizontal expansion.

[0139] GlusterFS is an open-source distributed file system that aggregates multiple storage devices into a unified file system, achieving high availability and scalability by distributing data across multiple nodes. The installation and configuration of GlusterFS are relatively simple, suitable for rapid deployment, and it supports multiple volume types (including distributed volumes, replicated volumes, and striped volumes), allowing for flexible configuration according to requirements. At the same time, through data replication and failover mechanisms, it ensures the high availability of data.

[0140] Reference Figure 4 As shown, the deployment of computing nodes in the IaaS platform involves installing computing node services on the primary node and the standby node, and at the same time configuring the network settings and resource management of the computing nodes.

[0141] It should be noted that through the research on cross-domain deployment of the IaaS platform, nodes such as management, control, and computing are distributed in different data centers. Combining the distributed keep-alive mechanism to ensure the stability and availability of cloud service components, and combining the distributed detection mechanism and IPMI to achieve high-availability detection of computing, realizing large-scale cross-domain applications of IaaS multi-live.

[0142] IaaS is a cloud computing service model that allows users to rent computing resources over the Internet, including servers, storage, and network devices. IaaS provides flexible infrastructure management, and users can scale resources up or down according to their needs. The characteristics of IaaS include:

[0143] Elasticity, where users can dynamically adjust resources according to actual needs, supporting rapid scaling up and down.

[0144] Autonomous control, where users have full control over the configuration of the operating system and applications and can install and run any software.

[0145] High availability, where IaaS providers offer redundancy and backup solutions to ensure the high availability of services.

[0146] The deployment of storage data nodes is to configure the storage data nodes in the distributed storage system.

[0147] The preparation of the business cluster is to deploy PaaS management nodes and the business cluster on the primary node and the standby node.

[0148] It should be noted that PaaS management nodes are deployed on the primary node and the standby node, responsible for managing the lifecycle of the business cluster; preparing the business cluster, including the deployment and configuration of applications.

[0149] Specifically, refer to Figure 5As shown in the figure, the PaaS management and control node deployment is to install the PaaS management and control platform on the primary node and the standby node and configure the network and storage of the PaaS management and control node. The PaaS management and control platform includes Kubernetes and Cloud Foundry;

[0150] It should be noted that it is necessary to study data caching and data persistence technologies in complex environments such as cross-domain and metropolitan area networks, implement caching and disk writing preservation for each synchronization task in relevant real-time streaming services, ensure that service data is not lost after any link of relevant services crashes or the network is interrupted, and the business cluster combines GSLB / DNS to achieve dual active at the business level as needed, improve the high availability of the container platform, and provide an important guarantee for business continuity.

[0151] PaaS is a cloud computing service model that provides a complete platform for developing and running applications, enabling developers to focus on application development without managing the underlying infrastructure. PaaS includes an operating system, a programming language execution environment, a database, development tools, and middleware; in a PaaS environment, Kubernetes and Cloud Foundry are two container management and application platform solutions;

[0152] Kubernetes is an open-source container orchestration platform used for automating the deployment, scaling, and management of containerized applications. Its features include container orchestration, where Kubernetes can manage hundreds or thousands of containers, automate deployment, scaling, load balancing, and fault recovery; service discovery and load balancing, where Kubernetes automatically assigns IP addresses and a single DNS name to containers, facilitating communication between services; auto-scaling, where Kubernetes automatically increases or decreases the number of container instances based on the load; self-healing capabilities, where Kubernetes can automatically restart or replace containers when they fail; and support for multiple cloud environments, running on private clouds, public clouds, or hybrid clouds.

[0153] Cloud Foundry is an open-source multi-cloud PaaS platform that provides the ability to quickly build, deploy, and manage applications. By abstracting the infrastructure, it allows developers to focus on the applications themselves. Its features include a simplified development process, where Cloud Foundry provides a CLI and a web interface for developers to easily deploy and manage applications; automated service management, providing automated management of multiple services (including databases and message queues) for easy integration; multi-language support, supporting multiple programming languages and frameworks, including Java, Ruby, Go, and Node.js; multi-cloud support, being able to run in different cloud environments, supporting public and private clouds; and application lifecycle management, providing functions such as version management, rollback, and scaling.

[0154] According to business requirements, create a business cluster environment on the primary node and the standby node, and configure service discovery to assist subsequent load balancing settings.

[0155] In the second step, perform load balancing configuration, including setting up a load balancer and health checks. The setup of the load balancer is to deploy the load balancer and configure it to receive requests from users, and configure load balancing policies for traffic distribution. At the same time, configure the health check mechanism of the load balancer to regularly detect the status of each business node;

[0156] Specifically, the specific operations in the second step include the following steps:

[0157] A1. Select the type of load balancer, which is selected according to usage requirements. The types of load balancers include hardware load balancers, software load balancers, and load balancers provided by cloud service providers;

[0158] It should be noted that a load balancer is a device or service used to distribute network traffic and application requests, improving the availability and reliability of applications. Among them, a hardware load balancer is a dedicated physical device, usually used in high-performance and high-availability environments, with specialized processors and network interfaces to support high-speed data processing and traffic distribution. Its characteristics include high performance. Since it is dedicated hardware, it can handle a large number of concurrent connections and high traffic; stability, usually having better stability and reliability, suitable for critical business applications; rich functions, supporting deep packet inspection, SSL offloading, and session persistence;

[0159] A software load balancer is a software-based solution that runs on general-purpose servers and usually relies on the operating system and network stack. They are highly flexible and can be configured and extended according to needs. Their characteristics include flexibility, being configured, deployed, and extended according to requirements; cost-effectiveness, being cheaper than hardware load balancers, suitable for enterprises with limited budgets; easy to upgrade, adding new functions or fixing problems through software updates;

[0160] The load balancing services provided by cloud service providers, as part of their cloud computing platforms, these load balancers adopt fully managed solutions, and users do not need to manage the underlying infrastructure. Their characteristics include on-demand scalability, automatically scaling according to traffic, supporting dynamic adjustment; high availability, cloud service providers usually provide redundancy and backup to ensure the high availability of the service; strong integration, being tightly integrated with other cloud services (including computing, storage, and databases), simplifying configuration and management.

[0161] A2. Deploy the selected load balancer, and the deployment content includes the cloud environment and self-owned servers;

[0162] Further, for the deployment of the cloud environment, log in to the cloud service console and create a load balancer instance. For the deployment of in-house servers, install the selected load balancing software and configure network and security group rules.

[0163] A3. Configure the load balancer, including setting the listening port and specifying the backend service, and at the same time select the load balancing algorithm;

[0164] Further, the load balancing algorithm performs a hash calculation through a hash function, expressed as:

[0165] h = H(x) (1);

[0166] H: {0,1} * → {0,1} n (2);

[0167] Where, {0,1} * represents a bit string of any length, {0,1} n represents the output space, and n represents the fixed length of the hash value.

[0168] It should be noted that the load balancing strategies include round-robin, weighted round-robin, least connections, and IP hash. Among them, round-robin distributes requests to backend servers in sequence, and each request is sent to the next server in turn until all servers have been assigned once, and then starts from the first server again. This strategy is simple to implement and is suitable for servers with similar processing capabilities;

[0169] Weighted round-robin is a further optimization of round-robin, allowing different weights to be assigned to different servers. The higher the weight, the more requests the server receives, and it makes a more reasonable traffic distribution according to the performance and processing capabilities of the servers;

[0170] The least connections strategy assigns new requests to the server with the fewest current connections, which is used to handle requests with uneven processing times and can balance the load in the case of large differences in request processing times;

[0171] The IP hash strategy calculates the hash value based on the client's IP address to determine which server to assign the request to, ensuring that requests from the same IP are always sent to the same server.

[0172] A4. Configure the health check mechanism. First, set up the health check service, then specify the protocol of the health check, regularly detect the status of each business node to ensure that traffic is only distributed to healthy nodes, then configure the request path of the health check, and finally set the frequency and timeout warning of the health check.

[0173] In the third step, perform scheduling strategy design, including formulating resource scheduling strategies and setting task scheduling strategies. Design a resource scheduling algorithm based on the load conditions, resource utilization rates, and response times of the current nodes, and perform dynamic scheduling. The scheduling algorithm is expressed as:

[0174]

[0175] Utilizationscore i = 1 - Utilization i (4);

[0176]

[0177] Score i = w1·Loadscore i + w2·Utilizationscore i + w3·ResponseTimescore i (6);

[0178] Among them, Loadscorei represents the load score of node i, Loadi represents the load of node i, Utilizationscorei represents the utilization score of node i, Utilizationi represents the utilization rate of node i, ResponseTimescorei represents the response time score of node i, ResponseTimei represents the response time of node i, w1, w2, and w3 represent weight coefficients, and are adjusted according to requirements;

[0179] Specifically, after the scheduling strategy design is completed, according to the business requirements and resource situation, allocate tasks to the corresponding business nodes for execution, and perform dynamic migration and scheduling of tasks.

[0180] It should be noted that after the scheduling strategy design is completed, evaluate the importance and urgency of each task according to characteristics such as the priority, resource requirements, and running time of the task; monitor the resource usage of each business node in real time, including CPU, memory, and network bandwidth, to ensure the reasonable utilization of resources; dynamically adjust the task allocation according to the load conditions of the business nodes to avoid overloading of a certain node while other nodes are idle; for running tasks, perform dynamic migration according to the real-time resource situation to ensure that tasks are executed on the optimal nodes.

[0181] In the fourth step, as shown in Figure 6 , perform distributed database deployment and formulate an availability strategy, including deploying a distributed database, configuring an arbitration node, and designing the availability strategy of the database.

[0182] It should be noted that the research is based on the cross-domain multi-active management system to achieve cross-cloud platform resource scheduling technology, ensure the cross-domain continuity of the cloud platform management, and avoid business interruptions caused by single computer room failures. In accordance with the disaster recovery design requirements, the cloud adopts a multi-domain deployment method, deploys a multi-active management platform in the main data center and the dual data centers in the same city, and realizes the unified management and scheduling of cloud resources in the main and backup data centers.

[0183] Specifically, the deployment of a distributed database includes selecting a database system, preparing the environment, installing database software, configuring a database cluster, and initializing the database;

[0184] The configuration of arbitration nodes includes selecting arbitration nodes, installing arbitration services, and configuring arbitration rules;

[0185] Furthermore, the arbitration node is set as an independent node, and communication between the arbitration node and the data nodes is established to conduct elections in the event of a failure.

[0186] The design of database availability strategy includes data replication strategy, failover mechanism, load balancing, monitoring and alarm, and regular backup.

[0187] It should be noted that selecting a database system is to evaluate different distributed database systems (including Apache Cassandra, MongoDB, CockroachDB, and Google Spanner), and select the most suitable database based on project requirements (including scalability, fault recovery, performance, and data consistency); at the same time, ensure that the infrastructure meets the requirements of the database, including computing resources (CPU, memory), storage (SSD / HDD), and network bandwidth, select the operating system and configure the network to ensure efficient communication between nodes.

[0188] Among them, Apache Cassandra supports writing and reading large-scale data, and its data model is wide column storage. It is used to process large-scale unstructured data, and supports multi-data center deployment and efficient load balancing; MongoDB is a document-oriented NoSQL database that supports JSON-style data format, provides a flexible data model, is easy to store and query complex data, and has query functions and aggregation frameworks; CockroachDB is a PostgreSQL-compatible SQL database that supports ACID transactions, has automatic sharding and high availability, is suitable for global distributed deployment, and provides strong consistency and horizontal expansion capabilities; Google Spanner is Google's distributed database that supports data consistency, provides transaction support and SQL query capabilities, and automatically expands and load balances.

[0189] Configuring a database cluster involves configuring the cluster according to the database's documentation, including node information, data sharding, and replication strategies, and configuring load balancing, failover, and monitoring tools to ensure the high availability and performance of the system.

[0190] Initializing the database involves creating the database schema, tables, and indexes, importing initial data, and performing data migration if necessary; performing basic tests to ensure that the database functions properly and meets performance expectations.

[0191] Embodiment 3

[0192] This embodiment also provides a computer device applicable to a cross-domain PaaS cloud platform distributed architecture scheduling method, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement a forced oscillation detection and localization method for a distribution network as proposed in the above embodiment.

[0193] This embodiment also provides a storage medium with a computer program stored thereon, and when the program is executed by a processor, it implements a forced oscillation detection and localization method for a distribution network as proposed in the above embodiment.

[0194] The computer device can be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be achieved through Wi-Fi, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0195] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., all kinds of media that can store program codes.

[0196] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device.

[0197] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), fiber optic devices, and portable compact disc read-only memories (CDROMs). Additionally, a computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or processing it in other suitable ways as necessary, and then storing it in a computer memory.

[0198] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0199] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A distributed architecture scheduling method for a cross-domain PaaS cloud platform, characterized in that: Including, architectural design and deployment, including preparation of data centers, node deployment and preparation of business clusters; Perform load balancing configuration, including setting up a load balancer and health check. The load balancer setting is to deploy a load balancer and configure it to receive requests from users, configure a load balancing strategy for traffic distribution, and configure a health check mechanism for the load balancer to regularly check the status of each business node; Design scheduling strategies, including formulating resource scheduling strategies and setting task scheduling strategies, designing resource scheduling algorithms based on the current node load, resource utilization, and response time, and performing dynamic scheduling; Perform distributed database deployment and formulate availability strategies, including deploying distributed databases, configuring arbitration nodes, and designing database availability strategies.

2. The cross-domain PaaS cloud platform distributed architecture scheduling method according to claim 1, wherein: The determination of the location of the data center includes the physical location of the primary node, backup node and arbitration node, and is selected based on geographical location, disaster recovery capability and bandwidth connection. At the same time, the network architecture is designed, including internal network and external network connections, and the network is configured for bandwidth connection between different data centers, and firewall and security group rules are configured; The node deployment is to deploy cloud platform management nodes, control nodes and distributed storage management and control nodes in each data center, and to deploy infrastructure layer platform computing nodes and storage data nodes in the master node and the standby node. The business cluster preparation is to deploy platform as a service management and control nodes and business clusters in the master node and the standby node.

3. The cross-domain PaaS cloud platform distributed architecture scheduling method according to claim 2, characterized in that: The cloud platform management node deployment is to install and configure the cloud platform management software on the master node and the standby node, and configure the network interface and storage settings of the management node at the same time, wherein the cloud platform management software includes an open source cloud computing management platform; The control node deployment is to install the control node service on the master node and the standby node, and configure the communication between the control node and the management node; The distributed storage management and control node deployment is to deploy a distributed storage solution in each data center and configure the network and storage pool of the storage node. The distributed storage solution includes an open source distributed storage system; The infrastructure layer platform computing node deployment is to install computing node services on the master node and the standby node, and configure the network settings and resource management of the computing nodes; The storage data node deployment is to configure the storage data nodes in the distributed storage system.

4. The cross-domain PaaS cloud platform distributed architecture scheduling method according to claim 3, characterized in that: The platform as a service management and control node deployment is to install the platform as a service management and control platform on the master node and the standby node and configure the network and storage of the platform as a service management and control node, wherein the platform as a service management and control platform includes an open source container orchestration platform and an open source application platform; The business cluster deployment creates a business cluster environment on the master node and the standby node according to business needs, and configures service discovery to assist subsequent load balancing settings.

5. The cross-domain PaaS cloud platform distributed architecture scheduling method according to claim 4, wherein: The load balancing configuration includes: Select the type of load balancer according to your usage requirements. The types of load balancers include hardware load balancers, software load balancers, and cloud service provider load balancers. Deploy the selected load balancer, including cloud environments and on-premises servers; Configure the load balancer, including setting the listening port and specifying the backend service, and at the same time select the load balancing algorithm; Configure the health check mechanism. First, set the health check service, then specify the health check protocol, then configure the health check request path, and finally set the health check frequency and timeout warning.

6. The cross-domain PaaS cloud platform distributed architecture scheduling method according to claim 5, wherein: After the scheduling policy is designed, according to the business requirements and resource situation, allocate tasks to the corresponding business nodes for execution, and perform dynamic migration and scheduling of tasks.

7. The distributed architecture scheduling method of the cross-domain PaaS cloud platform according to claim 6, characterized in that: The deployment of the distributed database includes selecting the database system, preparing the environment, installing the database software, configuring the database cluster, and initializing the database; The configuration of the arbitration node includes selecting the arbitration node, installing the arbitration service, and configuring the arbitration rules; The content of the availability policy design of the database includes data replication policy, failover mechanism, load balancing, monitoring and alarm, and regular backup; The arbitration node is set as an independent node, and communication between the arbitration node and the data node is established for election in case of failure.

8. A distributed architecture scheduling system for a cross-domain PaaS cloud platform, based on the cross-domain PaaS cloud platform distributed architecture scheduling method according to any one of claims 1 to 7, characterized in that: It also includes an architecture design and deployment module for architecture design and deployment, including preparing the data center, node deployment, and preparing the business cluster; The load balancing configuration module is used to perform load balancing configuration, including setting the load balancer and health check. The setting of the load balancer is to deploy the load balancer, configure it to receive requests from users, configure the load balancing policy for traffic distribution, and at the same time configure the health check mechanism of the load balancer to regularly detect the status of each business node; The scheduling policy design module is used to perform scheduling policy design, including formulating the resource scheduling policy and setting the task scheduling policy, and designing the resource scheduling algorithm according to the load situation, resource utilization rate, and response time of the current node, and performing dynamic scheduling; The database deployment module is used to perform the deployment of the distributed database and formulate the availability policy, including deploying the distributed database, configuring the arbitration node, and designing the availability policy of the database.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the cross-domain PaaS cloud platform distributed architecture scheduling method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the cross-domain PaaS cloud platform distributed architecture scheduling method according to any one of claims 1 to 7.

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