WO2022078322A1 - 一种基于状态迁移的专有云重建及校验方法及装置 - Google Patents

一种基于状态迁移的专有云重建及校验方法及装置 Download PDF

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Publication number
WO2022078322A1
WO2022078322A1 PCT/CN2021/123254 CN2021123254W WO2022078322A1 WO 2022078322 A1 WO2022078322 A1 WO 2022078322A1 CN 2021123254 W CN2021123254 W CN 2021123254W WO 2022078322 A1 WO2022078322 A1 WO 2022078322A1
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cluster
information
reconstruction
components
platform
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PCT/CN2021/123254
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English (en)
French (fr)
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李文乔
王大泳
白石
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北京京东尚科信息技术有限公司
北京京东世纪贸易有限公司
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Priority to US18/247,243 priority Critical patent/US20230367651A1/en
Priority to KR1020237014536A priority patent/KR20230078762A/ko
Priority to JP2023519752A priority patent/JP2023544571A/ja
Publication of WO2022078322A1 publication Critical patent/WO2022078322A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061—Partitioning or combining of resources
    • G06F9/5077—Logical partitioning of resources; Management or configuration of virtualized resources
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F8/00—Arrangements for software engineering
    • G06F8/60—Software deployment
    • G06F8/61—Installation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061—Partitioning or combining of resources
    • G06F9/5072—Grid computing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0876—Aspects of the degree of configuration automation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • G06F2009/45562—Creating, deleting, cloning virtual machine instances
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • G06F2009/45591—Monitoring or debugging support
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0866—Checking the configuration
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/12—Discovery or management of network topologies

Definitions

  • the present disclosure relates to the field of computer technologies, and more particularly, to a method and apparatus for rebuilding and verifying a private cloud based on state migration.
  • Proprietary cloud generally adopts a layered management structure, and the bottom layer supports physical server clusters and supports various business systems of users.
  • Proprietary Cloud provides customers with a rich product line through the service catalog. The product line is divided into multiple applications, and the multiple applications are interconnected.
  • Container cluster technology can be used to organize and manage application service programs provided by various product lines. Container technology can realize the gradual migration of a business system based on the flexible migration capability of the container itself, but for complex and large container cluster environments, there is still a lack of systematic automatic reconstruction and verification methods.
  • Proprietary cloud also has problems in cluster reconstruction and stability.
  • the present disclosure provides an automatic reconstruction of an instance proprietary cloud platform that is identical or similar to a standard proprietary cloud platform by using the obtained comprehensive container cluster genetic information in the proprietary cloud container cluster environment. And calibration method and device.
  • One aspect of the present disclosure provides a state migration-based proprietary cloud reconstruction and verification method, including:
  • the cluster gene information of the standard proprietary cloud platform is obtained, and the underlying system components are reconstructed and the underlying dependent components are reconstructed according to the cluster genetic information and the state of the to-be-built proprietary cloud server cluster. refactoring and refactoring of the proprietary cloud platform product line;
  • the acquiring information of the underlying basic platform of the dedicated cloud server cluster to be built, and determining whether the underlying basic platform of the private cloud server cluster to be built meets the deployment requirements include:
  • the difference situation data it is determined whether the underlying basic platform of the private cloud server cluster to be built meets the deployment requirements, and if it meets the deployment requirements, it is marked as the initial state of the physical device.
  • the refactoring of the underlying system components includes:
  • cluster component information in the cluster gene information a cluster component is created on the dedicated cloud platform to be built.
  • the refactoring of the underlying dependent components includes:
  • the reconfiguration of the proprietary cloud platform product line includes:
  • the component refactoring includes:
  • the component is reconstructed according to the gene information corresponding to the component.
  • the obtaining user demand information, configuring cluster gene information according to the user demand information, and selecting product line components of a standard proprietary cloud platform for deployment include:
  • VPC cluster to be built is in a static service tree state, and select the standard VPC platform version to be deployed in the VPC cluster to be built based on user demand information;
  • the product components of the standard proprietary cloud platform corresponding to the cluster gene information to be configured are selected for deployment.
  • the verifying the correctness and validity of components on the reconstructed proprietary cloud platform includes:
  • the associated component and the dependent component are reconstructed until all components are correctly reconstructed.
  • a state migration-based dedicated cloud reconstruction and verification apparatus including:
  • the underlying basic platform information detection module is used to obtain the information of the underlying basic platform of the private cloud server cluster to be built, and determine whether the underlying basic platform of the private cloud server cluster to be built meets the deployment requirements;
  • the reconstruction module is used to obtain the cluster gene information of the standard proprietary cloud platform, and reconstruct the underlying system components, the underlying dependent components, and Reconstruction of cloud platform product line;
  • Selecting a deployment module for obtaining user demand information configuring cluster gene information according to the user demand information, selecting product line components of a standard proprietary cloud platform for deployment, and obtaining a reconstructed proprietary cloud platform;
  • the verification module is used to verify the correctness and validity of components on the reconstructed proprietary cloud platform.
  • an electronic device comprising:
  • processors one or more processors
  • the one or more processors are caused to execute the foregoing state migration-based proprietary cloud reconstruction and verification method.
  • a computer-readable storage medium having executable instructions stored thereon that, when executed by a processor, cause the processor to perform the state-migration-based private cloud reconstruction as described above and verification methods.
  • the problem of overall replication and reconstruction of a standard proprietary cloud platform container cluster can be at least partially solved, and therefore, the automation degree in the deployment process of the proprietary cloud platform can be improved, and the newly-built proprietary cloud can be effectively guaranteed
  • FIG. 1a schematically shows an exemplary system architecture to which a state migration-based private cloud reconstruction and verification method and apparatus can be applied according to an embodiment of the present disclosure
  • Fig. 1b schematically shows the implementation flow of the method and apparatus for rebuilding and verifying a private cloud based on state migration according to an embodiment of the present disclosure
  • 1c is a structural block diagram of hierarchical gene information of a proprietary cloud cluster system according to an embodiment of the disclosure
  • FIG. 2 schematically shows a flowchart of a state migration-based proprietary cloud reconstruction and verification method according to an embodiment of the present disclosure
  • FIG. 3 schematically shows a flowchart of reconstruction according to the cluster gene information according to an embodiment of the present disclosure
  • FIG. 4 schematically shows a schematic diagram of a proprietary cloud product line according to an embodiment of the present disclosure
  • FIG. 5 schematically shows a flowchart of a method for deploying a product component according to an embodiment of the present disclosure
  • FIG. 6 schematically shows a flowchart of a method for verifying a rebuilt proprietary cloud platform according to an embodiment of the present disclosure
  • FIG. 7 schematically shows a block diagram of a state migration-based dedicated cloud reconstruction and verification apparatus according to an embodiment of the present disclosure
  • FIG. 8 schematically shows a block diagram of an electronic device according to an embodiment of the present disclosure.
  • At least one of the “systems” shall include, but not be limited to, systems with A alone, B alone, C alone, A and B, A and C, B and C, and/or A, B, C, etc. ).
  • Embodiments of the present disclosure provide a state migration-based proprietary cloud reconstruction and verification method, including a state migration-based proprietary cloud reconstruction and verification method, including: obtaining the bottom layer of a to-be-built proprietary cloud server cluster The information of the basic platform, to determine whether the underlying basic platform of the private cloud server cluster to be built meets the deployment requirements; if the underlying basic platform meets the deployment requirements, obtain the cluster gene information of the standard private cloud platform, and according to the Reconstruction of underlying system components, reconstruction of underlying dependent components, and reconstruction of the proprietary cloud platform product line based on cluster gene information and the state of the to-be-built proprietary cloud server cluster; Configure the cluster gene information, select product line components of the standard proprietary cloud platform for deployment, and obtain a reconstructed proprietary cloud platform; and verify the correctness and validity of components on the reconstructed proprietary cloud platform.
  • the proprietary cloud cluster needs to uniformly package all the containers in the cluster each time it is sealed, much information in the cluster is solidified.
  • the operation and maintenance personnel deploy the same version of the proprietary cloud platform in different environments, they often encounter various problems during deployment due to differences in the environment.
  • the operation and maintenance personnel often need to understand and master the curing information and Only the modification method can solve the problems encountered in the deployment, which greatly increases the deployment cost of the proprietary cloud.
  • the existing technology mainly relies on the automatic orchestration function of K8S and the creation of clusters according to the operation of configuration files and scripts, and does not achieve complete systemization and automation.
  • images or programs are packaged and then redeployed, components are relatively separated, and complex mutual associations and dependencies between components are not considered, which is prone to errors.
  • FIG. 1a schematically shows an exemplary system architecture to which a state migration-based private cloud reconstruction and verification method and apparatus can be applied according to an embodiment of the present disclosure. It should be noted that FIG. 1a is only an example of a system architecture to which the embodiments of the present disclosure can be applied, so as to help those skilled in the art to understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used for other A device, system, environment or scene.
  • the system architecture 100 may include a cluster gene information database 101 , a to-be-built dedicated cloud server cluster 102 and a network 103 .
  • the network 103 is used as a medium for providing a communication link between the cluster gene information database 101 and the dedicated cloud server cluster 102 to be built.
  • the network 103 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
  • the cluster gene information database 101 stores the cluster gene information of the standard proprietary cloud platform.
  • the standard proprietary cloud platform refers to a long-term research and development and quality verification to ensure functions, performance, stability, scalability, security, etc.
  • Cluster genes refer to structured information that can fully represent key information such as the number, configuration, composition, data, association, and dependency of each component in the cluster, through which the automatic creation and component deployment of the same or similar clusters can be realized.
  • the cluster genetic information database 101 may be a data server associated with the private cloud server cluster 102 to be built, or, in other embodiments, the cluster genetic information database 101 may also be configured as a server in the private cloud server cluster 102 to be built .
  • the cluster gene information database 101 and the private cloud server cluster 102 to be built are connected through the network 103, and the private cloud server cluster 102 to be built can obtain the data information of the cluster gene information database 101 through the network 103, so as to realize the private cloud server to be built
  • a proprietary cloud platform is deployed on the cluster 102 .
  • the state migration-based proprietary cloud reconstruction and verification system is based on K8S technology, and based on the characteristics of containers in a K8S cluster, it can effectively improve the automatic processing degree of the cluster gene information copied to the new proprietary cloud platform. .
  • the state migration-based proprietary cloud reconstruction and verification method provided by the embodiments of the present disclosure may generally be executed by the to-be-built proprietary cloud server cluster 102 .
  • the state migration-based proprietary cloud reconstruction and verification apparatus provided by the embodiments of the present disclosure may generally be set in the to-be-built proprietary cloud server cluster 102 .
  • the private cloud reconstruction and verification method based on state migration provided by the embodiments of the present disclosure may also be executed by a server or server cluster that is different from the private cloud server cluster 102 to be built and can communicate with the private cloud server cluster 102 to be built .
  • the state migration-based proprietary cloud reconstruction and verification apparatus may also be set on a server that is different from the to-be-built private cloud server cluster 102 and can communicate with the to-be-built private cloud server cluster 102 . or in a server cluster.
  • Fig. 1b schematically shows the implementation flow of the method and apparatus for rebuilding and verifying a dedicated cloud based on state migration according to an embodiment of the present disclosure.
  • the reconstruction of the standard proprietary cloud platform is implemented in the to-be-built proprietary cloud server cluster 102 .
  • the cluster gene database 101 stores the cluster gene information comprehensively collected and collected from the standard proprietary cloud platform.
  • the cluster gene can contain all the key information of the container cluster and various containers with business applications contained in it, and these key information can be used to implement the same or similar containers in other server clusters. Reconstruction of cluster and service applications.
  • the cluster gene information includes three dimensions of information: the first dimension is the 8-level information that forms the proprietary cloud cluster, that is, the system layered gene information, which realizes the discrete and full-coverage set of module components. Layer gene information realizes the composition analysis and information collection of modules and components; the second dimension is the set of association relationships between modules and components in the cluster's product line, that is, the associated gene information; the third dimension is each module in the cluster's product line And the set of dependencies of the components, that is, the dependency gene information.
  • the first dimension is the 8-level information that forms the proprietary cloud cluster, that is, the system layered gene information, which realizes the discrete and full-coverage set of module components. Layer gene information realizes the composition analysis and information collection of modules and components
  • the second dimension is the set of association relationships between modules and components in the cluster's product line, that is, the associated gene information
  • the third dimension is each module in the cluster's product line
  • the set of dependencies of the components that is, the dependency gene information.
  • IaaS is a cloud platform service layer that includes basic cloud resources such as virtualized computing resources, virtualized network resources, and virtualized storage resources
  • PaaS is a cloud platform that provides platform layer cloud resources such as databases, middleware, development components, and big data components.
  • Platform service layer is a cloud platform service layer that provides software resources that support specific businesses.
  • the state-migration-based proprietary cloud reconstruction and verification system of the embodiment of the present disclosure solves the technical problem of error-prone technical problems in the manual deployment of the proprietary cloud, and improves the accuracy and efficiency of the overall deployment of the automated cluster; it can effectively Improve the efficient replication of the proprietary cloud platform cluster based on cluster genetic information, realize the automatic deployment of a truly comprehensive and systematic proprietary cloud platform, and ensure that the newly built proprietary cloud platform and the standard proprietary cloud platform maintain the same stability, component availability and reliability. system performance.
  • FIG. 1c is a structural block diagram of hierarchical gene information of a proprietary cloud cluster system according to an embodiment of the disclosure.
  • the proprietary cloud cluster system is divided into 8 cluster gene information levels from bottom to top.
  • the first to third layers are the basic system genetic information.
  • the first layer of cluster genetic information is the genetic information of the physical server cluster that is automatically scanned through the out-of-band management system and the in-band management system, and the collected information, such as the number of servers, server specifications, server configuration, server type (management nodes, computing nodes, storage nodes, etc.);
  • the second layer of cluster genetic information is the operation and maintenance management system through the cloud platform, which automatically scans the operating system, network topology and other information of the service server;
  • the third layer of cluster genetic information is the operation and maintenance through the cloud platform.
  • the fourth layer is based on genetic information, including through the cloud platform operation and maintenance management system, scanning and collecting the information of the components that each product line depends on on the proprietary cloud platform, such as: log system, monitoring system, security protection system, DNS service, etc. .
  • Layers 5 to 7 are static service tree gene information for product lines.
  • the layer 5 cluster gene information is to scan the IaaS product component information on the proprietary cloud platform through the proprietary cloud management platform and the Kubernetes cluster management module, including K8S services, Pods, APIs, containers, applications, Grouping, container images, etc.
  • the layer 6 cluster gene information is to scan the PaaS product component information on the proprietary cloud platform through the proprietary cloud management platform and the Kubernetes cluster management module, including virtual machines, K8S services, Pods, APIs, containers, applications, groups, container images, etc.
  • the layer 7 cluster genetic information is to scan the SaaS product component information on the proprietary cloud platform through the proprietary cloud management platform and Kubernetes cluster management module, including the virtual machines supporting the product components , virtual storage, virtual network, PaaS service, K8S service, Pod, API, container, application, group, container image, etc.
  • the eighth layer of cluster gene information is to verify the consistency and integrity of all the collected information, and generate corresponding verification information and version information.
  • the version information is determined based on the automatic version generation rule, for example, the version number of the minor version is automatically incremented, etc.; or the version number of the gene information of each module component of the cluster is generated based on the manual determination of the version number.
  • the gene information of the container cluster in the proprietary cloud cluster gene information set is used for the reconstruction of the new proprietary cloud platform, thereby ensuring new The proprietary cloud platform is consistent with the existing standard proprietary cloud platform in structure and function, ensuring the technical standard consistency of the proprietary cloud platform.
  • FIG. 2 schematically shows a flowchart of a method for rebuilding and verifying a private cloud based on state migration according to an embodiment of the present disclosure.
  • the method includes operations S210-S240.
  • operation S210 the information of the underlying basic platform of the private cloud server cluster to be built is obtained, and it is determined whether the underlying basic platform of the private cloud server cluster to be built meets the deployment requirements;
  • the out-of-band management system and the in-band management system automatically scan the underlying basic genetic information of the dedicated cloud server cluster to be built, such as the number of servers, server specifications, server configurations, server types (management nodes, computing nodes, storage nodes, etc.). Obtain the underlying basic genetic information of the to-be-built proprietary cloud server cluster scanned by the out-of-band management system and the in-band management system, and then classify the underlying basic genetic information and compare it with the underlying basic genetic information of the standard proprietary cloud platform. By comparative analysis, difference data of the underlying platform is obtained, and according to the difference data, it is determined whether the underlying basic platform of the dedicated cloud server cluster to be built meets the deployment requirements. When the differences of the basic platform meet the technical requirements, the subsequent system reconstruction process can be carried out.
  • the underlying basic genetic information of the dedicated cloud server cluster to be built such as the number of servers, server specifications, server configurations, server types (management nodes, computing nodes, storage nodes, etc.).
  • a difference information report may also be formed according to the difference situation data. Send the report to the operation and maintenance personnel, so that the operation and maintenance personnel can understand the difference between the new platform and the standard proprietary cloud platform in terms of basic hardware facilities. If there is an important problem affecting the deployment of the proprietary cloud platform, it needs to be solved manually.
  • the to-be-built private cloud server cluster When determining whether the underlying basic platform of the to-be-built private cloud server cluster meets the deployment requirements, mark the to-be-built private cloud server cluster as the initial state of the physical device, and the initial state of the physical device indicates that all physical servers in the new computer room The initial cluster state after all are on the shelf, powered on, and connected to network devices.
  • the cluster gene information of the standard proprietary cloud platform is obtained through the cluster gene database.
  • the cluster gene information includes basic system gene information, basic dependent gene information and product line static service tree gene information. Then, the underlying system components, underlying dependent components and proprietary cloud platform product lines are reconstructed according to the cluster gene information.
  • FIG. 3 schematically shows a flowchart of reconstruction according to the cluster gene information according to an embodiment of the present disclosure.
  • the operation S220 includes operations S221 to S223.
  • operation S221 it is confirmed that the dedicated cloud server cluster to be built is in the initial state of the physical device, and the underlying system components are reconstructed by using the basic system gene information of the cluster gene database.
  • the operation S221 includes operations S2211-S2213.
  • cluster components such as Kubernetes, zookeeper and other cluster components, are created on the private cloud platform to be built.
  • the deployment and construction of the basic system environment is completed.
  • the to-be-built private cloud server cluster is marked as the basic system environment state, and the basic system environment state represents the basic state after operating system installation, network configuration and basic component installation are performed on all server nodes in the initial state of the physical device.
  • the dedicated cloud server cluster to be built is in the basic system environment state, and the underlying dependent components are reconstructed by using the basic dependent gene information of the cluster gene database.
  • the cloud platform operation and maintenance management system based on the genetic information of the components that each product line depends on on the standard proprietary cloud platform, such as: log system, monitoring system, security protection system, DNS service, etc., in each cluster component
  • the underlying dependent components of each product line are deployed on the node.
  • the to-be-built proprietary cloud server cluster is marked as the basic dependent environment state, and the basic dependent environment state represents the state after installing and deploying the basic dependent software required by the proprietary cloud platform cluster in the basic system environment state.
  • the proprietary cloud server cluster to be built is in a basic dependent environment state, and the proprietary cloud product line is reconstructed by using the product line static service tree gene information in the cluster gene database.
  • the product line static service tree gene includes the gene information of IaaS product components, the gene information of PaaS product components and the gene information of SaaS product components.
  • FIG. 4 schematically shows a schematic diagram of a proprietary cloud product line according to an embodiment of the present disclosure.
  • the proprietary cloud platform includes IaaS product line, PaaS product line and SaaS product line, each product line includes multiple products, and each product is divided into multiple application groups, where application groups can be mirrored Package or package build.
  • application groups can be mirrored Package or package build.
  • the operation S223 includes operations S2231 to S2232.
  • an application group is first created according to the arrangement gene information, and then the access address and component version of the container image or package are determined according to the group information, and then device allocation and service are performed in sequence according to the arrangement order of the application. Deploy and register application services to realize external services of ApsaraDB for MySQL products.
  • operation S230 obtain user demand information, configure cluster gene information according to the user demand information, select product line components of a standard proprietary cloud platform for deployment, and obtain a reconstructed proprietary cloud platform.
  • the VPC cluster to be built is marked as the static service tree state, and the static service tree state represents the cluster state in which the image packages and program packages of each product line are uploaded to the system based on the basic dependent environment state.
  • FIG. 5 schematically shows a flowchart of a method for deploying a product component according to an embodiment of the present disclosure.
  • the method includes operations S231 to S233.
  • the cluster gene information to be configured is adjusted based on the function and technical index requirements of the newly built proprietary cloud platform.
  • the configured cluster gene information needs to meet the technical specifications. Therefore, in operation S234, the system automatically checks the compliance of the specification to prevent system failure caused by configuration modification. If the specification compliance check is passed, the product components of the standard proprietary cloud platform corresponding to the cluster gene information to be configured are selected for deployment.
  • the reconstructed proprietary cloud platform is obtained, and then in operation S240, the correctness and validity of components are checked on the reconstructed proprietary cloud platform.
  • FIG. 6 schematically shows a flowchart of a method for verifying a rebuilt proprietary cloud platform according to an embodiment of the present disclosure.
  • the method includes operations S241-S242.
  • the relevant components, their associated components and dependent components are checked. If the associated components and dependent components have a creation error, they are re-created, and then the faulty component is re-created. , until all components are created correctly.
  • the VPC cluster to be built is marked as the cluster running state.
  • the cluster running state indicates that all application services are started and run normally after device allocation and arrangement are performed for each application group based on the association and dependencies of product lines. state.
  • the embodiment of the present disclosure realizes the reconstruction of the proprietary cloud platform based on the container cluster genes of the system layering dimension, the association relationship dimension, and the dependency relationship dimension, and can more accurately copy the whole picture of the standard container cluster to the newly-built proprietary cloud platform.
  • the reconstruction and verification technology of the proprietary cloud platform based on the cluster gene information of the proprietary cloud container cluster can greatly improve the degree of automation in the deployment process of the proprietary cloud platform, and can also effectively ensure the stability of the newly built proprietary cloud platform and the The standard proprietary cloud platform maintains the consistency of technical indicators.
  • FIG. 7 schematically shows a block diagram of a state migration-based dedicated cloud reconstruction and verification apparatus according to an embodiment of the present disclosure.
  • the state migration-based proprietary cloud reconstruction and verification apparatus 700 includes an underlying basic platform information detection module 710 , a reconstruction module 720 , a selection and deployment module 730 and a verification module 740 .
  • the underlying basic platform information detection module 710 is used to obtain the information of the underlying basic platform of the private cloud server cluster to be built, and determine whether the underlying basic platform of the private cloud server cluster to be built meets the deployment requirements.
  • the reconstruction module 720 is used to obtain the cluster gene information of the standard proprietary cloud platform, and perform reconstruction of the underlying system components, reconstruction of the underlying dependent components, and specialization according to the cluster genetic information and the state of the dedicated cloud server cluster to be built. Reconstruction of cloud platform product line;
  • the selection and deployment module 730 is configured to obtain user demand information, configure cluster gene information according to the user demand information, select product line components of a standard proprietary cloud platform for deployment, and obtain a reconstructed proprietary cloud platform.
  • the verification module 740 is used to verify the correctness and validity of components on the reconstructed proprietary cloud platform.
  • any of the modules, units, or at least part of the functions of any of the modules according to the embodiments of the present disclosure may be implemented in one module. Any one or more of the modules and units according to the embodiments of the present disclosure may be divided into multiple modules for implementation. Any one or more of the modules and units according to embodiments of the present disclosure may be implemented at least partially as hardware circuits, such as field programmable gate arrays (FPGA), programmable logic arrays (PLA), system-on-chip, on-board A system, a system-on-package, an application specific integrated circuit (ASIC), or any other reasonable hardware or firmware implementation that can integrate or package a circuit, or in any one of software, hardware, and firmware implementations or any appropriate combination of any of them. Alternatively, one or more of the modules and units according to the embodiments of the present disclosure may be implemented at least in part as computer program modules, which, when executed, may perform corresponding functions.
  • FPGA field programmable gate arrays
  • PLA programmable logic arrays
  • ASIC application specific integrated circuit
  • any of the underlying basic platform information detection module 710, the reconstruction module 720, the selection and deployment module 730, and the verification module 740 may be combined into one module for implementation, or any one of the modules may be split into multiple modules. module. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in one module.
  • At least one of the underlying base platform information detection module 710, the reconstruction module 720, the selection and deployment module 730, and the verification module 740 may be implemented at least partially as a hardware circuit, such as a field programmable gate array ( FPGA), Programmable Logic Array (PLA), System-on-Chip, System-on-Substrate, System-on-Package, Application-Specific Integrated Circuit (ASIC), or any other reasonable means by which circuits can be integrated or packaged such as hardware or firmware It can be realized by any one of the three implementation manners of software, hardware and firmware, or by any suitable combination of any of them.
  • at least one of the underlying base platform information detection module 710, the reconstruction module 720, the selection and deployment module 730, and the verification module 740 may be at least partially implemented as a computer program module that, when executed, can execute corresponding function.
  • FIG. 8 schematically shows a block diagram of an electronic device according to an embodiment of the present disclosure.
  • the electronic device shown in FIG. 8 is only an example, and should not impose any limitation on the function and scope of use of the embodiments of the present disclosure.
  • the electronic device 800 includes a processor 810 and a computer-readable storage medium 820 .
  • the electronic device 800 may execute the method according to the embodiment of the present disclosure.
  • the processor 810 may include, for example, a general-purpose microprocessor, an instruction set processor and/or a related chipset, and/or a special-purpose microprocessor (eg, an application specific integrated circuit (ASIC)), and the like.
  • the processor 810 may also include onboard memory for caching purposes.
  • the processor 810 may be a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.
  • the computer-readable storage medium 820 can be, for example, a non-volatile computer-readable storage medium, and specific examples include but are not limited to: magnetic storage devices, such as magnetic tapes or hard disks (HDD); optical storage devices, such as compact disks (CD-ROMs) ; memory, such as random access memory (RAM) or flash memory; etc.
  • magnetic storage devices such as magnetic tapes or hard disks (HDD)
  • optical storage devices such as compact disks (CD-ROMs)
  • CD-ROMs compact disks
  • memory such as random access memory (RAM) or flash memory; etc.
  • the computer-readable storage medium 820 may include a computer program 821, which may include code/computer-executable instructions that, when executed by the processor 810, cause the processor 810 to perform methods according to embodiments of the present disclosure or any variation thereof.
  • the computer program 821 may be configured with computer program code comprising, for example, computer program modules.
  • the code in computer program 821 may include one or more program modules including, for example, 821A, module 821B, . . .
  • the division method and number of modules are not fixed, and those skilled in the art can use appropriate program modules or combination of program modules according to the actual situation.
  • the processor 810 can A method according to an embodiment of the present disclosure or any variation thereof is performed.
  • At least one of the underlying base platform information detection module 710, the reconstruction module 720, the selection and deployment module 730, and the verification module 740 may be implemented as a computer program module described with reference to FIG. 8, which is executed by the processor When 810 is executed, the corresponding operations described above may be implemented.
  • the present disclosure also provides a computer-readable storage medium.
  • the computer-readable storage medium may be included in the device/apparatus/system described in the above embodiments; it may also exist alone without being assembled into the device/system. device/system.
  • the above-mentioned computer-readable storage medium carries one or more programs, and when the above-mentioned one or more programs are executed, implement the method according to the embodiment of the present disclosure.
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more logical functions for implementing the specified functions executable instructions.
  • the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

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Abstract

本公开提供了一种基于状态迁移的专有云重建及校验方法，包括：获取待建专有云服务器集群的底层基础平台的信息，确定待建专有云服务器集群的底层基础平台是否符合部署要求；若底层基础平台符合部署要求，则获取标准专有云平台的集群基因信息，并根据集群基因信息及待建专有云服务器集群的状态进行底层系统组件的重构、底层依赖组件的重构和专有云平台产品线的重构；获取用户需求信息，并根据用户需求信息配置集群基因信息，选取标准专有云平台的产品线组件进行部署，获得重建的专有云平台；以及对重建的专有云平台进行组件正确性和有效性的校验。

Description

一种基于状态迁移的专有云重建及校验方法及装置
本申请要求于2020年10月13日递交的中国专利申请No.202011093747.8的优先权，其内容一并在此作为参考。
技术领域
本公开涉及计算机技术领域，更具体地，涉及一种基于状态迁移的专有云重建及校验方法及装置。
背景技术
专有云一般采用分层管理架构，底层支持物理服务器集群，支撑用户各种业务系统。专有云通过服务目录为客户提供丰富的产品线，产品线下分为多个应用，多个应用之间的互相联系。可采用容器集群技术来组织和管理各个产品线提供的应用服务程序。容器技术能够基于容器本身的灵活迁移能力，实现针对一个业务系统的逐步迁移，但是对于复杂、庞大容器集群环境，还缺乏系统性的自动重建和校验方法。专有云还存在集群重建、稳定性等方面的问题。
发明内容
有鉴于此，本公开提供了一种在专有云容器集群环境下，利用已经获取的全面容器集群基因信息复制和构建出与标准专有云平台相同或相似的实例专有云平台的自动重建及校验方法及装置。
本公开的一个方面提供了一种基于状态迁移的专有云重建及校验方法，包括：
获取待建专有云服务器集群的底层基础平台的信息，确定所述待建专有云服务器集群的底层基础平台是否符合部署要求；
若所述底层基础平台符合部署要求，则获取标准专有云平台的集群基因信息，并根据所述集群基因信息及待建专有云服务器集群的状态进行底层系统组件的重构、底层依赖组件的重构和专有云平台产品线的重构；
获取用户需求信息，并根据所述用户需求信息配置集群基因信息，选取标准专有云平台的产品线组件进行部署，获得重建的专有云平台；以及
对所述重建的专有云平台进行组件正确性和有效性的校验。
根据本公开的实施例，所述获取待建专有云服务器集群的底层基础平台的信息，确定所述待建专有云服务器集群的底层基础平台是否符合部署要求包括：
获取带外管理系统和带内管理系统扫描得到的待建专有云服务器集群的底层基础基因信息；
将所述底层基础基因信息进行与标准专有云平台的底层基础基因信息进行对比分析，获得差异情况数据；以及
根据所述差异情况数据确定所述待建专有云服务器集群的底层基础平台是否符合部署要求，若符合部署要求则标记为物理设备初始状态。
根据本公开的实施例，所述底层系统组件的重构包括：
确认待建专有云服务器集群处于物理设备初始状态，根据所述集群基因信息中标准专有云的操作系统分步信息，对全部服务服务器的操作系统进行安装；
根据所述集群基因信息中标准专有云的网络拓扑基因信息，对网络组件进行配置；以及
所述集群基因信息中的集群组件信息，在待建专有云平台上创建集群组件。
根据本公开的实施例，所述底层依赖组件的重构包括：
确认待建专有云服务器集群处于基础系统环境状态，根据所述集群基因信息中的基础依赖基因信息，在所述集群组件的各个节点上部署各产品线的底层依赖组件。
根据本公开的实施例，所述专有云平台产品线的重构包括：
确认待建专有云服务器集群处于基础依赖环境状态，对IaaS产品组件、PaaS产品组件和SaaS产品组件进行产品的组件重构，并在所述产品组件重构完成后标记为静态服务树状态。
根据本公开的实施例，所述组件重构包括：
确定所述组件所关联的资源和依赖的组件是否已创建，以及
若确定所述组件所关联的资源和依赖的组件已创建，则根据所述组件对应的基因信息重构该组件。
根据本公开的实施例，所述获取用户需求信息，并根据所述用户需求信息配置集群基因信息，选取标准专有云平台的产品线组件进行部署包括：
确认待建专有云服务器集群处于静态服务树状态，基于用户需求信息，选取部署在待建专有云服务器集群的标准专有云平台版本；
获取所述标准专有云平台版本对应的集群基因信息，作为待配置的集群基因信息；
基于待建专有云服务器集群的功能和技术指标需求，对待配置的集群基因信息进行调整，并对待配置的集群基因信息的调整过程的规范符合性进行检查；以及
若规范符合性检查通过，则选取待配置的集群基因信息的对应的标准专有云平台的产品组件进行部署。
根据本公开的实施例，所述对所述重建的专有云平台进行组件正确性和有效性的校验包括：
针对重建的专有云平台，依次在系统分层维度、关联关系维度和依赖关系维度三个维度进行组件重构的正确性和有效性的校验；
若校验确定存在组件重构错误，则对该组件及该组件的关联组件和依赖组件进行检查；以及
若关联组件和依赖组件存在重构错误，则重构所述关联组件和依赖组件，直到全部组件正确重构。
根据本公开的另一个方面，提供了一种基于状态迁移的专有云重建及校验装置，包括：
底层基础平台信息检测模块，用于获取待建专有云服务器集群的底层基础平台的信息，确定所述待建专有云服务器集群的底层基础平台是否符合部署要求；
重构模块，用于获取标准专有云平台的集群基因信息，并根据所述集群基因信息及待建专有云服务器集群的状态进行底层系统组件的重构、底层依赖组件的重构和专有云平台产品线的重构；
选择部署模块，用于获取用户需求信息，并根据所述用户需求信息配置集群基因信息，选取标准专有云平台的产品线组件进行部署，获得重建的专有云平台；以及
校验模块，用于对所述重建的专有云平台进行组件正确性和有效性的校验。
根据本公开的另一个方面，提供了一种电子设备，包括：
一个或多个处理器；
存储装置，用于存储一个或多个程序，
其中，当所述一个或多个程序被所述一个或多个处理器执行时，使得所述一个或多个处理器执行如前所述的基于状态迁移的专有云重建及校验方法。
根据本公开的另一个方面，提供了一种计算机可读存储介质，其上存储有可执行指令，该指令被处理器执行时使处理器执行如前所述的基于状态迁移的专有云重建及校验方法。
根据本公开的实施例，可以至少部分地解决对标准专有云平台容器集群的整体复制和重建问题，并因此可以实现提升专有云平台部署过程中的自动化程度，有效保障新建的专有云平台与稳定的标准专有云平台保持技术指标的一致性的技术效果。
附图说明
通过以下参照附图对本公开实施例的描述，本公开的上述以及其他目的、特征和优点将更为清楚，在附图中：
图1a示意性示出了根据本公开实施例的可以应用基于状态迁移的专有云重建及校验方法及装置的示例性系统架构；
图1b示意性示出了根据本公开实施例的基于状态迁移的专有云重建及校验方法及装置的实现流程；
图1c为本公开实施例专有云集群系统分层基因信息的结构框图；
图2示意性示出了根据本公开的实施例的基于状态迁移的专有云重建及校验方法的流程图；
图3示意性示出了根据本公开的实施例的根据所述集群基因信息进行重构的流程图；
图4示意性的示出了根据本公开的实施例专有云产品线的示意图；
图5示意性示出了根据本公开的实施例的对产品组件进行部署方法的流程图；
图6示意性示出了根据本公开的实施例对重建的专有云平台进行校验的方法流程图；
图7示意性示出了根据本公开的实施例的基于状态迁移的专有云重建及校验装置的框图；
图8示意性示出了根据本公开实施例的电子设备的框图。
具体实施方式
以下，将参照附图来描述本公开的实施例。但是应该理解，这些描述只是示例性的，而并非要限制本公开的范围。在下面的详细描述中，为便于解释，阐述了许多具体的细节以提供对本公开实施例的全面理解。然而，明显地，一个或多个实施例在没有这些具体细节的情况下也可以被实施。此外，在以下说明中，省略了对公知结构和技术的描述，以避免不必要地混淆本公开的概念。
在此使用的术语仅仅是为了描述具体实施例，而并非意在限制本公开。在此使用的术语“包括”、“包含”等表明了所述特征、步骤、操作和/或部件的存在，但是并不排除存在或添加一个或多个其他特征、步骤、操作或部件。
在此使用的所有术语(包括技术和科学术语)具有本领域技术人员通常所理解的含义，除非另外定义。应注意，这里使用的术语应解释为具有与本说明书的上下文相一致的含义，而不应以理想化或过于刻板的方式来解释。
在使用类似于“A、B和C等中至少一个”这样的表述的情况下，一般来说应该按照本领域技术人员通常理解该表述的含义来予以解释(例如，“具有A、B和C中至少一个的系统”应包括但不限于单独具有A、单独具有B、单独具有C、具有A和B、具有A和C、具有B和C、和/或具有A、B、C的系统等)。
本公开的实施例提供了一种基于状态迁移的专有云重建及校验方法，包括一种基于状态迁移的专有云重建及校验方法，包括：获取待建专有云服务器集群的底层基础平台的信息，确定所述待建专有云服务器集群的底层基础平台是否符合部署要求；若所述底层基础平台符合部署要求，则获取标准专有云平台的集群基因信息，并根据所述集群基因信息及待建专有 云服务器集群的状态进行底层系统组件的重构、底层依赖组件的重构和专有云平台产品线的重构；获取用户需求信息，并根据所述用户需求信息配置所述集群基因信息，选取标准专有云平台的产品线组件进行部署，获得重建的专有云平台；以及对所述重建的专有云平台进行组件正确性和有效性的校验。
由于专有云集群在每次封版时需要对集群中的所有容器进行统一打包，集群中的许多信息因此固化。运维人员在不同环境部署相同版本的专有云平台时，往往会因为环境的差异从而在部署时遇到各种问题，运维人员往往需要了解、掌握封版时多种产品的固化信息及修改方式才能解决部署中遇到的问题，极大增加了专有云的部署成本。同时，现有技术主要依赖K8S的自动编排功能和根据配置文件和脚本的运行实现集群创建，没有实现完整的体系化和自动化。此外，现有技术将镜像或程序进行打包后重新部署，组件相对割离，没有考虑组件间较复杂的相互的关联和依赖关系，较易出错。
为了解决上述问题，本公开实施例提供了一种基于状态迁移的专有云重建及校验方法及装置。图1a示意性示出了根据本公开实施例的可以应用基于状态迁移的专有云重建及校验方法及装置的示例性系统架构。需要注意的是，图1a所示仅为可以应用本公开实施例的系统架构的示例，以帮助本领域技术人员理解本公开的技术内容，但并不意味着本公开实施例不可以用于其他设备、系统、环境或场景。
如图1a所示，根据该实施例的系统架构100可以包括集群基因信息数据库101、待建专有云服务器集群102和网络103。网络103用以在集群基因信息数据库101、待建专有云服务器集群102之间提供通信链路的介质。网络103可以包括各种连接类型，例如有线、无线通信链路或者光纤电缆等等。
其中，集群基因信息数据库101存储有标准专有云平台的集群基因信息，标准专有云平台是指经过长时间的研发和质量验证，确保功能、性能、稳定性、可扩展性、安全性等各项技术指标能够达到技术标准要求的专有云平台环境。集群基因是指能够完整表示集群中各个组件数量、配置、组成、数据、关联关系、依赖关系等关键信息的结构化信息，通过它能够实 现相同或相似集群的自动化创建和组件部署。集群基因信息数据库101可以为与待建专有云服务器集群102关联的数据服务器，或者，在其他实施例中，集群基因信息数据库101还可以被配置为待建专有云服务器集群102中的服务器。
集群基因信息数据库101、待建专有云服务器集群102通过网络103相连，待建专有云服务器集群102可以通过网络103获取集群基因信息数据库101的数据信息，从而实现在待建专有云服务器集群102上部署专有云平台。示例性的，所述基于状态迁移的专有云重建及校验系统基于K8S技术，基于K8S集群中容器的特点，能够有效提升集群基因信息在复制到新的专有云平台上的自动化处理程度。
需要说明的是，本公开实施例所提供的基于状态迁移的专有云重建及校验方法一般可以由待建专有云服务器集群102执行。相应地，本公开实施例所提供的基于状态迁移的专有云重建及校验装置一般可以设置于待建专有云服务器集群102中。本公开实施例所提供的基于状态迁移的专有云重建及校验方法也可以由不同于待建专有云服务器集群102且能够与待建专有云服务器集群102通信的服务器或服务器集群执行。相应地，本公开实施例所提供的基于状态迁移的专有云重建及校验装置也可以设置于不同于待建专有云服务器集群102且能够与待建专有云服务器集群102通信的服务器或服务器集群中。
应该理解，图1中的数据库、服务器和网络的数目仅仅是示意性的。根据实现需要，可以具有任意数目的服务器和网络。
图1b示意性示出了根据本公开实施例的基于状态迁移的专有云重建及校验方法及装置的实现流程。根据本公开的实施例，基于集群基因数据库101存储的标准专有云平台的集群基因信息，在待建专有云服务器集群102实现标准专有云平台的重建。
本实施例中，基于集群基因数据库101存储有从标准专有云平台全面采集和收集的集群基因信息。在容器集群中，基于容器的组织特点，集群基因能够包含容器集群和其中包含的具有业务应用的各种容器的全部关键信息，并基因这些关键信息能够在其他的服务器集群中实现相同或相似容器集群及服务应用的重建。
其中，集群基因信息包括三个维度的信息：第一个维度是形成专有云集群的8个层次信息，即系统分层基因信息，实现离散化、全覆盖的模块组件集合，通过该系统分层基因信息实现模块、组件的组成分析和信息收集；第二个维度是在集群的产品线各模块和组件的关联关系集合，即关联基因信息；第三个维度是在集群的产品线各个模块和组件的依赖关系集合，即依赖关系基因信息。
基于以上三个维度，形成完整的专有云集群基因信息集合。利用此专有云集群基因信息集合，在待建专有云服务器集群102构建IaaS、PaaS、SaaS平台以及容器集群，实现标准专有云平台的重建。其中，IaaS为包含虚拟化计算资源、虚拟化网络资源、虚拟化存储资源等基础云资源的云平台服务层；PaaS为提供数据库、中间件、开发组件、大数据组件等平台层云资源的云平台服务层；SaaS为提供支持特定业务的软件资源的云平台服务层。
由此，本公开实施例的基于状态迁移的专有云重建及校验系统解决了人工实施专有云部署过程中，容易出错的技术问题，提升了自动化集群整体部署的准确和效率；能够有效提升基于集群基因信息的专有云平台集群有效率复制，实现真正全面系统的专有云平台自动化部署，并确保新建的专有云平台与标准专有云平台保持一致的稳定性、组件可用性和系统性能。
图1c为本公开实施例专有云集群系统分层基因信息的结构框图。
如图1c所示，专有云集群系统自底向上分为8个集群基因信息层次。其中，第1层至第3层为基础系统基因信息。具体地，第1层集群基因信息为通过带外管理系统和带内管理系统，自动扫描物理服务器集群的基因信息，收集到的信息，例如：服务器数量、服务器规格、服务器配置、服务器类型(管理节点、计算节点、存储节点等)；第2层集群基因信息为通过云平台运维管理系统，自动扫描服务服务器的操作系统、网络拓扑等信息；第3层集群基因信息为通过云平台运维管理系统，扫描并收集的集群组件信息，例如：Kubernetes、zookeeper等集群组件。
第4层为基础依赖基因信息，包括通过云平台运维管理系统，扫描并收集专有云平台上各产品线依赖的组件的信息，例如：日志系统、监控系统、安全防护系统、DNS服务等。
第5层至第7层为产品线静态服务树基因信息。具体地，第5层集群基因信息为通过专有云管理平台和Kubernetes集群管理模块，扫描专有云平台上的IaaS产品组件信息，包括支撑产品组件的K8S服务、Pod、API、容器、应用、分组、容器镜像等；第6层集群基因信息为通过专有云管理平台和Kubernetes集群管理模块，扫描专有云平台上的PaaS产品组件信息，包括支撑产品组件的虚拟机、K8S服务、Pod、API、容器、应用、分组、容器镜像等；第7层集群基因信息为通过专有云管理平台和Kubernetes集群管理模块，扫描专有云平台上的SaaS产品组件信息，包括支撑产品组件的虚拟机、虚拟存储、虚拟网络、PaaS服务、K8S服务、Pod、API、容器、应用、分组、容器镜像等。
第8层集群基因信息为对收集到的全部信息进行一致性和完整性进行校验，并生成对应的校验信息以及版本信息。示例性的，基于版本的自动生成规则确定版本信息，例如小版本的版本号自增等；或者基于人工确定版本号，生成集群各模块组件基因信息的版本号。
通过本公开实施例的基于状态迁移的专有云重建及校验系统，将所述专有云集群基因信息集合中的容器集群的基因信息用于新的专有云平台的重建，从而保证新的专有云平台和已有的标准专有云平台结构和功能一致，保证专有云平台的技术标准一致性。
图2示意性示出了根据本公开的实施例的基于状态迁移的专有云重建及校验方法的流程图。
如图2所示，该方法包括操作S210～S240。
在操作S210，获取待建专有云服务器集群的底层基础平台的信息，确定所述待建专有云服务器集群的底层基础平台是否符合部署要求；
在操作S220，若所述底层基础平台符合部署要求，则获取标准专有云平台的集群基因信息，并根据所述集群基因信息及待建专有云服务器集群的状态进行底层系统组件、底层依赖组件和专有云平台产品线的重构；
在操作S230，获取用户需求信息，并根据所述用户需求信息配置集群基因信息，选取标准专有云平台的产品线组件进行部署，获得重建的专有云平台；以及
在操作S240，对所述重建的专有云平台进行组件正确性和有效性的校 验。
以下结合具体实施例对图2所示的方法做进一步说明。
在操作S210，通过带外管理系统和带内管理系统，自动扫描待建专有云服务器集群的底层基础基因信息，例如：服务器数量、服务器规格、服务器配置、服务器类型(管理节点、计算节点、存储节点等)。获取带外管理系统和带内管理系统扫描得到的待建专有云服务器集群的底层基础基因信息，然后将所述底层基础基因信息进行分类，并与标准专有云平台的底层基础基因信息进行对比分析，获得底层平台的差异情况数据，根据所述差异情况数据确定所述待建专有云服务器集群的底层基础平台是否符合部署要求。当基础平台的差异性满足技术要求，可进行后续的系统重建流程。
示例性的，可以通过差异情况数据与预设阈值的比较，自动判断是否符合部署要求，或者，还可以根据差异情况数据形成差异信息报告。将报告发送给运维人员，使运维人员了解新建平台与标准专有云平台在基础硬件设施方面的差异，若有重要问题影响专有云平台部署，则需要人工进行解决。
当确定所述待建专有云服务器集群的底层基础平台是否符合部署要求，标记待新建专有云服务器集群为物理设备初始状态，所述物理设备初始状态表示在新的机房中将所有物理服务器都上架、上电、连接网络设备后的最初始集群状态。
在操作S220，若所述底层基础平台符合部署要求，则通过集群基因数据库获取标准专有云平台的集群基因信息。其中，集群基因信息包括基础系统基因信息、基础依赖基因信息和产品线静态服务树基因信息。然后，根据所述集群基因信息进行底层系统组件、底层依赖组件和专有云平台产品线的重构。
图3示意性示出了根据本公开的实施例的根据所述集群基因信息进行重构的流程图。
如图3所示，所述操作S220包括操作S221～S223。
在操作S221，确认待建专有云服务器集群处于物理设备初始状态，利用集群基因数据库的基础系统基因信息对底层系统组件进行重构。示例性 的，所述操作S221包括操作S2211～S2213。
在操作S2211，通过云平台运维管理系统，按照获取的基础系统基因信息中标准专有云的操作系统分步信息，对全部服务服务器的操作系统进行安装。
然后在操作S2212，通过云平台运维管理系统和带外网络管理系统，按照获取的基础系统基因信息中标准专有云的网络拓扑基因信息，对全部网络组件进行配置，使得待建专有云平台能够具备满足要求的网络结构。在完成物理网络部署后，检查物理网络是否与标准云平台的部署标准一致。
之后在操作S2213，通过云平台运维管理系统，基于集群基因信息，在待建专有云平台上创建集群组件，例如：Kubernetes、zookeeper等集群组件。由此，完成基础系统环境的部署搭建。此时标记待建专有云服务器集群为基础系统环境状态，所述基础系统环境状态表示对物理设备初始状态中的全部服务器节点进行操作系统安装、网络配置和基础组件安装后的基础状态。
在操作S222，确认待建专有云服务器集群处于基础系统环境状态，利用集群基因数据库的基础依赖基因信息对底层依赖组件进行重构。示例性的，通过云平台运维管理系统，基于标准专有云平台上各产品线依赖的组件的基因信息，例如：日志系统、监控系统、安全防护系统、DNS服务等，在集群组件的各个节点上部署各产品线的底层依赖组件。此时标记待建专有云服务器集群为基础依赖环境状态，所述基础依赖环境状态表示在基础系统环境状态中将专有云平台集群所需要的基础依赖软件进行安装部署后的状态。
在操作S223，确认待建专有云服务器集群处于基础依赖环境状态，利用集群基因数据库的产品线静态服务树基因信息对专有云产品线进行重构。其中，产品线静态服务树基因包括IaaS产品组件基因信息、PaaS产品组件基因信息和SaaS产品组件基因信息。
图4示意性的示出了根据本公开的实施例专有云产品线的示意图。如图4所示，专有云平台包括IaaS产品线、PaaS产品线和SaaS产品线，每个产品线下包括多个产品，而每个产品分为多个应用分组，其中应用分组可以通过镜像包或程序包构建。进行专有云产品线进行重构时根据不同应 用分组进行配置上线。
具体的，所述操作S223包括操作S2231～S2232。
在操作S2231，通过专有云管理平台和Kubernetes集群管理模块，基于集群基因数据库的IaaS产品组件基因信息，按照组件的关联关系和组件依赖关系在K8S容器集群上创建一致的服务、Pod、API、容器、应用、分组、容器镜像等组件，并校验组件的一致性和完整性。
在大量组件的自动重构过程中，在进行某个组件创建之前首先需要确定其所关联的资源和所依赖的组件均已创建，而后再根据相关的基因信息创建该组件。
示例性的，在创建云数据库MySQL产品时，先根据编排基因信息创建应用分组，然后根据分组信息确定容器镜像或者程序包的访问地址以及组件版本，之后按照应用的编排顺序依次进行设备分配和服务部署，并注册应用服务，实现云数据库MySQL产品的对外服务。
在操作S2232，待建专有云平台上的PaaS产品组件、SaaS产品组件依照操作S2231的方法进行重构。
完成上述重构后，在操作S230，获取用户需求信息，并根据所述用户需求信息配置集群基因信息，选取标准专有云平台的产品线组件进行部署，获得重建的专有云平台，此时标记待建专有云服务器集群为静态服务树状态，所述静态服务树状态表示在基础依赖环境状态基础上，将各个产品线的镜像包、程序包上传到系统中的集群状态。
图5示意性示出了根据本公开的实施例的对产品组件进行部署方法的流程图。
如图5所示，该方法包括操作S231～S233。
在操作S231，确认待建专有云服务器集群处于静态服务树状态，基于用户的需求选取要部署在新专有云平台上的所基于的标准专有云平台的版本。
在操作S232，通过自动读取的方式，将所需标准专有云平台版本的对应基因信息读取到系统重建和校验平台中。
在操作S233，针对专有云平台的版本，基于新建专有云平台的功能和技术指标需求，对需配置的集群基因信息进行调整。
其中，配置的集群基因信息需要符合技术规范。因此，在操作S234，系统自动对规范的符合性进行检查，防止配置修改造成系统故障。若规范符合性检查通过，则选取所述待配置的集群基因信息的对应的标准专有云平台的产品组件进行部署。
在完成部署之后，获得重建的专有云平台，然后在操作S240，对所述重建的专有云平台进行组件正确性和有效性的校验。
图6示意性示出了根据本公开的实施例对重建的专有云平台进行校验的方法流程图。
如图6所示，该方法包括操作S241～S242。
在操作S241，针对重建的专有云平台，依照系统分层维度、关联关系维度、和依赖关系维度等三个维度进行组件重构的正确性和有效性的校验。分成三轮校验，每轮校验一个维度的基因信息。
在操作S242，当校验出有组件重构错误时，对相关组件及其关联组件和依赖组件进行检查，若关联组件和依赖组件有创建错误，则进行重新创建，而后把出错组件进行重新创建，直到全部组件正确创建。此时标记待建专有云服务器集群为集群运行状态，所述集群运行状态表示基于产品线的关联关系和依赖关系对各个应用分组进行设备分配和编排后，全部应用服务都启动并正常运行的状态。
本公开实施例基于系统分层维度、关联关系维度、依赖关系维度的容器集群基因实现对专有云平台的重建，能够更准确的将标准容器集群的全貌复制到新建的专有云平台中。同时，基于专有云容器集群的集群基因信息的专有云平台重建和校验技术，能够大幅提升专有云平台部署过程中的自动化程度，也能有效保障新建的专有云平台与稳定的标准专有云平台保持技术指标的一致性。
图7示意性示出了根据本公开的实施例的基于状态迁移的专有云重建及校验装置的框图。
如图7所示，基于状态迁移的专有云重建及校验装置700包括底层基础平台信息检测模块710、重构模块720、选择部署模块730和校验模块740。
其中，底层基础平台信息检测模块710用于获取待建专有云服务器集 群的底层基础平台的信息，确定所述待建专有云服务器集群的底层基础平台是否符合部署要求.
重构模块720用于获取标准专有云平台的集群基因信息，并根据所述集群基因信息及待建专有云服务器集群的状态进行底层系统组件的重构、底层依赖组件的重构和专有云平台产品线的重构；
选择部署模块730用于获取用户需求信息，并根据所述用户需求信息配置集群基因信息，选取标准专有云平台的产品线组件进行部署，获得重建的专有云平台。
校验模块740用于对所述重建的专有云平台进行组件正确性和有效性的校验。
根据本公开的实施例的模块、单元中的任意多个、或其中任意多个的至少部分功能可以在一个模块中实现。根据本公开实施例的模块、单元中的任意一个或多个可以被拆分成多个模块来实现。根据本公开实施例的模块、单元中的任意一个或多个可以至少被部分地实现为硬件电路，例如现场可编程门阵列(FPGA)、可编程逻辑阵列(PLA)、片上系统、基板上的系统、封装上的系统、专用集成电路(ASIC)，或可以通过对电路进行集成或封装的任何其他的合理方式的硬件或固件来实现，或以软件、硬件以及固件三种实现方式中任意一种或以其中任意几种的适当组合来实现。或者，根据本公开实施例的模块、单元中的一个或多个可以至少被部分地实现为计算机程序模块，当该计算机程序模块被运行时，可以执行相应的功能。
例如，底层基础平台信息检测模块710、重构模块720、选择部署模块730和校验模块740中的任意多个可以合并在一个模块中实现，或者其中的任意一个模块可以被拆分成多个模块。或者，这些模块中的一个或多个模块的至少部分功能可以与其他模块的至少部分功能相结合，并在一个模块中实现。根据本公开的实施例，底层基础平台信息检测模块710、重构模块720、选择部署模块730和校验模块740中的至少一个可以至少被部分地实现为硬件电路，例如现场可编程门阵列(FPGA)、可编程逻辑阵列(PLA)、片上系统、基板上的系统、封装上的系统、专用集成电路(ASIC)，或可以通过对电路进行集成或封装的任何其他的合理方式等硬件或固件 来实现，或以软件、硬件以及固件三种实现方式中任意一种或以其中任意几种的适当组合来实现。或者，底层基础平台信息检测模块710、重构模块720、选择部署模块730和校验模块740中的至少一个可以至少被部分地实现为计算机程序模块，当该计算机程序模块被运行时，可以执行相应的功能。
图8示意性示出了根据本公开实施例的电子设备的框图。图8示出的电子设备仅仅是一个示例，不应对本公开实施例的功能和使用范围带来任何限制。
如图8所示，电子设备800包括处理器810、计算机可读存储介质820。该电子设备800可以执行根据本公开实施例的方法。
具体地，处理器810例如可以包括通用微处理器、指令集处理器和/或相关芯片组和/或专用微处理器(例如，专用集成电路(ASIC))，等等。处理器810还可以包括用于缓存用途的板载存储器。处理器810可以是用于执行根据本公开实施例的方法流程的不同动作的单一处理单元或者是多个处理单元。
计算机可读存储介质820，例如可以是非易失性的计算机可读存储介质，具体示例包括但不限于：磁存储装置，如磁带或硬盘(HDD)；光存储装置，如光盘(CD-ROM)；存储器，如随机存取存储器(RAM)或闪存；等等。
计算机可读存储介质820可以包括计算机程序821，该计算机程序821可以包括代码/计算机可执行指令，其在由处理器810执行时使得处理器810执行根据本公开实施例的方法或其任何变形。
计算机程序821可被配置为具有例如包括计算机程序模块的计算机程序代码。例如，在示例实施例中，计算机程序821中的代码可以包括一个或多个程序模块，例如包括821A、模块821B、……。应当注意，模块的划分方式和个数并不是固定的，本领域技术人员可以根据实际情况使用合适的程序模块或程序模块组合，当这些程序模块组合被处理器810执行时，使得处理器810可以执行根据本公开实施例的方法或其任何变形。
根据本公开的实施例，底层基础平台信息检测模块710、重构模块720、选择部署模块730和校验模块740中的至少一个可以实现为参考图8描述 的计算机程序模块，其在被处理器810执行时，可以实现上面描述的相应操作。
本公开还提供了一种计算机可读存储介质，该计算机可读存储介质可以是上述实施例中描述的设备/装置/系统中所包含的；也可以是单独存在，而未装配入该设备/装置/系统中。上述计算机可读存储介质承载有一个或者多个程序，当上述一个或者多个程序被执行时，实现根据本公开实施例的方法。
附图中的流程图和框图，图示了按照本公开各种实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，上述模块、程序段、或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图或流程图中的每个方框、以及框图或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
本领域技术人员可以理解，尽管已经参照本公开的特定示例性实施例示出并描述了本公开，但是本领域技术人员应该理解，在不背离所附权利要求及其等同物限定的本公开的精神和范围的情况下，可以对本公开进行形式和细节上的多种改变。因此，本公开的范围不应该限于上述实施例，而是应该不仅由所附权利要求来进行确定，还由所附权利要求的等同物来进行限定。

Claims (11)

  1. 一种基于状态迁移的专有云重建及校验方法，包括：
    获取待建专有云服务器集群的底层基础平台的信息，确定所述待建专有云服务器集群的底层基础平台是否符合部署要求；
    若所述底层基础平台符合部署要求，则获取标准专有云平台的集群基因信息，并根据所述集群基因信息及待建专有云服务器集群的状态进行底层系统组件的重构、底层依赖组件的重构和专有云平台产品线的重构；
    获取用户需求信息，并根据所述用户需求信息配置所述集群基因信息，选取标准专有云平台的产品线组件进行部署，获得重建的专有云平台；以及
    对所述重建的专有云平台进行组件正确性和有效性的校验。
  2. 根据权利要求1所述的专有云重建及校验方法，其中，所述获取待建专有云服务器集群的底层基础平台的信息，确定所述待建专有云服务器集群的底层基础平台是否符合部署要求包括：
    获取带外管理系统和带内管理系统扫描得到的待建专有云服务器集群的底层基础基因信息；
    将所述底层基础基因信息进行与标准专有云平台的底层基础基因信息进行对比分析，获得差异情况数据；以及
    根据所述差异情况数据确定所述待建专有云服务器集群的底层基础平台是否符合部署要求，若符合部署要求则标记为物理设备初始状态。
  3. 根据权利要求2所述的专有云重建及校验方法，其中，所述底层系统组件的重构包括：
    确认待建专有云服务器集群处于物理设备初始状态，根据所述集群基因信息中标准专有云的操作系统分步信息，对全部服务服务器的操作系统进行安装；
    根据所述集群基因信息中标准专有云的网络拓扑基因信息，对网 络组件进行配置；以及
    所述集群基因信息中的集群组件信息，在待建专有云平台上创建集群组件。
  4. 根据权利要求3所述的专有云重建及校验方法，其中，所述底层依赖组件的重构包括：
    确认待建专有云服务器集群处于基础系统环境状态，根据所述集群基因信息中的基础依赖基因信息，在所述集群组件的各个节点上部署各产品线的底层依赖组件。
  5. 根据权利要求4所述的专有云重建及校验方法，其中，所述专有云平台产品线的重构包括：
    确认待建专有云服务器集群处于基础依赖环境状态，对IaaS产品组件、PaaS产品组件和SaaS产品组件进行产品的组件重构，并在所述产品组件重构完成后标记为静态服务树状态。
  6. 根据权利要求5所述的专有云重建及校验方法，其中，所述组件重构包括：
    确定所述组件所关联的资源和依赖的组件是否已重构，以及
    若确定所述组件所关联的资源和依赖的组件已重构，则根据所述组件对应的基因信息重构该组件。
  7. 根据权利要求5所述的专有云重建及校验方法，其中，所述获取用户需求信息，并根据所述用户需求信息配置集群基因信息，选取标准专有云平台的产品线组件进行部署包括：
    确认待建专有云服务器集群处于静态服务树状态，基于用户需求信息，选取部署在待建专有云服务器集群的标准专有云平台版本；
    获取所述标准专有云平台版本对应的集群基因信息，作为待配置的集群基因信息；以及
    基于待建专有云服务器集群的功能和技术指标需求，对所述待配置的集群基因信息进行调整，并对所述待配置的集群基因信息的调整过程的规范符合性进行检查；
    若规范符合性检查通过，则选取所述待配置的集群基因信息的对应的标准专有云平台的产品组件进行部署。
  8. 根据权利要求7所述的专有云重建及校验方法，其中，所述对所述重建的专有云平台进行组件正确性和有效性的校验包括：
    针对重建的专有云平台，依次在系统分层维度、关联关系维度和依赖关系维度三个维度进行组件重构的正确性和有效性的校验；
    若校验确定存在组件重构错误，则对该组件及该组件的关联组件和依赖组件进行检查；以及
    若关联组件和依赖组件存在重构错误，则重构所述关联组件和依赖组件，直到全部组件正确重构。
  9. 一种基于状态迁移的专有云重建及校验装置，包括：
    底层基础平台信息检测模块，用于获取待建专有云服务器集群的底层基础平台的信息，确定所述待建专有云服务器集群的底层基础平台是否符合部署要求；
    重构模块，用于获取标准专有云平台的集群基因信息，并根据所述集群基因信息及待建专有云服务器集群的状态进行底层系统组件的重构、底层依赖组件的重构和专有云平台产品线的重构；
    选择部署模块，用于获取用户需求信息，并根据所述用户需求信息配置集群基因信息，选取标准专有云平台的产品线组件进行部署，获得重建的专有云平台；以及
    校验模块，用于对所述重建的专有云平台进行组件正确性和有效性的校验。
  10. 一种电子设备，包括：
    一个或多个处理器；
    存储装置，用于存储一个或多个程序，
    其中，当所述一个或多个程序被所述一个或多个处理器执行时，使得所述一个或多个处理器执行如权利要求1-8任一项所述的基于状态迁移的专有云重建及校验方法。
  11. 一种计算机可读存储介质，其上存储有可执行指令，该指令被处理器执行时使处理器执行如权利要求1-8任一项所述的基于状态迁移的专有云重建及校验方法。
PCT/CN2021/123254 2020-10-13 2021-10-12 一种基于状态迁移的专有云重建及校验方法及装置 WO2022078322A1 (zh)

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