A resource orchestration method, device and medium based on heterogeneous multi-cloud

Abstract

The present invention provides a resource orchestration method, device and medium based on heterogeneous multi-cloud, belonging to the field of resource orchestration technology. It is achieved by the following technical scheme: multiple cloud platforms are managed into a unified multi-cloud management platform; the resources of multiple cloud platforms are used as resource nodes, and the context connection point is set for each resource node, and the input parameters are set; the connection relationship between resource nodes is established and drawn as a blueprint template; the blueprint template is deployed, and if approval is required, the blueprint deployment approval is entered, and if no approval is required, the deployment is directly performed; after the deployment is completed, the target cloud microservice returns the resource activation and configuration results to the multi-cloud management platform to complete the activation of the entire resource. The present invention provides unified resource management, business capabilities, operation and maintenance functions indiscriminately through heterogeneous multi-cloud management, thereby shielding the differences in the underlying cloud resource pool, greatly reducing the user's construction cost and operation and maintenance cost.

Description

Technical Field

[0001] The present invention relates to a resource orchestration method, device and medium based on heterogeneous multi-cloud, belonging to the technical field of resource orchestration. Background Art

[0002] In the construction of enterprise cloud platforms, due to different understandings of cloud, enterprises may choose multiple cloud platforms for different departments and different needs. After a period of construction, there are often several cloud platforms with different manufacturers, different architectures, and different management processes within the enterprise. Users need to create cloud resources across platforms, which is not only complicated, but also has complex dependencies. It requires high requirements and heavy workload for management personnel, and the cost of using the cloud increases. How to improve the efficiency of creating cloud resources and simplify the operation process has become a difficult problem to solve. Summary of the invention

[0003] The purpose of the present invention is to provide a resource orchestration method, device and medium based on heterogeneous multi-cloud, and to solve the efficiency problem of operating and managing resources based on the visual orchestration of heterogeneous multi-cloud.

[0004] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0005] Manage multiple cloud platforms into a unified multi-cloud management platform through credential management;

[0006] The resources of multiple cloud platforms are used as resource nodes, and the context connection points and input parameters are set for each resource node;

[0007] Establish the connection relationship between resource nodes and draw it as a blueprint template, set configuration information for the resource nodes, and save the blueprint template;

[0008] Deploy the blueprint template. If approval is required, enter the blueprint deployment approval process. If no approval is required, deploy directly.

[0009] After the deployment is completed, the target cloud microservice returns the resource activation and configuration results to the multi-cloud management platform to complete the activation of the entire resource.

[0010] Preferably, the resource node types include VPC, subnet, security group, and cloud server.

[0011] Preferably, the blueprint template is deployed in the following manner:

[0012] The blueprint template is converted into a blueprint data model through the blueprint interpreter, and the association relationship between each data model is saved through the data model Graphic to form a complete blueprint data model;

[0013] According to the multi-cloud management platform interface parameter requirements, the resource request parameters are converted into a resource creation interface JSON request;

[0014] The multi-cloud management platform obtains the current multi-cloud resource distribution, calculates the current resource surplus of each cloud provider, and selects the cloud with the highest resource surplus as the target cloud;

[0015] The generated JSON request parameters are converted into service requests of the target cloud. The multi-cloud management platform sends the target cloud service request to the target cloud microservice to communicate with the cloud provider.

[0016] The target cloud microservice calls the target cloud provider's service interface to activate and configure corresponding resources.

[0017] Preferably, the blueprint interpreter includes a control layer, a business logic layer, and a data persistence layer. The control layer is used to receive blueprint template data, the business logic layer is used to process the relationship between nodes and connections and request parameters in the blueprint template, and the data persistence layer is used to output a blueprint data model. The blueprint data model is converted into a data model of the data persistence layer and saved in a database.

[0018] Preferably, the specific formula for calculating the current remaining resource situation of each cloud provider is as follows:

[0019] S=a*remaining number of CPUs+b*remaining memory capacity+c*remaining hard disk capacity+d*remaining bandwidth,

[0020] Among them, S represents the remaining value of resources, a represents the weight of CPU, b represents the weight of memory, c represents the weight of disk, and d represents the weight of network resources.

[0021] Preferably, the weight values ​​are as follows:

[0022] Computation-intensive: CPU weight = 0.4, memory weight = 0.3, disk weight = 0.2, network resource weight -0.1;

[0023] IO intensive: CPU weight = 0.3, memory weight = 0.3, disk weight = 0.3, network resource weight = 0.1;

[0024] Network intensive: CPU weight = 0.3, memory weight = 0.2, disk weight = 0.2, network resource weight = 0.3.

[0025] Preferably, the configuration information includes virtual machine specifications, mirroring, payment type, and disk specifications.

[0026] Preferably, in the blueprint data model, the blueprint resource node data model is Node, the resource node parameter data model is Node_Data, and the blueprint connection line data model is Connector.

[0027] The advantages of the present invention are as follows: the present invention provides unified resource management, business capabilities, operation and maintenance functions indiscriminately through heterogeneous multi-cloud management, thereby shielding the differences in the underlying cloud resource pool and greatly reducing the user's construction cost and operation and maintenance cost.

[0028] Through multi-cloud unified scheduling, we can achieve usage balance and advantages among multiple clouds and reduce the business pressure of a single cloud.

[0029] Blueprint resource orchestration is a process used to automatically manage multiple workloads across public and private clouds. Users only need to edit and connect cloud components to complete the blueprint orchestration of a set of cloud resources, thereby completing the creation of resource requirements and improving operational efficiency. It simplifies the management of manual and repetitive tasks through automation, making it easier to coordinate cloud-based resources and expand the infrastructure according to business needs; improve business responsiveness, blueprint orchestration can create a more integrated, efficient, and responsive cloud deployment model, thereby improving agility and accelerating product delivery; improve the reusability of orchestration models, and frequently used orchestration processes can be saved for reuse the next time a similar resource business is opened, greatly reducing the design workload. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0031] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] Example 1

[0034] like Figure 1 As shown, a resource orchestration method based on heterogeneous multi-cloud is implemented through the following technical solutions:

[0035] S1: Manage multiple cloud platforms into a unified multi-cloud management platform through credential management. The multi-cloud management platform connects to multiple cloud service providers through different microservices, including public clouds: Alibaba Cloud, Tencent Cloud, Huawei Cloud, AWS Cloud, etc., and private clouds: Alibaba Stack, Telecom Zstack, Huawei Stack and Openstack. Manage multiple clouds through credential management. This shields the differences and details between multiple clouds, and the business layer seems to have no perception of multiple clouds, as if operating on one platform.

[0036] S2: Use resources of multiple cloud platforms as resource nodes, set a context connection point for each resource node, and set input parameters; the resource node types include VPC, subnet, security group, and cloud server.

[0037] The input parameters of VPC are: IP address range, VPC name, the input parameters of subnet are: IP address range, subnet name; the input parameters of cloud server are: memory size, charging type, server specifications, cloud disk specifications, cloud disk capacity, image type, operating system, operating system bit number, image, name, password, etc.

[0038] S3: Establish the connection relationship between resource nodes and draw it as a blueprint template, set configuration information for the resource nodes, and save the blueprint template; specifically, draw the blueprint template by dragging different resource nodes and node connections, and the configuration information includes virtual machine specifications, images, payment types, and disk specifications.

[0039] S4: Deploy the blueprint template. If approval is required, enter the blueprint deployment approval process. If no approval is required, deploy directly.

[0040] The specific method of blueprint template deployment is as follows:

[0041] S401: The blueprint template is converted into a blueprint data model through a blueprint interpreter, and the association relationship between each data model is saved through the data model Graphic to form a complete blueprint data model.

[0042] S402: According to the multi-cloud management platform interface parameter requirements, the resource request parameters are converted into a resource creation interface JSON request.

[0043] Specifically, the resource request parameters in Node_Data are converted into resource creation interface JSON requests using the Jackson tool, such as the mask, name, and network settings required to create a VPC. The parameters generated here do not take into account the heterogeneity and differences between multiple clouds.

[0044] S403: The multi-cloud management platform obtains the current multi-cloud resource distribution, calculates the current remaining resources of each cloud provider, and selects the cloud with the highest remaining resources as the target cloud.

[0045] Specifically, the specific formula for calculating the current remaining resource situation S of each cloud provider is as follows:

[0046] S=a*remaining number of CPUs+b*remaining memory capacity+c*remaining hard disk capacity+d*remaining bandwidth,

[0047] Among them, S represents the remaining value of resources, a represents the weight of CPU, b represents the weight of memory, c represents the weight of disk, and d represents the weight of network resources.

[0048] The weight values ​​are as follows:

[0049] Computation-intensive: CPU weight = 0.4, memory weight = 0.3, disk weight = 0.2, network resource weight -0.1;

[0050] IO intensive: CPU weight = 0.3, memory weight = 0.3, disk weight = 0.3, network resource weight = 0.1;

[0051] Network intensive: CPU weight = 0.3, memory weight = 0.2, disk weight = 0.2, network resource weight = 0.3.

[0052] The weight of each resource is dynamically adjusted according to the requirements. If the current need is mainly computing, the weight of the CPU is higher. If it is mainly storage, the weight of the disk is higher. If it is mainly network transmission, the weight of network resources is higher.

[0053] S404: The generated JSON request parameters are converted into a service request of the target cloud. The multi-cloud management platform sends the target cloud service request to the target cloud microservice to communicate with the cloud provider.

[0054] S405: The target cloud microservice calls the service interface of the target cloud provider to activate and configure corresponding resources.

[0055] In this embodiment, the blueprint interpreter includes a control layer, a business logic layer, and a data persistence layer. The control layer is used to receive blueprint template data, the business logic layer is used to process the relationship between nodes and connections and request parameters in the blueprint template, and the data persistence layer is used to output the blueprint data model, which is converted into a data model of the data persistence layer and saved in the database. In the blueprint data model, the data model of the blueprint resource node is Node, the data model of the resource node parameter is Node_Data, and the data model of the blueprint connection line is Connector.

[0056] S5: After the deployment is completed, the target cloud microservice returns the resource activation and configuration results to the multi-cloud management platform to complete the activation of the entire resource.

[0057] It should be noted that:

[0058] The present invention can orchestrate heterogeneous multi-cloud resources. This orchestration method is not limited to the same cloud. It can realize multiple application deployments in heterogeneous multi-clouds through one-time orchestration, shielding the differences in underlying cloud resources. The cloud components have cross-cloud universality, truly achieving the purpose of fast cloud deployment with one-time construction.

[0059] Orchestrate once, use multiple times. This orchestration method allows multiple application deployments after one resource design and orchestration. Applications can be deployed for different clouds and different architectures to improve the design reuse rate.

[0060] This orchestration method is intuitive and visual, and the relationships between cloud components can be carried out step by step according to prompts, reducing the requirements for the cloud computing level of managers, truly eliminating personnel dependence, making it available to everyone and easy to use.

[0061] Example 2

[0062] The embodiment of the present disclosure also provides a resource orchestration device based on heterogeneous multi-cloud, including a processor and a memory. Optionally, the device may also include a communication interface and a bus. Among them, the processor, the communication interface, and the memory can communicate with each other through the bus. The communication interface can be used for information transmission. The processor can call the logic instructions in the memory to execute the resource orchestration method based on heterogeneous multi-cloud of the above embodiment.

[0063] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0064] The memory, as a computer-readable storage medium, can be used to store software programs and computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor executes the functional application and data processing by running the program instructions / modules stored in the memory, that is, the resource orchestration method based on heterogeneous multi-cloud in the above embodiment is implemented.

[0065] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory may include a high-speed random access memory and may also include a non-volatile memory.

[0066] An embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute the above-mentioned resource orchestration method based on heterogeneous multi-cloud.

[0067] The computer-readable storage medium mentioned above may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0068] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, and other media that can store program codes, or a transient storage medium.

[0069] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A resource orchestration method based on heterogeneous multi-cloud, characterized in that: include: Manage multiple cloud platforms into a unified multi-cloud management platform through credential management; The resources of multiple cloud platforms are used as resource nodes, and the context connection points and input parameters are set for each resource node; Establish the connection relationship between resource nodes and draw it as a blueprint template, set configuration information for the resource nodes, and save the blueprint template; Deploy the blueprint template. If approval is required, enter the blueprint deployment approval process. If no approval is required, deploy directly. After the deployment is completed, the target cloud microservice returns the resource activation and configuration results to the multi-cloud management platform to complete the activation of the entire resource; The resource node types include VPC, subnet, security group, and cloud server; The specific method of blueprint template deployment is as follows: The blueprint template is converted into a blueprint data model through the blueprint interpreter, and the association relationship between each data model is saved through the data model Graphic to form a complete blueprint data model; According to the multi-cloud management platform interface parameter requirements, the resource request parameters are converted into a resource creation interface JSON request; The multi-cloud management platform obtains the current multi-cloud resource distribution, calculates the current resource surplus of each cloud provider, and selects the cloud with the highest resource surplus value as the target cloud; The generated JSON request parameters are converted into service requests of the target cloud. The multi-cloud management platform sends the target cloud service request to the target cloud microservice to communicate with the cloud provider. The target cloud microservice calls the target cloud provider's service interface to activate and configure corresponding resources.

2. The resource orchestration method based on heterogeneous multi-cloud according to claim 1, characterized in that: The blueprint interpreter includes a control layer, a business logic layer, and a data persistence layer. The control layer is used to receive blueprint template data, the business logic layer is used to process the relationship between nodes and connections and request parameters in the blueprint template, and the data persistence layer is used to output a blueprint data model. The blueprint data model is converted into a data model of the data persistence layer and saved in a database.

3. The resource orchestration method based on heterogeneous multi-cloud according to claim 1, characterized in that: The specific formula for calculating the current remaining resource situation of each cloud provider is as follows: S=a*remaining number of CPUs+b*remaining memory capacity+c*remaining hard disk capacity+d*remaining network resources, Among them, S represents the remaining value of resources, a represents the weight of CPU, b represents the weight of memory, c represents the weight of disk, and d represents the weight of network resources.

4. The resource orchestration method based on heterogeneous multi-cloud according to claim 1, characterized in that: The weight values ​​are as follows: Computation-intensive: CPU weight = 0.4, memory weight = 0.3, disk weight = 0.2, network resource weight = 0.1; IO intensive: CPU weight = 0.3, memory weight = 0.3, disk weight = 0.3, network resource weight = 0.1; Network intensive: CPU weight = 0.3, memory weight = 0.2, disk weight = 0.2, network resource weight = 0.

3.

5. The resource orchestration method based on heterogeneous multi-cloud according to claim 1, characterized in that: The configuration information includes virtual machine specifications, images, payment types, and disk specifications.

6. The resource orchestration method based on heterogeneous multi-cloud according to claim 1, characterized in that: In the blueprint data model, the blueprint resource node data model is Node, the resource node parameter data model is Node_Data, and the blueprint connection line data model is Connector.

7. A resource orchestration device based on heterogeneous multi-cloud, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the heterogeneous multi-cloud-based resource orchestration method according to any one of claims 1 to 6 when running the program instructions.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the method described in any one of claims 1 to 6 is implemented.

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