Service optimization processing system based on cloud platform
By designing a service optimization processing system on the cloud platform, using Sdocker components and message queues to implement container resource management and scheduling, the problem of low container scheduling efficiency is solved and the operation efficiency and accuracy of the cloud platform is improved.
Patent Information
- Application Number
- CN202410943266.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-07-15
- Publication Date
- 2024-12-20
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing container scheduling scheme does not fully consider the actual resource usage during container runtime, which affects the operation and processing efficiency of the cloud platform.
Design a cloud-based service optimization processing system, obtain resources by using Sdocker components to call the API interfaces of Keystone, Glance and Neutron components in the OpenStack cloud platform, and create and manage containers through the Docker API, and use message queues to communicate between components. , realize efficient scheduling and deployment of containers, and provide user information viewing function through RESTful API, combine the LBSM scheduling mechanism to select the optimal computing node and NUMA node to perform resource management and feedback collection of containers.
It improves container scheduling efficiency, reduces the coupling between Sdocker system components, improves the operation efficiency and accuracy of the cloud platform service system, and enhances the availability of the system.
Smart Images
Figure CN119166325A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of cloud platforms, and in particular to a service optimization processing system based on a cloud platform. Background Art
[0002] As a resource management platform in the field of cloud computing, the cloud platform can ensure the quality of user services with its high availability and realize the unified allocation and elastic scaling of resources with its high flexibility. At the same time, the cloud platform provides a variety of calling methods and rich API interfaces, which greatly facilitates users to develop other applications. Docker container technology has the advantages of rapid deployment and delivery, flexible migration and expansion, and high resource utilization. The OpenStack cloud platform has the advantages of open source, compatibility, flexibility and scalability. The integration of the two can provide users with CaaS solutions, provide efficient and flexible management and control for Docker containers, and bring better performance and higher community activity to OpenStack. However, the existing container scheduling schemes mostly schedule and allocate resources to containers based on the user's initial resource request for the container, without fully considering the actual resource usage of the container during runtime, which seriously affects the operation and processing of the cloud platform. Therefore, it is necessary to design a service optimization processing system based on the cloud platform with intelligent management control and high processing efficiency. Summary of the invention
[0003] The purpose of the present invention is to provide a service optimization processing system based on a cloud platform to solve the problems raised in the above background technology.
[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a service optimization processing method based on a cloud platform, comprising: Carry out architecture design and management of cloud platform service system; Carry out message transmission and interface management for cloud platform service system; Further analyze and control the cloud platform service system; Conduct collection and feedback management of cloud platform service systems; The architecture design and management of the cloud platform service system includes: In the OpenStack cloud platform service system, Sdocker obtains the image and network resources required to create containers by calling the API interfaces exposed by Keystone, Glance and Neutron components in OpenStack, and creates and updates containers by calling the API interfaces provided by Docker.
[0005] According to the above technical solution, the message transmission and interface management of the cloud platform service system includes: Control the transmission and communication between cloud platform service system components through message queues, including: Producer, Exchange, Binding, Queue and Consumer.
[0006] According to the above technical solution, the message transmission and interface management of the cloud platform service system further includes: The sdoc_API of Sdocker provides users with a RESTful API, allowing users to quickly and easily view relevant information about containers and computing nodes in OpenStack through HTTP requests.
[0007] According to the above technical solution, the further analysis and control of the cloud platform service system includes: In the cloud platform service system, when the user turns on the load balancing scheduling mode of Sdocker and specifies the container resource information (CPU, memory, and disk), and requests Sdocker to create a container, the command in which the user requests to create a container includes the container name, container image, container network information, and container resource request specification information; After receiving the container scheduling request information, the sdoc_Scheduler component first parses the resource information of the container. If the container has CPU, memory, and disk information, it schedules the container. When scheduling the container, the LBSM scheduling mechanism is used to select the optimal computing node and the optimal NUMA node for the container. After sdoc_Compute obtains the image information, it calls the API interface provided by Docker to create and start the container according to the container name and container resource request information. Sdoc_Compute relies on the parsed token to call the API interface exposed by the Neutron component to create a port, and creates a virtual network card on the local computing node through the port, and mounts the network card to the network namespace of the container to complete the container deployment.
[0008] According to the above technical solution, the collection and feedback management of the cloud platform service system includes: The operation data information of the cloud platform service system is collected according to the set cycle and transmitted to the workbench so that the staff can conduct corresponding monitoring and analysis; Users can provide feedback on the use of cloud services, providing an effective reference for subsequent maintenance and optimization of the cloud platform service system.
[0009] According to the above technical solution, a service optimization processing system based on a cloud platform includes: Design transmission module, used for design transmission control of cloud platform service system; Analysis and management module, used for analysis and optimization management of cloud platform service systems; The collection and feedback module is used to collect and feedback information from the cloud platform service system.
[0010] According to the above technical solution, the design transmission module includes: Architecture design module, used to manage the architecture design of the cloud platform service system; Message transmission module, used for message transmission and processing between cloud platform service system components; The interface management module is used to control the cloud platform service system interface.
[0011] According to the above technical solution, the analysis management module includes: Request management module, used for load balancing request management of cloud platform service system; Analysis and control module, used for cloud platform container scheduling analysis and control; The deployment processing module is used to perform cloud platform container deployment analysis and processing.
[0012] According to the above technical solution, the collection and feedback module includes: Information collection module, used for collecting information of cloud platform service system; Feedback input module, used for feedback input management of data information.
[0013] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention, by providing a design transmission module, an analysis management module and a collection feedback module, can not only realize the asynchronous transmission of messages between the various components of the cloud platform service system and improve the efficiency of container scheduling, but also reduce the coupling degree between the various components of the Sdocker system of the cloud platform service system and improve the availability of the sdocker system, thereby effectively improving the operating efficiency and accuracy of the cloud platform service system. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 is a flow chart of a service optimization processing method based on a cloud platform provided in Embodiment 1 of the present invention; Figure 2 This is a module structure diagram of a cloud platform-based service optimization processing system provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION
[0015] 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.
[0016] Embodiment 1: Figure 1 This is a flow chart of a service optimization processing method based on a cloud platform provided in Embodiment 1 of the present invention. This embodiment can be applied to a cloud platform service system. The method can be executed by a service optimization processing system based on a cloud platform provided in Embodiment 1 of the present invention. The system is composed of multiple software and hardware modules, such as Figure 1 As shown, the method specifically comprises the following steps: S101. Conduct architecture design and management of cloud platform service systems; Exemplarily, in an embodiment of the present invention, in an OpenStack cloud platform service system, Sdocker obtains the image and network resources required for creating a container by calling the API interfaces exposed by the Keystone, Glance and Neutron components in OpenStack, and creates and updates the container by calling the API interfaces provided by Docker; in this step, Sdocker internally includes six components, Sdoc_API, Sdoc_Scheduler, Sdoc_Compute, Sdoc_Recompute, Sdoc_Migration and Sdoc_Conductor, and each component interacts with the user by providing a configuration file on the local node. At the same time, the various components of Sdocker have clear division of labor and low coupling, and the scalability of container scheduling can be achieved by adding new components to Sdocker to respond to more complex container scheduling scenarios. Among them, the Sdoc_API component is deployed on the OpenStack control node, and is mainly responsible for user request parameter processing, scheduling mode parsing and token acquisition. The Sdoc_Scheduler component is deployed on the control node and is mainly responsible for scheduling mode parsing and container scheduling. The sdoc_Compute component is deployed on the OpenStack computing node and does not interact directly with the Sdoc database. It is mainly responsible for resource acquisition and container creation. The Sdoc_Recompute component is deployed on the OpenStack computing node and does not interact directly with the Sdoc database. It is designed for high resource utilization scheduling mode services and is mainly responsible for container monitoring and container update / migration. The Sdoc_Migration component is deployed on the OpenStack computing node and does not interact with the Sdoc database. It is designed for high resource utilization scheduling mode services and is mainly responsible for container migration and container re-creation. The sdoc_Conductor component is deployed on the control node and is mainly used as a database proxy for the Sdoc_Compute component and the Sdoc_Recompute component to prevent the sdoc_Compute component and the Sdoc_Recompute component located on the OpenStack computing node from remotely accessing the Sdoc database located on the control node.
[0017] S102, performing message transmission and interface management on the cloud platform service system; Exemplarily, in an embodiment of the present invention, the cloud platform service system components are controlled to communicate through message queues, including: Producer, Exchange, Binding, Queue and Consumer; in this step, Producer sends a message carrying a routing key to Exchange, Exchange is responsible for comparing the routing key with the binding keys of all Queues bound to it, and sending the message to the matching Queue for storage, and finally Consumer will get the message from the Queue. At the same time, Exchange has three common types: Direct, Topic and Fanout. When using a Direct type Exchange, the message will be sent to the queue only when the binding key of the queue is exactly the same as the routing key in the message. When using a Topic type Exchange, the binding key allows wildcards "#" (representing zero or more words) and "" (representing one word). For example, a message with a routingkey of "sdocker.jn.edu" can be successfully sent to a queue or binding with a binding key of "#.jn.edu". The key is "#.jn.*", but it cannot be sent to the queue with the binding key "*.edu". When using the Fanout type Exchange, the message will be broadcast to all queues bound to the Exchange. Users can flexibly select the appropriate Exchange for message transmission according to their needs. Through this step, the asynchronous transmission of messages between the various components of the cloud platform service system can be realized to improve the efficiency of container scheduling, and the coupling between the various components of the Sdocker system of the cloud platform service system can be reduced and the availability of the sdocker system can be improved.
[0018] The sdoc_API of Sdocker provides users with a RESTful API, so that users can easily and quickly view the relevant information of containers and each computing node in OpenStack through HTTP requests; in this step, for different scheduling modes, the Sdoc_API component is controlled to display differentiated information to users. For example, when a user wants to view all information of a specified container, the Sdoc_API component will parse the scheduling mode. Only in the load balancing scheduling mode and when the resource requirement type of the container is 4, will all information including the container bandwidth field be displayed to the user. Otherwise, the container information returned to the user by the Sdoc_API component does not include the bandwidth field. When a user wants to view all information of a specified computing node, the Sdoc_API component will display all information including the computing node "bandwidth_total" field to the user only in the load balancing scheduling mode and when the resource requirement type of the container is 4. Otherwise, the computing node information returned to the user by the Sdoc_API component does not include the "bandwidth_total" field. S103, further analyzing and controlling the cloud platform service system; Exemplarily, in an embodiment of the present invention, in a cloud platform service system, when a user turns on the load balancing scheduling mode of Sdocker and specifies the container resource information (CPU, memory, and disk), and requests Sdocker to create a container, the command in which the user requests to create a container includes the container name, container image, container network information, and resource request specification information of the container; in this step, after Sdoc_API receives the user's request to create a container command, it first processes the user's request parameters to verify whether the user's request complies with the set rules. If it complies, Sdoc_API is controlled to create a new container entry in the database. Otherwise, Sdoc_API returns the parameters to the user. The error information is displayed. Sdoc_API obtains a token by calling the API interface exposed by the Keystone component. With the token, you can obtain the permission to use the image and virtual network resources in OpenStack. Sdoc_API integrates the processed user request parameters and the obtained token into the container scheduling request information. Sdoc_API sends the container scheduling request information to the message queue and returns detailed information including the container name and container image to the user for the user to view. At this time, the user cannot view the scheduling result of the container. The Sdoc_Scheduler component obtains the container scheduling request information from the message queue to prepare for the container scheduling.
[0019] After receiving the container scheduling request information, the sdoc_Scheduler component first parses the resource information of the container. If the container has CPU, memory and disk information, the container is scheduled. When scheduling the container, the LBSM scheduling mechanism is used to select the optimal computing node and the optimal NUMA node for the container. In this step, Sdoc_Scheduler integrates the processed user request parameters, the obtained token and the container scheduling results into the container creation request information. Sdoc_Scheduler sends the container creation request information to the message queue. The Sdoc_Compute component of the optimal computing node obtains the container creation request information from the message queue to prepare for container creation. Sdoc_Compute parses the token from the container creation request information. Relying on the token, sdoc_Compute calls the API interface exposed by the Glance component to download the image required by the container to the local computing node.
[0020] After sdoc_Compute obtains the image information, it calls the API interface provided by Docker to create and start the container according to the container name and container resource request information. Sdoc_Compute relies on the parsed token to call the API interface exposed by the Neutron component to create a port, and creates a virtual network card on the local computing node through the port, and mounts the network card to the network namespace of the container to complete the container deployment. In this step, sdoc_Compute integrates the created container related information into container update request information, and sends the container update request information to the message queue. The sdoc_Conductor component obtains the container update request information from the message queue, and updates the container entry and the optimal computing node entry in the Sdoc database according to the container scheduling result and other information in the container update request message. At this time, the container deployment process is completely completed, and users can view the complete container information through the API interface exposed by Sdoc_API (the container information at this time includes the container scheduling result), effectively improving the operation efficiency and accuracy of the cloud platform service system.
[0021] S104, performing collection feedback management of the cloud platform service system; Exemplarily, in an embodiment of the present invention, the operation data information of the cloud platform service system is collected according to a set period and transmitted to a workbench so that the staff can perform corresponding monitoring and analysis; Users can provide feedback on the use of cloud services, providing an effective reference for subsequent maintenance and optimization of the cloud platform service system.
[0022] Embodiment 2: Embodiment 2 of the present invention provides a service optimization processing system based on a cloud platform. Figure 2 A schematic diagram of the module structure of a service optimization processing system based on a cloud platform provided in the second embodiment, such as Figure 2 As shown, the system includes: Design transmission module, used for design transmission control of cloud platform service system; Analysis and management module, used for analysis and optimization management of cloud platform service systems; The collection and feedback module is used to collect and feedback information from the cloud platform service system.
[0023] In some embodiments of the present invention, the design transmission module includes: Architecture design module, used to manage the architecture design of the cloud platform service system; Message transmission module, used for message transmission and processing between cloud platform service system components; The interface management module is used to control the cloud platform service system interface.
[0024] In some embodiments of the present invention, the analysis management module includes: Request management module, used for load balancing request management of cloud platform service system; Analysis and control module, used for cloud platform container scheduling analysis and control; The deployment processing module is used to perform cloud platform container deployment analysis and processing.
[0025] In some embodiments of the present invention, the collection feedback module includes: Information collection module, used for collecting information of cloud platform service system; Feedback input module, used for feedback input management of data information.
[0026] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.
[0027] Finally, it should be noted that the above 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 service optimization processing method based on a cloud platform, characterized in that: include: Carry out architecture design and management of cloud platform service system; Carry out message transmission and interface management for cloud platform service system; Further analyze and control the cloud platform service system; Conduct collection and feedback management of cloud platform service systems; The architecture design and management of the cloud platform service system includes: In the OpenStack cloud platform service system, Sdocker obtains the image and network resources required to create containers by calling the API interfaces exposed by Keystone, Glance and Neutron components in OpenStack, and creates and updates containers by calling the API interfaces provided by Docker.
2. A cloud platform-based service optimization processing method according to claim 1, characterized in that: The message transmission and interface management of the cloud platform service system includes: Control the transmission and communication between cloud platform service system components through message queues, including: Producer, Exchange, Binding, Queue and Consumer.
3. The service optimization processing method based on a cloud platform according to claim 1, characterized in that: The message transmission and interface management of the cloud platform service system further includes: The sdoc_API of Sdocker provides users with a RESTful API, allowing users to quickly and easily view relevant information about containers and computing nodes in OpenStack through HTTP requests.
4. The service optimization processing method based on a cloud platform according to claim 1, characterized in that: The further analysis and control of the cloud platform service system includes: In the cloud platform service system, when the user turns on the load balancing scheduling mode of Sdocker and specifies the container resource information (CPU, memory, and disk), and requests Sdocker to create a container, the command in which the user requests to create a container includes the container name, container image, container network information, and container resource request specification information; After receiving the container scheduling request information, the sdoc_Scheduler component first parses the resource information of the container. If the container has CPU, memory, and disk information, it schedules the container. When scheduling the container, the LBSM scheduling mechanism is used to select the optimal computing node and the optimal NUMA node for the container. After sdoc_Compute obtains the image information, it calls the API interface provided by Docker to create and start the container according to the container name and container resource request information. Sdoc_Compute relies on the parsed token to call the API interface exposed by the Neutron component to create a port, and creates a virtual network card on the local computing node through the port, and mounts the network card to the network namespace of the container to complete the container deployment.
5. The cloud platform-based service optimization processing method according to claim 1, characterized in that: The collection and feedback management of the cloud platform service system includes: The operation data information of the cloud platform service system is collected according to the set cycle and transmitted to the workbench so that the staff can conduct corresponding monitoring and analysis; Users can provide feedback on the use of cloud services, providing an effective reference for subsequent maintenance and optimization of the cloud platform service system.
6. A service optimization processing system based on a cloud platform, characterized in that: include: Design transmission module, used for design transmission control of cloud platform service system; Analysis and management module, used for analysis and optimization management of cloud platform service systems; The collection and feedback module is used to collect and feedback information from the cloud platform service system.
7. The cloud platform-based service optimization processing system according to claim 6, characterized in that: The design transmission module includes: Architecture design module, used to manage the architecture design of the cloud platform service system; Message transmission module, used for message transmission and processing between cloud platform service system components; The interface management module is used to control the cloud platform service system interface.
8. The cloud platform-based service optimization processing system according to claim 6, characterized in that: The analysis management module includes: Request management module, used for load balancing request management of cloud platform service system; Analysis and control module, used for cloud platform container scheduling analysis and control; The deployment processing module is used to perform cloud platform container deployment analysis and processing.
9. The cloud platform-based service optimization processing system according to claim 6, characterized in that: The collection feedback module includes: Information collection module, used for collecting information of cloud platform service system; Feedback input module, used for feedback input management of data information.