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WO2018001004A1 - Procédé et appareil de commande de plateforme en nuage de docker - Google Patents

Procédé et appareil de commande de plateforme en nuage de docker Download PDF

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Publication number
WO2018001004A1
WO2018001004A1 PCT/CN2017/085626 CN2017085626W WO2018001004A1 WO 2018001004 A1 WO2018001004 A1 WO 2018001004A1 CN 2017085626 W CN2017085626 W CN 2017085626W WO 2018001004 A1 WO2018001004 A1 WO 2018001004A1
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WIPO (PCT)
Prior art keywords
host
cloud platform
information
application information
container
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Ceased
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PCT/CN2017/085626
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English (en)
Chinese (zh)
Inventor
童遥
彭勇
孙自广
蒋天超
申光
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ZTE Corp
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ZTE Corp
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
    • H04L65/40—Support for services or applications

Definitions

  • the present invention relates to the field of cloud platform technologies in cloud computing data, and in particular, to a Docker-based cloud platform control method and apparatus.
  • PaaS Platform as a Services
  • application developers can easily host applications and services on the platform without concern for infrastructure settings such as their underlying hardware configuration and operating environment.
  • PaaS greatly reduces the overhead and pain of IT deployment, providing resources to applications on demand to make them more scalable.
  • developers can focus on application innovation without worrying about the cumbersome tasks of the operating environment, so that applications on the Internet will become increasingly rich.
  • Google and Sina Internet have launched their own PaaS cloud platform.
  • Google has launched the Google App Engine (GAE) cloud service, providing application developers with a web application running and management platform that is simple to develop, easy to deploy, and scalable.
  • GAE Google App Engine
  • Sina's Sina App Engine (SAE) is a distributed Web service development and operation platform.
  • SAE chose PHP, the most popular web development language in China, as its preferred support language. Provides a range of basic capabilities such as distributed storage, distributed caching, etc. for developers to use.
  • Sina and Google PaaS cloud platform still has shortcomings, that is, the application environment is single, SAE only supports PHP applications in the initial stage of launch, GAE only supports Java and Python applications, which leads users to expand.
  • the issue of compatibility is not conducive to the rapid expansion and versatility of the PaaS platform, and can not meet the diversified needs of application providers. Therefore, it is imperative to improve the usability and scalability of the platform.
  • the Docker-based cloud platform control method and apparatus provided by the embodiments of the present invention are used to solve the problem that the PaaS cloud platform application in the prior art can only operate in a specific running environment, and the PaaS platform cannot achieve rapid expansion and versatility. technical problem.
  • an embodiment of the present invention provides a Docker-based platform control method, including:
  • the resource deployment request is a dynamic scheduling request
  • the load information of each host on the cloud platform and the application information that needs to be scheduled are acquired according to the resource deployment request, where the application information is application information on a host with the highest load information.
  • an embodiment of the present invention further provides a Docker-based platform control apparatus, including:
  • a receiving module configured to receive a resource deployment request input by a user
  • the resource scheduling module is configured to deploy the copy of the application information that needs to be scheduled to the host.
  • an embodiment of the present invention further provides a Docker-based platform control apparatus, including: a resource scheduling subunit and a platform branch unit;
  • the platform support unit is configured to receive a resource deployment request input by a user
  • the resource scheduling unit is configured to: when the resource deployment request is a dynamic scheduling request, select a host with the lowest load information according to load information of each host on the cloud platform, and deploy a copy of the application information that needs to be scheduled on the host.
  • the application information is application information on a host with the highest load information.
  • the embodiment of the invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions are used to execute the foregoing Docker-based platform control method.
  • a Docker-based cloud platform control method, apparatus, and computer storage medium includes receiving a resource deployment request input by a user, and when the resource deployment request is a dynamic scheduling request, according to the resource deployment request Obtaining load information of each host on the cloud platform and application function information to be scheduled, selecting a host with the lowest load information according to a preset scheduling policy, and deploying a copy of the application information on the host; The load information redeploys the application information and adjusts the application information from the high-load host to the low-load host, thereby realizing the expansion of the host application and the reasonable allocation of the cloud platform resources, thereby improving the availability of the cloud platform.
  • FIG. 1 is a schematic structural diagram of a Docker-based cloud platform control apparatus according to a first embodiment of the present invention
  • FIG. 2 is another schematic structural diagram of a Docker-based cloud platform control apparatus according to a first embodiment of the present invention
  • FIG. 3 is still another schematic structural diagram of a Docker-based cloud platform control apparatus according to a first embodiment of the present invention
  • FIG. 4 is a flowchart of a Docker-based cloud platform control method according to a second embodiment of the present invention.
  • FIG. 1 is a schematic structural diagram of a Docker-based cloud platform control apparatus according to an embodiment.
  • the Docker-based cloud platform control apparatus 1 includes a receiving module 11, an obtaining module 12, a selecting module 13, and a resource scheduling module 14, wherein:
  • the receiving module 11 is configured to receive a resource deployment request input by a user, where the resource deployment request includes a first scheduling request and a dynamic scheduling request;
  • the obtaining module 12 is configured to acquire load information of each host on the cloud platform and application information that needs to be scheduled according to the resource deployment request when the resource deployment request is a dynamic scheduling request;
  • the selecting module 13 is configured to select a host with the lowest load information according to a preset scheduling policy
  • the resource scheduling module 14 is configured to deploy a copy of the application information that needs to be scheduled to the host.
  • the infrastructure creation of the application is first required, and the infrastructure for creating the application can directly create or configure the infrastructure by inputting the resource deployment request.
  • the resource scheduling module 14 acquires the physical server on the host according to the resource deployment request, and filters the physical server according to the request policy, and rejects the host. The physical server is used, and then the resource is allocated for the host according to a preset scheduling policy.
  • the obtaining module 12 acquires load information of each host on the cloud platform according to the resource deployment request, and the load information may be an available physical server of the host, and It may be application information of a container set on the physical server, and the selection module 13 selects the lowest and highest load information according to the load information, and then deploys the application in the host with the highest load information to the host with the lowest load information. Specifically, the application is deployed in a container on the host with the lowest load information, and a copy of the application is executed through the container, thereby implementing scheduling of the application.
  • the cloud platform control device 1 provided in this embodiment further sets a general container. 15.
  • the universal container 15 is a Docker container, which can automatically create a corresponding application running infrastructure and IT resources according to the deployment request, without requiring the user to manually redeploy. Specifically, the universal container 15 performs the container according to different requests. Encapsulate and package a variety of different web containers, such as Docker-Jetty, Docker-Tomcat, Docker-PHP5, and more.
  • a routing unit 16 is provided on the cloud platform control device 1, and the routing unit 16 is configured to perform all the received scheduling requests. Routing, re-adjusting the IP correspondence between the application and the container.
  • FIG. 2 another schematic structural diagram of the Docker-based cloud platform control apparatus 1 provided in this embodiment includes a platform support unit 21 and a resource scheduling unit 22, and the platform supports
  • the unit 21 is configured to receive a resource deployment request input by the user
  • the resource scheduling unit 22 is configured to: when the resource deployment request is a dynamic scheduling request, select a host with the lowest load information according to load information of each host on the cloud platform, and A copy of the application information that needs to be scheduled is deployed on the host.
  • the load information includes load information of the container
  • the resource scheduling unit 22 is configured to select a container with the lowest load information from the host according to the preset scheduling policy, and on the container. Deploy a copy of the application information.
  • the cloud platform control apparatus 1 provided by this embodiment further includes a routing control unit 23 and a general container setting unit 24;
  • the universal container setting unit 24 is configured to set, on the host, a general container for running a copy of the application information, where the universal container is a container including at least one programming language running environment;
  • the routing control unit 23 is configured to adjust the route of the application information to a container corresponding to the copy of the application information after the resource scheduling unit deploys the copy of the application information.
  • the scheduling of the application is performed according to the load information of each host on the cloud platform
  • the resource scheduling unit 22 further includes determining, according to the acquired load information of the host on the cloud platform, whether the load is in a high load state, and if so, The alarm information is sent; if not, the application information on the host with lower load information is deployed to other hosts, and the application information on the host is deleted.
  • each module in FIG. 2 can also communicate with each other by setting a message bus, and the specific connection relationship is as shown in FIG. 3.
  • the platform support unit 21 provides some basic modules for the cloud platform in addition to receiving the request, and provides some basic capabilities for the cloud platform, such as setting the API Server 211 (Application Programming Interface Server, server application program interface).
  • the API Server 211 is used as an external interface of the entire device, receives the request through the interface, and sends the received request to the resource scheduling unit 22, and the server application interface 211 exposes it by abstracting the restful-style HTTP interface.
  • the user is convenient to call the interface to implement the request receiving and reasonable resource allocation; in addition, the platform supporting unit 21 is also provided with Memcached, which provides session sharing between different containers, and the Cache provides a high performance distributed.
  • Object caching service Mysql provides a native relational database
  • Freedisk provides a small file storage service.
  • the resource scheduling unit 22 serves as a resource scheduling core of the cloud platform, and is mainly used for reasonably allocating IT resources and infrastructure provided by the cloud platform according to the request, specifically, for running the application.
  • the container is deployed and operated on the host.
  • the resource deployment request includes a first scheduling request and a dynamic scheduling request, where the initial scheduling request is a scheduling of resources in the cloud platform when the user first creates an application, including The creation, deletion, modification, and other operations of the resource are created to set the corresponding running environment infrastructure and allocate IT resources for the newly added application; deleting refers to shrinking the container, and scheduling the application in the container with lower load information to After the other containers are deleted, the original container is deleted; the modification refers to reallocating other resources such as IT resources to the container according to the scheduling request.
  • the dynamic scheduling request is a scheduling in the running process of the system, and the scheduling is performed on a host or a container that is too high or too low in the running process, mainly by monitoring the state of the application and the state of the container, according to the monitoring.
  • the actual situation expands or shrinks the application.
  • the resource scheduling unit 22 specifically includes a primary resource scheduler 221, a dynamic resource scheduler 222, and a node agent 223, where
  • the initial resource scheduler 221 implements the transfer and coordination of all initial resource operation requests.
  • the implementation manner is divided into two steps.
  • the first step is to take out all the physical servers, and use different filters (filters) to filter the physical servers for different request policies. , get a list of available servers, the purpose of this step is to ensure the availability of the host.
  • the second step is to introduce the weight and scoring concept.
  • the initial resource scheduler calculates the score of each host by different weights, then sorts the host according to the score from high to low, and deploys multiple copies of the application (the default is 2) to On a host with a high score.
  • the dynamic resource scheduler 222 implements dynamic scheduling, dynamically increasing and reducing the number of application backend containers. After the dynamic resource scheduler obtains the load information of the host and the load information of the Docker container, the application copy expansion is completed according to the following policy: the host with the lowest load is selected according to the corresponding scheduling policy, and a copy of the application is deployed on the host, and the route is simultaneously routed. The subsystem sends a request to modify the direction of the route so that the newly added application replica can receive the request, thereby implementing load balancing. When all hosts in the cluster are close to a high load state, the expansion of the application replica is stopped and an alert message is sent to the administrator.
  • the application copy shrinks according to a certain strategy.
  • regular defragmentation is added to ensure multiple applications of the same application. Under the premise that the replicas are distributed on different hosts, try to distribute the distributed applications to some hosts.
  • the node agent 223 runs on each computing node as a resident process, and periodically reports heartbeat data to the primary resource scheduler.
  • the heartbeat data includes information such as CPU utilization, memory usage, and remaining space of the hard disk.
  • it adapts the underlying Docker container and obtains the load data of each container on it, including CPU usage, memory usage, hard disk read and write status, and so on.
  • the routing unit 24 includes a software router 241 (App Router), an Nginx controller 242 (Ngnix Controller), and a Tengine, where:
  • the software router 241 is the core control component of the PaaS cloud platform routing. After determining that the application on a host needs to be scheduled, and after setting up the container, the routing direction of the application needs to be adjusted, and the component is responsible for maintaining a copy. A routing table containing all the application routing policies, and controlling the Nginx to complete the routing of the application access request according to a specific task scheduling algorithm. The routing for implementing the above functions is mainly performed in two steps. The first step is to externally domain name to internal according to the routing request. The routing of the temporary domain name, that is, the IP address of the external IP to the host and the IP of the host to the physical server.
  • the second step is the routing of the internal temporary domain name to the container, that is, the IP address from the physical server to the container IP, providing internal temporary
  • the domain name can also be used to facilitate application developers to check and confirm the application after submitting the application, thereby implementing load balancing, health check, session sticking and the like among multiple application copies.
  • the software router 241 before performing routing, the software router 241 further includes determining the validity of the request, determining whether the routing table needs to be updated after determining the legality, and notifying the Ngnix Controller (controller) to update the routing table if necessary. operating.
  • the Nginx controller 242 loads the most from the database after receiving the notification of the updated routing information of the software router 241.
  • New routing information, and the corresponding configuration file is generated by Java's Velocity technology, replacing the current Nginx configuration file and restarting Nginx, so that the routing information takes effect in time.
  • Tengine Based on Nginx, Tengine adds advanced features and features to the needs of large-volume websites, such as consistent hashes, session persistence, health checks, and automatic offline and offline based on server status.
  • the universal container 23 is configured to set a running environment container for an application, and the general container 23 performs different deployment allocation according to different requests, according to a corresponding writing language and a compatible language of the application.
  • the Web container is re-encapsulated.
  • the Java application can only be run on the Java container.
  • the general container 23 is used to implement the fast package to provide the corresponding container, which improves the compatibility of the PaaS platform.
  • the general container 23 provided in this embodiment can specifically abstract two layers, which are an adaptation layer and a container layer respectively.
  • the adaptation layer can collect information such as the host load and the like as a resident node (that is, a node agent). Aspects can be adapted to the underlying container.
  • the container layer encapsulates different web containers based on Docker Api, such as Docker-Jetty, Docker-Tomcat, Docker-PHP5, and so on.
  • Docker Api such as Docker-Jetty, Docker-Tomcat, Docker-PHP5, and so on.
  • the embodiment of the present invention also introduces a centralized image management module of the Image Server, which facilitates the release, replacement, and deletion of the image.
  • the Docker-based cloud platform control device receives the resource deployment request input by the user through the receiving module.
  • the acquiring module acquires load information of each host of the cloud platform according to the resource deployment request.
  • the selection module selects the host with the lowest load information according to the preset scheduling policy, and deploys a copy of the application information on the host, and deploys the application information on the host with the highest load information to the host with the lowest load information, so that the application information is obtained. It can be scheduled between multiple hosts, which realizes the expansion and availability of the cloud platform, and also improves the utilization of cloud platform resources.
  • the embodiment of the present invention further provides a corresponding container for the scheduled application information, so that the application information can be normally operated after being scheduled, and the PaaS cloud platform application in the prior art can only be run on a specific host.
  • the universal container can implement container packaging in a variety of programming languages, providing corresponding containers for different application information, thereby improving the compatibility and scalability of the PaaS cloud platform.
  • FIG. 4 is a Docker-based cloud platform control method according to an embodiment.
  • the Docker is an open source application container engine, which allows developers to package their applications and dependencies into a portable container. And then released to any popular LINUX machine, can also achieve virtualization, the control process specifically includes the following steps:
  • the resource deployment request received by the cloud platform control device includes a primary scheduling request or a dynamic scheduling request.
  • the received request should be the initial scheduling request
  • the cloud platform control device should Allocating IT resources and setting up the infrastructure according to the initial scheduling request is implemented in two steps.
  • the first step is to take out all the physical servers and use different filters (filters) to filter the physics for different request policies.
  • the server gets a list of available servers.
  • the purpose of this step is to ensure the availability of the host.
  • the second step is to introduce weights and scoring concepts. Resource Schedule calculates the score of each host by different weights, then sorts the host's score from high to low, and deploys multiple copies of the application (default is 2 ) to the host with high scores.
  • the cloud platform control device When receiving a dynamic scheduling request, the cloud platform control device should dynamically increase the number of applications and reduce the number of application backend containers according to the request.
  • Request request to modify the direction of the route, so that the newly added application copy can receive the request, thereby achieving load balancing.
  • the expansion of the application replica is stopped and an alert message is sent to the administrator.
  • the application copy shrinks according to a certain strategy.
  • regular defragmentation is added to ensure multiple applications of the same application. Under the premise that the replicas are distributed on different hosts, try to distribute the distributed applications to some hosts.
  • the load information acquired by the cloud platform control device may specifically be load information of the container, where the load information may be understood as application information; and the load is selected from the host according to the preset scheduling policy.
  • the load information obtained by the cloud platform control device may specifically be the load information of the physical server on the host, where the load information may be understood as an application container; and the load information is selected from the host according to the preset scheduling policy.
  • the lowest physical server and a copy of the application information is deployed on the physical server.
  • the method further includes: setting, on the host, a general-purpose container for running a copy of the application information, and sending a routing request Orienting the application information to a container corresponding to a copy of the application information, where the universal container is a container including at least one programming language running environment, so that the scheduled application can run normally, thereby implementing the PaaS cloud. Compatibility between different applications of the platform and the container.
  • the Docker-based cloud platform control method and apparatus includes receiving a resource deployment request input by a user, and when the resource deployment request is a dynamic scheduling request, according to the resource deployment request.
  • the load information redeploys the application information and adjusts the application information from the high-load host to the low-load host, thereby realizing the expansion of the host application and the reasonable allocation of the cloud platform resources, thereby improving the availability of the cloud platform.
  • the application information is run in different operating environments, which solves the problem that the application information in the prior art can only be operated in a single operating environment, resulting in poor compatibility.
  • modules or steps of the above embodiments of the present invention can be implemented by a general computing device, which can be concentrated on a single computing device or distributed among multiple computing devices.
  • they may be implemented by program code executable by the computing device, such that they may be stored in a computer storage medium (ROM/RAM, disk, optical disk) by a computing device, and at some In some cases, you can
  • ROM/RAM, disk, optical disk a computer storage medium
  • you can The steps shown or described are performed in a different order than that herein, or they are separately fabricated into individual integrated circuit modules, or a plurality of modules or steps thereof are fabricated as a single integrated circuit module. Therefore, the invention is not limited to any particular combination of hardware and software.
  • the present disclosure is applicable to the cloud platform technical field in cloud computing data, to implement extension of the host application and reasonable allocation of the cloud platform resources, improve the availability of the cloud platform, and realize the application information running in different operating environments, and solve the present problem.
  • application information can only be run in a single operating environment, resulting in poor compatibility.

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Abstract

L'invention concerne un procédé et un appareil de commande de plateforme en nuage de Docker. Le procédé consiste : à recevoir une requête de déploiement de ressource entrée par un utilisateur (S402) ; lorsque la requête de déploiement de ressource est une requête de planification dynamique, conformément à la requête de déploiement de ressource, à acquérir des informations de charge concernant chaque hôte sur la plateforme en nuage et des informations d'application ayant besoin d'être planifiées (S404) ; et selon une politique de planification prédéfinie, à sélectionner un hôte avec les informations de charge minimales, et à déployer une copie des informations d'application ayant besoin d'être planifiées sur l'hôte (S406). En redéployant des informations d'application selon des informations de charge sur chaque hôte, et en réglant les informations d'application d'un hôte ayant une charge élevée à un hôte ayant une faible charge, l'extension d'une application d'hôte et l'attribution raisonnable de ressources de plateforme en nuage sont réalisées, ce qui permet ainsi d'améliorer la disponibilité de la plateforme en nuage, de réaliser l'exécution des informations d'application dans différents environnements d'exécution, et de résoudre le problème de mauvaise compatibilité provoqué par le fait que les informations d'application ne peuvent s'exécuter que dans un seul environnement d'exécution dans l'état de la technique.
PCT/CN2017/085626 2016-06-27 2017-05-24 Procédé et appareil de commande de plateforme en nuage de docker Ceased WO2018001004A1 (fr)

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CN201610482515.9A CN107547596B (zh) 2016-06-27 2016-06-27 一种基于Docker的云平台控制方法及装置
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