CN110764786A - Optimized deployment resource and software delivery platform in cloud computing environment - Google Patents
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Abstract
The invention discloses an optimized resource deployment and software delivery platform in a cloud computing environment, wherein the software delivery platform comprises a Server and a working end Worker, and the Server is responsible for calling a development software API and issuing a software delivery instruction and a monitoring instruction to the working end Worker; the Server at the Server is provided with API service, Docker maven plugin, Gitlab, Jenkins, Grafana, Prometheus, Harbor, Haproxy, Kubernetes, Keepalived and DataBase storage module DataBase. The resource utilization rate is improved and the configuration is simplified under the cloud computing environment; the cost expenditure is reduced, the investment of operation and maintenance personnel is reduced, and the continuous delivery of the service is more, faster, better, more and more stable. And realizing the service horizontal extension.
Description
Technical Field
The invention relates to a software delivery platform, in particular to a resource optimized deployment and software delivery platform in a cloud computing environment.
Background
With the increasing robustness and maturity of the cloud computing technology, the cloud computing technology is a new hotspot for the development of the IT industry in recent years and is widely concerned by all parties; the concept of cloud computing has been proposed for several years, and many technologically advanced enterprises have adopted virtualization technology to provide a large amount of virtual computing resources and storage resources for users; under the promotion of computer virtualization technology, computing resources including virtual machines in cloud computing are increased rapidly, and dynamic deployment software on a large number of computing resources is generally needed to construct different computing environments to meet the changing requirements of users.
Most of the traditional resource division methods are resource allocation after the virtual machines and the services are integrated, with the expansion of the services, more and more computing resources need to be invested, the number of the virtual machines is increased, the personnel maintenance cost is increased, and the software delivery cost is increased sharply. The traditional method of delivering software on line by depending on operation and maintenance engineers is unrealistic, the efficiency is too low, the error rate is extremely high, a large amount of manpower is consumed, and the delivery version of the software is not easy to control. Problems occur and tracking and tracing cannot be achieved.
Disclosure of Invention
The invention aims to provide a platform for optimizing and deploying resources and delivering software in a cloud computing environment, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: an optimized deployment resource and software delivery platform in a cloud computing environment comprises: the system comprises a Server and a working end Worker, wherein the Server is responsible for calling a development software API and simultaneously issuing a software delivery instruction and a monitoring instruction to the working end Worker; the Server at the Server is deployed with APIservice, Dockermaven plugin, Gitlab, Jenkins, Grafana, Prometheus, Harbor, Haproxy, Kubernetes, Keepaived and DataBase storage module DataBase.
Wherein the API service provides configuration services: the method comprises the steps of automatic testing, binary package application construction, mirror image creation and uploading to Harbor, Kubernetes for downloading mirror images, triggering deployment and the like, receiving delivery results fed back by a Worker at a working end, recording a process into a database, and entering a 7 x 24 monitoring flow after delivery is completed;
the Docker maven plugin is an open source tool plugin and is used for Docker file automatic generation and automatic Docker mirror image manufacturing. In the continuous delivery process, the system generally uses a Docker maven plugin to edit and pack, generates a mirror image and pushes the mirror image to a mirror image warehouse Harbor.
Wherein, the Gitlab is a warehouse for daily management and storage of source codes, is an open source project for a warehouse management system, and is distributed version control software. Further, the Gitlab is divided into a plurality of warehouses according to the Group.
The Jenkins are responsible for job task construction and shell script execution, basic mirror images or service mirror images are uploaded to a mirror image warehouse Harbor through a Docker maven plugin, and the mirror images are automatically updated to be run in Kubernets.
Wherein Grafana and Prometoxus are open source tools used for monitoring container environment and deployment resources.
The Prometheus is used for collecting and storing time series data, generating corresponding indexes for services needing to be monitored, exposing the indexes to the outside, receiving the index data and sending an alarm. Prometheus has few functions of a self-contained UI interface, the visual display function is incomplete, and the daily monitoring requirement cannot be met, so that the visual data display is often required to be combined with a Prometheus + Grafana mode.
Grafana is a cross-platform open-source measurement analysis and visualization tool, and can visually customize alarm rules for the most important indexes and notify the most important indexes in time by inquiring and then visually displaying data collected by Prometoeus.
Wherein the Harbor is a mirror image warehouse used for storing the basic mirror images and the business mirror images. Harbor is an enterprise-level warehouse server for storing and distributing Docker images, and as an enterprise-level private warehouse server, Harbor provides better performance and security. The efficiency of the user to use the warehouse to construct and run the environment transmission mirror image is improved. And the Harbor supports the mirror image resource replication installed at a plurality of warehouse nodes, and all mirror images are stored in a private warehouse, so that the data and service mirror images are controlled in an internal network.
Among them, Haproxy is a free and open source software written in C language that provides high availability, load balancing, and TCP and HTTP based application proxies. Haproxy combines Keepalived for load balancing and high availability of Kubernetes master node access.
Kubernetes is a container orchestration engine for Google open sources that supports automated deployment, large-scale scalable, application containerization management. The Kubernetes is used for managing the containers, a series of complete functions such as deployment and operation, resource scheduling, service discovery, dynamic expansion and the like are provided for containerized application, and convenience in large-scale container cluster management is improved.
The method comprises the steps that the Keepalived is used for being combined with Haproxy to achieve high available load balance of a Harbor, the Keepalived is used for detecting the state of a server, if one Harbor server is down or works in a fault, the Keepalived is detected, the faulty server is removed from a system, meanwhile, other servers are used for replacing the server, the Keepalived automatically adds the server into a server group after the server works normally, all the works are completed automatically, manual interference is not needed, and only the faulty server is repaired manually.
The DataBase is responsible for storing resource distribution and management logs, and comprises information such as the position of a binary software package, the manufacturing process of a manufactured mirror image and the like, change content, change time and the like.
The working end Worker is responsible for receiving an instruction of the Server, the mirror image is pushed to an open source tool Harbor after being manufactured by the open source tool, and the Kubernetes cluster deploys the mirror image into the cluster by pulling the latest service mirror image.
The invention also discloses a method for optimizing the automatic delivery of the deployment resources and the software delivery platform in the cloud computing environment, which comprises the following specific steps:
step one, building a Server, and deploying API service, Docker maven plugin, Gitlab, Jenkins, Grafana, Prometheus, Harbor, Haproxy, Kubernetes, Keepallved and a DataBase storage module DataBase;
secondly, the API service creates and integrates a job task corresponding to the project continuous delivery Jenkins according to the requirement;
step three, building a working end Worker and deploying the working end Worker;
step four, a user adds a link of a Gitlab warehouse address corresponding to a project into configuration according to a configured format, and automatically configures a job task in Jenkins, if the job task runs, the Jenkins can select the job task of a corresponding node to construct, execute a corresponding shell script, perform automatic testing, application construction, manufacture of a service mirror image, push the service mirror image to a mirror image warehouse Harbor, update a configuration file of the service to update the mirror image to a container cloud, and simultaneously store configuration information corresponding to the project into a DataBase DataBase; after the construction is completed, the log is recorded and the service is added into the monitoring;
and step five, after receiving the construction result of the working end, the API service records the construction log into the DataBase.
Further, in the fourth step, after the update is completed, Prometheus is responsible for logging, collecting and monitoring, and Grafana is responsible for graphically displaying data of indexes related to Prometheus collection.
The working process of optimizing the deployment resources and the software delivery platform in the cloud computing environment comprises the following steps:
the job task of Jenkins is automatically generated after API service is configured, the job task is configured and triggered according to requirements, at the moment, a Worker receiving Server instruction of a working end pulls a source code of an automatic case detection system in Gitlab, the source code is constructed through automatic testing and binary package application, a Docker universal plug-in is used for creating a mirror image and uploading the mirror image to a mirror image warehouse Harbor, then a shell script is automatically executed, and updating of a Kubernets service mirror image is automatically completed. And after the updating is finished, the Prometous automatically detects the service mirror image and stores the index data into the database according to the running condition, and the Grafana automatically and fully displays the index data in the Grafana by reading the index data provided by the Prometous through a graphic template.
Compared with the prior art, the invention has the beneficial effects that: the resource utilization rate is improved and the configuration is simplified under the cloud computing environment; the IT cost expenditure is reduced, the investment of operation and maintenance personnel is reduced, and the continuous delivery of the service is more, faster, better, more and more stable. At present, the service cannot be expanded transversely rapidly, a virtual machine needs to be created, and then a program and a code are deployed to provide service to the outside; the virtual machine can not be self-cured after the fault occurs, and high availability is realized depending on the load; the solution is as follows: the container is used for replacing a virtual machine, the integration of the service code and the container mirror image is realized, the purpose of quickly starting different service containers on any host is achieved, and the service transverse expansion is quickly realized. The Kubernetes is used for container management, a series of complete functions such as deployment and operation, resource scheduling, service discovery, dynamic expansion and the like are provided for containerized application on the basis of the Docker technology, and convenience of large-scale container management is improved.
Drawings
FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a flow chart of an automated delivery method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example (b):
an optimized deployment resource and software delivery platform in a cloud computing environment comprises: the system comprises a Server and a working end Worker, wherein the Server is responsible for calling a development software API and simultaneously issuing a software delivery instruction and a monitoring instruction to the working end Worker; the Server at the Server is deployed with API service, Dockermaven plugin, Gitlab, Jenkins, Grafana, Prometheus, Harbor, Haproxy, Kubernetes, Keepaived and DataBase storage module DataBase.
Wherein the API service provides configuration services: the method comprises the steps of automatic testing, binary package application construction, mirror image creation and uploading to Harbor, Kubernetes for downloading mirror images, triggering deployment and the like, receiving delivery results fed back by a Worker at a working end, recording a process into a database, and entering a 7 x 24 monitoring flow after delivery is completed;
the Docker maven plugin is an open source tool plugin and is used for Docker file automatic generation and automatic Docker mirror image manufacturing. In the continuous delivery process, the system generally uses a Docker maven plugin to edit and pack, generates a mirror image and pushes the mirror image to a mirror image warehouse Harbor.
Wherein, the Gitlab is a warehouse for daily management and storage of source codes, is an open source project for a warehouse management system, and is distributed version control software. Further, the Gitlab is divided into a plurality of warehouses according to the Group.
The Jenkins are responsible for job task construction and shell script execution, basic mirror images or service mirror images are uploaded to a mirror image warehouse Harbor through a Docker maven plugin, and the mirror images are automatically updated to be run in Kubernets.
Wherein Grafana and Prometoxus are open source tools used for monitoring container environment and deployment resources.
The Prometheus is used for collecting and storing time series data, generating corresponding indexes for services needing to be monitored, exposing the indexes to the outside, receiving the index data and sending an alarm. Prometheus has few functions of a self-contained UI interface, the visual display function is incomplete, and the daily monitoring requirement cannot be met, so that the visual data display is often required to be combined with a Prometheus + Grafana mode.
Grafana is a cross-platform open-source measurement analysis and visualization tool, and can visually customize alarm rules for the most important indexes and notify the most important indexes in time by inquiring and then visually displaying data collected by Prometoeus.
Wherein the Harbor is a mirror image warehouse used for storing the basic mirror images and the business mirror images. Harbor is an enterprise-level warehouse server for storing and distributing Docker images, and as an enterprise-level private warehouse server, Harbor provides better performance and security. The efficiency of the user to use the warehouse to construct and run the environment transmission mirror image is improved. And the Harbor supports the mirror image resource replication installed at a plurality of warehouse nodes, and all mirror images are stored in a private warehouse, so that the data and service mirror images are controlled in an internal network.
Among them, Haproxy is a free and open source software written in C language that provides high availability, load balancing, and TCP and HTTP based application proxies. Haproxy combines Keepalived for load balancing and high availability of Kubernetes master node access.
Kubernetes is a container orchestration engine for Google open sources that supports automated deployment, large-scale scalable, application containerization management. The Kubernetes is used for managing the containers, a series of complete functions such as deployment and operation, resource scheduling, service discovery, dynamic expansion and the like are provided for containerized application, and convenience in large-scale container cluster management is improved.
The method comprises the steps that the Keepalived is used for being combined with Haproxy to achieve high available load balance of a Harbor, the Keepalived is used for detecting the state of a server, if one Harbor server is down or works in a fault, the Keepalived is detected, the faulty server is removed from a system, meanwhile, other servers are used for replacing the server, the Keepalived automatically adds the server into a server group after the server works normally, all the works are completed automatically, manual interference is not needed, and only the faulty server is repaired manually.
The DataBase is responsible for storing resource distribution and management logs, and comprises information such as the position of a binary software package, the manufacturing process of a manufactured mirror image and the like, change content, change time and the like.
The working end Worker is responsible for receiving an instruction of the Server, the mirror image is pushed to an open source tool Harbor after being manufactured by the open source tool, and the Kubernetes cluster deploys the mirror image into the cluster by pulling the latest service mirror image.
The working process of optimizing the deployment resources and the software delivery platform in the cloud computing environment comprises the following steps:
the job task of Jenkins is automatically generated after API service is configured, the job task is configured and triggered according to requirements, at the moment, a Worker receiving Server instruction of a working end pulls a source code of an automatic case detection system in Gitlab, the source code is constructed through automatic testing and binary package application, a Docker maven plugin opens a source software plugin to create a mirror image and uploads the mirror image to a mirror image warehouse Harbor, then a shell script is automatically executed, and updating of a Kubernets service mirror image is automatically completed. And after the updating is finished, the Prometous automatically detects the service mirror image and stores the index data into the database according to the running condition, and the Grafana automatically and fully displays the index data in the Grafana by reading the index data provided by the Prometous through a graphic template.
A method for optimizing automated delivery of deployment resources and a software delivery platform in a cloud computing environment comprises the following specific steps:
step one, building a Server, and deploying API service, Docker maven plugin, Gitlab, Jenkins, Grafana, Prometheus, Harbor, Haproxy, Kubernetes, Keepallved and a DataBase storage module DataBase;
secondly, the API service creates and integrates a job task corresponding to the project continuous delivery Jenkins according to the requirement;
step three, building a working end Worker and deploying the working end Worker;
step four, a user adds a link of a Gitlab warehouse address corresponding to a project into configuration according to a configured format, and automatically configures a job task in Jenkins, if the job task runs, the Jenkins can select the job task of a corresponding node to construct, execute a corresponding shell script, perform automatic testing, application construction, manufacture of a service mirror image, push the service mirror image to a mirror image warehouse Harbor, update a configuration file of the service to update the mirror image to a container cloud, and simultaneously store configuration information corresponding to the project into a DataBase DataBase; after the updating is finished, the Prometheus is responsible for log recording, collecting and monitoring, and the Grafana is responsible for graphically displaying data of indexes related to Prometheus collection;
and step five, after receiving the construction result of the working end, the API service records the construction log into the DataBase.
And (3) comparing the actual application effects:
the optimized deployment resource and software delivery platform in the cloud computing environment is used for processing resource division services of insurance claim wind control enterprises, and compared with the existing case automatic detection system, the result shows that:
the existing automatic case detection system needs manual work to perform testing work such as function testing, performance testing, smoking testing and the like, needs to be deployed online in a virtual machine manual creation mode, cannot rapidly and transversely expand services, needs to create a virtual machine, deploys program codes, can provide services externally, and is time-consuming and labor-consuming. And the virtual machine failure can not be self-cured, and high availability is realized depending on load. The operation and maintenance labor cost is high, time and labor are consumed, and mistakes are easy to make.
The method and the platform of the application use the container to replace a virtual machine, realize the integration of the case automatic detection system code and the container mirror image, achieve the purpose of quickly starting the container at any host machine, and quickly realize the transverse extension of the case automatic detection system. The efficiency is improved by about 100 percent on the same scale, and at least 70 percent of the time for preparing the transversely-extended infrastructure is saved. Meanwhile, the existing resources are utilized to the maximum, and the resources are automatically recycled. The Kubernetes is used for managing the containers, a series of functions such as deployment operation, resource scheduling, service discovery, dynamic expansion and the like are provided for the containerized case automatic detection system and the auxiliary function modules thereof on the basis of the container technology, and the convenience of large-scale container cluster management is improved. The dynamic flexible expansion and contraction is more time-saving and time-saving with the operation and maintenance intervention (no additional preparation of virtual machine deployment codes is needed), labor cost (no operation and maintenance intervention is needed, automatic expansion and contraction is carried out), and resource cost (the virtual machine is not beneficial to resource recycling). At present, one node submits about 10s to 15s, manual intervention deployment codes to a virtual machine in the same operation and maintenance process are needed, at least 3-5 minutes and 180s are needed for deploying 1 node, and the efficiency improvement effect is obvious.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A platform for optimizing and deploying resources and delivering software in a cloud computing environment is characterized in that: the software delivery platform comprises a Server and a working end Worker, wherein the Server is responsible for calling a development software API and simultaneously issuing a software delivery instruction and a monitoring instruction to the working end Worker; the Server at the Server is provided with API service, Dockermaven plugin, Gitlab, Jenkins, Grafana, Prometheus, Harbor, Haproxy, Kubernetes, Keepaived and a DataBase storage module DataBase;
the API service provides configuration service, receives delivery results fed back by a Worker at a working end, records the process into a database, and enters a 7 × 24 monitoring flow after delivery is completed;
the Docker maven plugin is an open source tool plugin and is used for automatic Docker file generation and automatic Docker mirror image manufacturing;
the Gitlab is a warehouse for daily management and code storage of the source code;
the Jenkins are responsible for job task construction and shell script execution;
the Grafana and Prometoxus are open source tools and are used for monitoring container environment and deployment resources;
the Harbor is a mirror image warehouse and is used for storing the basic mirror images and the business mirror images;
the Haproxy is combined with keepalive and used for load balancing and high availability of Kubernets main node access;
the Kubernetes is an open-source container arrangement engine and supports automatic deployment, large-scale scalability and application containerization management;
the DataBase is responsible for storing resource allocation and management logs;
and the working end Worker is responsible for receiving an instruction of the Server, pushing the mirror image to an open source tool Harbor after the mirror image is manufactured by using the open source tool, and deploying the latest service mirror image to the Kubernets by pulling the latest service mirror image.
2. The platform for optimized deployment of resources and delivery of software in a cloud computing environment according to claim 1, wherein: the configuration service provided by the API service comprises automatic testing, binary package application construction, mirror image creation and uploading to Harbor and Kubernetes to download mirror images and trigger deployment.
3. The platform for optimized deployment of resources and delivery of software in a cloud computing environment according to claim 1, wherein: and the Gitlab takes Group as a unit and divides the Gitlab into a plurality of warehouses.
4. The platform for optimized deployment of resources and delivery of software in a cloud computing environment according to claim 1, wherein: and uploading the basic mirror image or the service mirror image to a mirror image warehouse Harbor through a Docker maven plugin, and simultaneously automatically updating the mirror image to operate in Kubernetes.
5. The platform for optimized deployment of resources and delivery of software in a cloud computing environment according to claim 1, wherein: the DataBase storage content comprises a binary software package position, a change content and a change time of a manufacturing process of the manufactured mirror image.
6. The optimized deployment resource and software delivery in cloud computing environment as claimed in claim 1
Platform, its characterized in that: the Harbor is an enterprise-level warehouse server used for storing and distributing Docker images.
7. The optimized deployment resource and software delivery in cloud computing environment as claimed in claim 1
Platform, its characterized in that: in the continuous delivery process, the Docker maven plugin is used for editing and packaging, generating a mirror image and pushing the mirror image to a mirror image warehouse Harbor.
8. The platform for optimized deployment of resources and delivery of software in a cloud computing environment according to claim 1, wherein: the Prometoxus is used for collecting and storing time sequence data, generating corresponding indexes for services needing to be monitored, exposing the indexes to the outside, receiving the index data and giving an alarm, and Grafana visually customizes alarm rules for the most important indexes by inquiring and then visually displaying the data collected by the Prometoxus and notifies the most important indexes in time.
9. The platform for optimized deployment of resources and delivery of software in a cloud computing environment according to claim 1, wherein: the Haproxy is free and open source code software written in C language, which provides high availability, load balancing, and TCP and HTTP based application proxies, Keepalived is used to detect the state of the server.
10. A method for optimizing deployment resources and delivering software in a cloud computing environment is characterized by comprising the following steps: the method is implemented by the platform of claim 1, and specifically comprises the steps of:
step one, building a Server, and deploying API service, Docker maven plugin, Gitlab, Jenkins, Grafana, Prometheus, Harbor, Haproxy, Kubernetes, Keepallved and a DataBase storage module DataBase;
secondly, the API service creates and integrates a job task corresponding to the project continuous delivery Jenkins according to the requirement;
step three, building a working end Worker and deploying the working end Worker;
step four, a user adds a link of a Gitlab warehouse address corresponding to a project into configuration according to a configured format, and automatically configures a job task in Jenkins, if the job task of the Jenkins runs, the Jenkins can select the job task of a corresponding node to construct, execute a corresponding shell script, perform automatic testing, application construction, manufacture and push of a service mirror image to a mirror image warehouse Harbor, update a configuration file of the service to update the mirror image to a container cloud, and simultaneously store configuration information corresponding to the project into a DataBase; after the construction is completed, the log is recorded and the service is added into the monitoring;
and step five, after receiving the construction result of the working end, the API service records the construction log into the DataBase.
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| CN111738689A (en) * | 2020-06-24 | 2020-10-02 | 北京首汽智行科技有限公司 | Demo project delivery method and system |
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| CN111897541A (en) * | 2020-08-03 | 2020-11-06 | 上海嗨酷强供应链信息技术有限公司 | Software interaction platform and method for automatically deploying resources in cloud environment |
| CN111935312A (en) * | 2020-09-21 | 2020-11-13 | 深圳蜂巢互联(南京)科技研究院有限公司 | Industrial Internet container cloud platform and flow access control method thereof |
| CN112084040A (en) * | 2020-09-28 | 2020-12-15 | 上海道客网络科技有限公司 | Container resource planning system and method based on application mirror image data identification |
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| CN113114482A (en) * | 2021-03-08 | 2021-07-13 | 中国—东盟信息港股份有限公司 | Container-based hybrid cloud management system and method |
| CN113238928A (en) * | 2021-04-23 | 2021-08-10 | 杭州电子科技大学 | End cloud collaborative evaluation system for audio and video big data task |
| CN113377342A (en) * | 2020-02-25 | 2021-09-10 | 武汉斗鱼鱼乐网络科技有限公司 | Project construction method and device, electronic equipment and storage medium |
| CN113467819A (en) * | 2021-09-06 | 2021-10-01 | 南京联迪信息系统股份有限公司 | Development operation and maintenance platform and implementation method thereof |
| CN113641480A (en) * | 2021-08-27 | 2021-11-12 | 四川中电启明星信息技术有限公司 | Task scheduling system and method based on Kubernetes cluster |
| CN113835827A (en) * | 2021-08-18 | 2021-12-24 | 微梦创科网络科技（中国）有限公司 | Application deployment method and device based on container Docker and electronic equipment |
| US11341025B2 (en) | 2020-05-27 | 2022-05-24 | Red Hat Inc. | Dynamic tuning of computing devices using application log data |
| CN115113909A (en) * | 2022-06-29 | 2022-09-27 | 北京永信至诚科技股份有限公司 | Project automation operation and maintenance method, system, electronic equipment and storage medium |
| CN115268929A (en) * | 2022-07-26 | 2022-11-01 | 成都智元汇信息技术股份有限公司 | Extremely simple operation and maintenance method supporting light delivery deployment |
| US11556332B2 (en) | 2021-02-23 | 2023-01-17 | International Business Machines Corporation | Application updating in a computing environment using a function deployment component |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105677356A (en) * | 2016-01-11 | 2016-06-15 | 上海雷腾软件股份有限公司 | Operation and maintenance method and device |
| CN106873975A (en) * | 2016-12-30 | 2017-06-20 | 武汉默联股份有限公司 | Devops based on Docker persistently pays and automated system and method |
| CN106888254A (en) * | 2017-01-20 | 2017-06-23 | 华南理工大学 | A kind of exchange method between container cloud framework based on Kubernetes and its each module |
| CN107562472A (en) * | 2017-08-24 | 2018-01-09 | 四川长虹电器股份有限公司 | Micro services system and method based on docker containers |
| CN109062655A (en) * | 2018-06-05 | 2018-12-21 | 腾讯科技（深圳）有限公司 | A kind of containerization cloud platform and server |
| CN109491674A (en) * | 2018-11-07 | 2019-03-19 | 李斌 | A kind of method and system of the automatic deployment service in Kubernetes cluster |
| CN109947452A (en) * | 2019-03-26 | 2019-06-28 | 南京联创信息科技有限公司 | A kind of Kubernetes container platform application update method |
-
2019
- 2019-12-27 CN CN201911372915.4A patent/CN110764786A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105677356A (en) * | 2016-01-11 | 2016-06-15 | 上海雷腾软件股份有限公司 | Operation and maintenance method and device |
| CN106873975A (en) * | 2016-12-30 | 2017-06-20 | 武汉默联股份有限公司 | Devops based on Docker persistently pays and automated system and method |
| CN106888254A (en) * | 2017-01-20 | 2017-06-23 | 华南理工大学 | A kind of exchange method between container cloud framework based on Kubernetes and its each module |
| CN107562472A (en) * | 2017-08-24 | 2018-01-09 | 四川长虹电器股份有限公司 | Micro services system and method based on docker containers |
| CN109062655A (en) * | 2018-06-05 | 2018-12-21 | 腾讯科技（深圳）有限公司 | A kind of containerization cloud platform and server |
| CN109491674A (en) * | 2018-11-07 | 2019-03-19 | 李斌 | A kind of method and system of the automatic deployment service in Kubernetes cluster |
| CN109947452A (en) * | 2019-03-26 | 2019-06-28 | 南京联创信息科技有限公司 | A kind of Kubernetes container platform application update method |
Cited By (27)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113377342A (en) * | 2020-02-25 | 2021-09-10 | 武汉斗鱼鱼乐网络科技有限公司 | Project construction method and device, electronic equipment and storage medium |
| CN113377342B (en) * | 2020-02-25 | 2024-01-19 | 南昌节点汇智科技有限公司 | Project construction method and device, electronic equipment and storage medium |
| CN111324371B (en) * | 2020-04-17 | 2023-12-19 | 北京简单一点科技有限公司 | Method for pipeline SAAS service plug-in to support user-defined environment and task |
| CN111324371A (en) * | 2020-04-17 | 2020-06-23 | 北京简单一点科技有限公司 | Method for supporting custom environment and task by production line SAAS service plug-in |
| US11341025B2 (en) | 2020-05-27 | 2022-05-24 | Red Hat Inc. | Dynamic tuning of computing devices using application log data |
| CN111738689A (en) * | 2020-06-24 | 2020-10-02 | 北京首汽智行科技有限公司 | Demo project delivery method and system |
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| CN111897541A (en) * | 2020-08-03 | 2020-11-06 | 上海嗨酷强供应链信息技术有限公司 | Software interaction platform and method for automatically deploying resources in cloud environment |
| CN111880832A (en) * | 2020-08-03 | 2020-11-03 | 上海嗨酷强供应链信息技术有限公司 | Mirror image management method based on docker containerization technology |
| CN111935312A (en) * | 2020-09-21 | 2020-11-13 | 深圳蜂巢互联(南京)科技研究院有限公司 | Industrial Internet container cloud platform and flow access control method thereof |
| CN112084040A (en) * | 2020-09-28 | 2020-12-15 | 上海道客网络科技有限公司 | Container resource planning system and method based on application mirror image data identification |
| CN112084040B (en) * | 2020-09-28 | 2021-07-20 | 上海道客网络科技有限公司 | Container resource planning system and method based on application mirror image data identification |
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| CN112416524B (en) * | 2020-11-25 | 2023-06-23 | 电信科学技术第十研究所有限公司 | Method and device for implementing CI/CD of cross-platform based on docker and kubernetes offline |
| CN112527349A (en) * | 2020-12-02 | 2021-03-19 | 航天科工网络信息发展有限公司 | Dynamic deployment strategy optimization and continuous deployment service guarantee system |
| US11556332B2 (en) | 2021-02-23 | 2023-01-17 | International Business Machines Corporation | Application updating in a computing environment using a function deployment component |
| CN113114482A (en) * | 2021-03-08 | 2021-07-13 | 中国—东盟信息港股份有限公司 | Container-based hybrid cloud management system and method |
| CN113114482B (en) * | 2021-03-08 | 2022-06-14 | 中国—东盟信息港股份有限公司 | Container-based hybrid cloud management system and method |
| CN113238928B (en) * | 2021-04-23 | 2022-05-06 | 杭州电子科技大学 | End cloud collaborative evaluation system for audio and video big data task |
| CN113238928A (en) * | 2021-04-23 | 2021-08-10 | 杭州电子科技大学 | End cloud collaborative evaluation system for audio and video big data task |
| CN113835827A (en) * | 2021-08-18 | 2021-12-24 | 微梦创科网络科技（中国）有限公司 | Application deployment method and device based on container Docker and electronic equipment |
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| CN115113909A (en) * | 2022-06-29 | 2022-09-27 | 北京永信至诚科技股份有限公司 | Project automation operation and maintenance method, system, electronic equipment and storage medium |
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