CN112579260A - Automatic expansion and contraction method and device for industrial Internet of things data center to transmit Worker service - Google Patents
Automatic expansion and contraction method and device for industrial Internet of things data center to transmit Worker service Download PDFInfo
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- CN112579260A CN112579260A CN202011516466.9A CN202011516466A CN112579260A CN 112579260 A CN112579260 A CN 112579260A CN 202011516466 A CN202011516466 A CN 202011516466A CN 112579260 A CN112579260 A CN 112579260A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/40—Transformation of program code
- G06F8/41—Compilation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/45562—Creating, deleting, cloning virtual machine instances
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/45595—Network integration; Enabling network access in virtual machine instances
Abstract
The application discloses an automatic expansion and contraction method and device for the Worker service transmitted by an industrial Internet of things data center, wherein the method comprises the steps of developing a Worker service environment configuration Shell script, and configuring the operating environment of a Worker service container internal system in a Docker mirror image; developing a Worker service to run a Shell script, and enabling a Worker service container in the Docker mirror image to start a Worker service call; developing a Dockerfile file, and constructing a Worker service container in a Docker mirror image; developing a Worker dispatching center service, adding an interactive interface between the Worker dispatching center service and a monitoring platform, and adding a Worker container starting method and a Worker container closing method for respectively starting or closing a Worker service container. According to the method and the device, through constructing the Worker service container in the Docker mirror image, when the number of factories and the number of equipment are increased or reduced, the data volume is changed, and automatic expansion and contraction can be realized without the participation of operation and maintenance personnel.
Description
Technical Field
The invention belongs to the technical field of industrial Internet of things data transmission, and relates to an automatic expansion and contraction method and device for transmitting Worker service through an industrial Internet of things data center.
Background
When the industrial Internet of things data transmission Worker service is operated, when plant equipment and equipment generate data changes, operation and maintenance personnel manually modify the operation quantity of the Worker service through judging the monitoring index of the Worker service, and due to the fact that the number of plants is increased, and the operation and maintenance personnel cannot respond timely due to the fact that a lot of configuration files are available, the Worker service is shut down due to too large pressure or the Worker service is started too much, and resource waste is caused.
Disclosure of Invention
In order to solve the technical problems in the related art, the application provides an automatic expansion and contraction method and device for transmitting Worker service through an industrial internet of things data center, and the technical scheme is as follows:
in a first aspect, the application provides an automatic expansion and contraction capacity method for transmitting Worker service through an industrial internet of things data center, and the method comprises the following steps:
developing a Worker service environment to configure a Shell script, and configuring the operating environment of an internal system of a Worker service container in a Docker mirror image;
developing a Worker service to run a Shell script, and enabling a Worker service container in the Docker mirror image to start a Worker service call;
developing a Dockerfile file, and constructing a Worker service container in the Docker mirror image;
developing a Worker dispatching center service, adding an interactive interface between the Worker dispatching center service and a monitoring platform, and adding a Worker container starting method and a Worker container closing method for respectively starting or closing a Worker service container.
Optionally, the developing a Worker service environment to configure the Shell script and configure the operating environment of the internal system of the Worker service container in the Docker image includes:
developing the Worker service environment to configure Shell scripts, configuring Java running environments, and packaging according to Worker service codes to generate a Worker service running dependency package;
copying the dependent packet to a Repo directory;
and generating CLASSPATH according to the dependency packet in the current system Repo directory and adding the CLASSPATH to the system PATH environment.
Optionally, the developing a Worker service runs a Shell script, so that a Worker service container in the Docker image starts a Worker service call, including:
receiving parameters of HelixZooceeperServer and HelixClasterName as a write-in configuration file;
receiving a MemoryLimit parameter to limit the size of starting resources of the Worker service;
and starting the Worker service through the configuration file.
Optionally, the developing a Dockerfile file and constructing a Worker service container in the Docker image includes:
setting a version of a Maven mirror image JDK8 as a container operation system;
setting a MAVEN _ OPTS parameter to compile a Worker service and create a Worker service working directory;
compiling a Worker service code, sequentially executing the configuration of the Shell script in the Worker service environment and the running of the Shell script by the Worker service to finish the Docker mirror image packaging of the Worker service, and realizing containerization.
Optionally, the method further comprises:
when the monitoring platform monitors that the data volume is increased, the Worker dispatching center service is called through an interactive interface between the Worker dispatching center service and the monitoring platform, and the Worker service container is started;
and when the monitoring platform monitors that the data volume is reduced, the Worker dispatching center service is called through an interactive interface between the Worker dispatching center service and the monitoring platform, and the Worker service container is closed.
In a second aspect, the application provides an automatic expansion and contraction capacity device of an industrial internet of things data center for transmitting Worker service, the device includes:
the system comprises a first development module, a second development module and a third development module, wherein the first development module is used for developing a Worker service environment configuration Shell script and configuring the operating environment of a system in a Worker service container in a Docker mirror image;
the second development module is used for developing a Worker service to run a Shell script so that a Worker service container in the Docker mirror image starts the Worker service call;
the third issuing module is used for developing a Docker file and constructing a Worker service container in the Docker mirror image;
and the fourth development module is used for developing a Worker dispatching center service, adding an interactive interface between the monitoring platform and the Worker dispatching center service, and adding a Worker container starting method and a Worker container closing method to be respectively used for starting or closing the Worker service container.
Optionally, the first development module is further configured to:
developing the Worker service environment to configure Shell scripts, configuring Java running environments, and packaging according to Worker service codes to generate a Worker service running dependency package;
copying the dependent packet to a Repo directory;
and generating CLASSPATH according to the dependency packet in the current system Repo directory and adding the CLASSPATH to the system PATH environment.
Optionally, the second development module is further configured to:
receiving parameters of HelixZooceeperServer and HelixClasterName as a write-in configuration file;
receiving a MemoryLimit parameter to limit the size of starting resources of the Worker service;
and starting the Worker service through the configuration file.
Optionally, the third sending module is further configured to:
setting a version of a Maven mirror image JDK8 as a container operation system;
setting a MAVEN _ OPTS parameter to compile a Worker service and create a Worker service working directory;
compiling a Worker service code, sequentially executing the configuration of the Shell script in the Worker service environment and the running of the Shell script by the Worker service to finish the Docker mirror image packaging of the Worker service, and realizing containerization.
Optionally, the apparatus further comprises:
the starting module is used for calling the Worker dispatching center service through an interactive interface between the Worker dispatching center service and the monitoring platform when the monitoring platform monitors that the data volume is increased, and starting the Worker service container;
and the closing module is used for calling the Worker dispatching center service through an interactive interface between the Worker dispatching center service and the monitoring platform when the monitoring platform monitors that the data volume is reduced, and closing the Worker service container.
Through above-mentioned technical scheme, this application can realize following beneficial effect at least:
by constructing the Worker service container in the Docker mirror image, when the data volume is changed due to increase or decrease of the number of factories and the number of equipment, automatic capacity expansion and contraction can be realized without participation of operation and maintenance personnel. In addition, after the Worker service realizes automatic expansion and contraction, the time cost of operation and maintenance personnel and the resource cost of the server are saved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a flowchart of an automatic expansion and contraction method for an industrial internet of things data center-to-Worker service provided in an embodiment of the present application;
fig. 2 is a schematic diagram of an industrial internet of things data center-to-Worker service automatic capacity expansion system provided in an embodiment of the present application;
fig. 3 is a schematic diagram of an automatic scalability and scalability apparatus for transmitting a Worker service to an industrial internet of things data center provided in an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
Fig. 1 is a flowchart of an automatic expansion and contraction method for transmitting a Worker service by an industrial Internet of things data center provided in an embodiment of the present application, where the automatic expansion and contraction method for transmitting a Worker service by an industrial Internet of things data center may include the following steps:
in one possible implementation manner for implementing the step 101, firstly, developing a Worker service environment configuration Shell script, configuring a Java running environment, and packaging according to a Worker service code to generate a Worker service running dependency package; then copying the dependent packet to a Repo directory; and finally, according to the dependency packet generated under the current system Repo directory, CLASSPATH is generated and added to the system PATH environment.
102, developing a Worker service to run a Shell script, and enabling a Worker service container in a Docker mirror image to start a Worker service call;
in one possible implementation manner of implementing step 102, first, parameters of the hellxzookeeper server and the hellixclustername are received as a write configuration file; then receiving a MemoryLimit parameter to limit the size of the starting resource of the Worker service; and finally, starting the Worker service through the configuration file.
103, developing a Dockerfile file, and constructing a Worker service container in the Docker mirror image;
in one possible implementation manner for implementing step 103, first, a version of Maven mirror JDK8 is set as a container operating system; then setting a MAVEN _ OPTS parameter to compile the Worker service and create a Worker service working directory; and finally compiling a Worker service code, sequentially executing the configuration of the Shell script in the Worker service environment and the running of the Shell script by the Worker service to finish the Docker mirror image packaging of the Worker service, and realizing containerization.
As shown in fig. 2, the Worker service container includes a Worker service environment configuration Shell script, a Worker service running Shell script, a Worker service Dockerfile file, and a Worker service code.
And 104, developing a Worker dispatching center service, adding an interactive interface between the Worker dispatching center service and a monitoring platform, and adding a Worker container starting method and a Worker container closing method for respectively starting or closing a Worker service container.
For example, when the monitoring platform monitors that the data volume is increased, the Worker dispatching center service is called through an interactive interface between the Worker dispatching center service and the monitoring platform, and the Worker service container is started;
for another example, when the monitoring platform monitors that the data amount is reduced, the Worker dispatching center service is called through an interactive interface between the Worker dispatching center service and the monitoring platform, and the Worker service container is closed.
To sum up, the automatic expansion and contraction capacity method for transmitting the Worker service through the data center of the industrial Internet of things provided by the application has the advantages that through the establishment of the Worker service container in the Docker mirror image, when the number of factories and the number of equipment are increased or reduced, the data volume is changed, and the automatic expansion and contraction capacity can be realized without the participation of operation and maintenance personnel. In addition, after the Worker service realizes automatic expansion and contraction, the time cost of operation and maintenance personnel and the resource cost of the server are saved.
Fig. 3 is a schematic view of an automatic scalability apparatus for transmitting a Worker service through an industrial internet of things data center provided in an embodiment of the present application, where the automatic scalability apparatus for transmitting a Worker service through an industrial internet of things data center provided in the present application may include: a first development module 310, a second development module 320, a third development module 330, and a fourth development module 340.
The first development module 310 can be used for developing a Worker service environment configuration Shell script and configuring the operating environment of the internal system of the Worker service container in the Docker mirror image.
The second development module 320 may be configured to develop a Worker service to run Shell scripts, so that a Worker service container in the Docker mirror starts a Worker service call.
The third launching module 330 may be configured to develop a Dockerfile file and construct a Worker service container in the Docker image.
The fourth sending module 340 may be configured to develop a Worker dispatching center service, add an interactive interface between the monitoring platform and the Worker dispatching center service, and add a Worker container starting method and a Worker container closing method to respectively start or close a Worker service container.
Optionally, the first development module 310 is further configured to perform the following operations:
developing the Worker service environment to configure Shell scripts, configuring Java running environments, and packaging according to Worker service codes to generate a Worker service running dependency package;
copying the dependent packet to a Repo directory;
and generating CLASSPATH according to the dependency packet in the current system Repo directory and adding the CLASSPATH to the system PATH environment.
Optionally, the second development module 320 is further configured to perform the following operations:
receiving parameters of HelixZooceeperServer and HelixClasterName as a write-in configuration file;
receiving a MemoryLimit parameter to limit the size of starting resources of the Worker service;
and starting the Worker service through the configuration file.
Optionally, the third sending module 330 is further configured to perform the following operations:
setting a version of a Maven mirror image JDK8 as a container operation system;
setting a MAVEN _ OPTS parameter to compile a Worker service and create a Worker service working directory;
compiling a Worker service code, sequentially executing the configuration of the Shell script in the Worker service environment and the running of the Shell script by the Worker service to finish the Docker mirror image packaging of the Worker service, and realizing containerization.
In a possible implementation manner, the automatic expansion and contraction device for transmitting the Worker service through the data center of the industrial internet of things provided by the application may further include: the device comprises a starting module and a closing module.
The starting module can be used for calling the Worker dispatching center service through an interactive interface between the Worker dispatching center service and the monitoring platform when the monitoring platform monitors that the data volume is increased, and starting the Worker service container.
The closing module can call the Worker dispatching center service through an interactive interface between the Worker dispatching center service and the monitoring platform when the monitoring platform monitors that the data volume is reduced, and close the Worker service container.
To sum up, the automatic expansion and contraction capacity device of the Worker service transmitted by the industrial internet of things data center provided by the application can realize automatic expansion and contraction capacity without participation of operation and maintenance personnel by constructing the Worker service container in the Docker mirror image and changing data volume caused by increase or decrease of factory quantity and equipment quantity. In addition, after the Worker service realizes automatic expansion and contraction, the time cost of operation and maintenance personnel and the resource cost of the server are saved.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. An automatic expansion and contraction capacity method for transmitting Worker service through an industrial Internet of things data center is characterized by comprising the following steps:
developing a Worker service environment to configure a Shell script, and configuring the operating environment of an internal system of a Worker service container in a Docker mirror image;
developing a Worker service to run a Shell script, and enabling a Worker service container in the Docker mirror image to start a Worker service call;
developing a Dockerfile file, and constructing a Worker service container in the Docker mirror image;
developing a Worker dispatching center service, adding an interactive interface between the Worker dispatching center service and a monitoring platform, and adding a Worker container starting method and a Worker container closing method for respectively starting or closing a Worker service container.
2. The method of claim 1, wherein the developing a Worker service environment configures Shell scripts and configures the operating environment of a system inside a Worker service container in a Docker image, and the method comprises the following steps:
developing the Worker service environment to configure Shell scripts, configuring Java running environments, and packaging according to Worker service codes to generate a Worker service running dependency package;
copying the dependent packet to a Repo directory;
and generating CLASSPATH according to the dependency packet in the current system Repo directory and adding the CLASSPATH to the system PATH environment.
3. The method of claim 1, wherein the developing a Worker service runs Shell scripts, causing a Worker service container in the Docker image to initiate a Worker service call, comprising:
receiving parameters of HelixZooceeperServer and HelixClasterName as a write-in configuration file;
receiving a MemoryLimit parameter to limit the size of starting resources of the Worker service;
and starting the Worker service through the configuration file.
4. The method of claim 1, wherein the developing a Dockerfile file and constructing a Worker service container in the Docker image comprises:
setting a version of a Maven mirror image JDK8 as a container operation system;
setting a MAVEN _ OPTS parameter to compile a Worker service and create a Worker service working directory;
compiling a Worker service code, sequentially executing the configuration of the Shell script in the Worker service environment and the running of the Shell script by the Worker service to finish the Docker mirror image packaging of the Worker service, and realizing containerization.
5. The method according to any one of claims 1 to 4, further comprising:
when the monitoring platform monitors that the data volume is increased, the Worker dispatching center service is called through an interactive interface between the Worker dispatching center service and the monitoring platform, and the Worker service container is started;
and when the monitoring platform monitors that the data volume is reduced, the Worker dispatching center service is called through an interactive interface between the Worker dispatching center service and the monitoring platform, and the Worker service container is closed.
6. The utility model provides an automatic scaling device that expands of industry thing networking data center biography Worker service, its characterized in that, the device includes:
the system comprises a first development module, a second development module and a third development module, wherein the first development module is used for developing a Worker service environment configuration Shell script and configuring the operating environment of a system in a Worker service container in a Docker mirror image;
the second development module is used for developing a Worker service to run a Shell script so that a Worker service container in the Docker mirror image starts the Worker service call;
the third issuing module is used for developing a Docker file and constructing a Worker service container in the Docker mirror image;
and the fourth development module is used for developing a Worker dispatching center service, adding an interactive interface between the monitoring platform and the Worker dispatching center service, and adding a Worker container starting method and a Worker container closing method to be respectively used for starting or closing the Worker service container.
7. The apparatus of claim 6, wherein the first development module is further configured to:
developing the Worker service environment to configure Shell scripts, configuring Java running environments, and packaging according to Worker service codes to generate a Worker service running dependency package;
copying the dependent packet to a Repo directory;
and generating CLASSPATH according to the dependency packet in the current system Repo directory and adding the CLASSPATH to the system PATH environment.
8. The apparatus of claim 6, wherein the second development module is further configured to:
receiving parameters of HelixZooceeperServer and HelixClasterName as a write-in configuration file;
receiving a MemoryLimit parameter to limit the size of starting resources of the Worker service;
and starting the Worker service through the configuration file.
9. The apparatus of claim 6, wherein the third sending module is further configured to:
setting a version of a Maven mirror image JDK8 as a container operation system;
setting a MAVEN _ OPTS parameter to compile a Worker service and create a Worker service working directory;
compiling a Worker service code, sequentially executing the configuration of the Shell script in the Worker service environment and the running of the Shell script by the Worker service to finish the Docker mirror image packaging of the Worker service, and realizing containerization.
10. The apparatus of any of claims 6 to 9, further comprising:
the starting module is used for calling the Worker dispatching center service through an interactive interface between the Worker dispatching center service and the monitoring platform when the monitoring platform monitors that the data volume is increased, and starting the Worker service container;
and the closing module is used for calling the Worker dispatching center service through an interactive interface between the Worker dispatching center service and the monitoring platform when the monitoring platform monitors that the data volume is reduced, and closing the Worker service container.
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| CN113949638A (en) * | 2021-08-26 | 2022-01-18 | 中铁第四勘察设计院集团有限公司 | Railway communication system capacity expansion and reduction method and system based on cloud platform |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113949638A (en) * | 2021-08-26 | 2022-01-18 | 中铁第四勘察设计院集团有限公司 | Railway communication system capacity expansion and reduction method and system based on cloud platform |
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