Load prediction based Docker container dynamic scheduling method

A docker container and load forecasting technology, applied in the direction of instruments, resource allocation, software deployment, etc., can solve problems such as deficiencies, automatically adjust resource load capacity, etc., and achieve the effect of improving availability

Active Publication Date: 2018-11-30
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

[0006] The purpose of the present invention is to apply the load forecasting technology to the dynamic scheduling of Docker containers, solve the problem that the current Docker container clusters cannot automatically ad

Method used

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  • Load prediction based Docker container dynamic scheduling method
  • Load prediction based Docker container dynamic scheduling method
  • Load prediction based Docker container dynamic scheduling method

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Embodiment Construction

[0019] The specific implementation of the present invention will be further described below in conjunction with accompanying drawings and examples, but the implementation and protection of the present invention are not limited thereto. Achieved with technology.

[0020] This example provides a dynamic scheduling method for Docker containers based on load forecasting. The overall architecture is as follows figure 1 As shown, it specifically includes four processes including load data collection, load data storage, load data analysis, and Docker container dynamic scheduling.

[0021] Load Data Acquisition

[0022] The Docker container cluster user first deploys the Docker container load monitoring module on each server node, and specifies the time interval for the Docker container load monitoring module to collect load data, and then starts the Docker container load monitoring module.

[0023] The Docker container load monitoring module collects the consumption of the four typ...

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Abstract

The invention provides a load prediction based Docker container dynamic scheduling method. The method includes: acquiring Docker container load data by a Docker container load monitoring module; storing the load data in a time series database; enabling a Docker container load prediction module to acquire and process the load data from the time series database, analyze the load data by the aid of an ARIMA model and generate a load predicated value; finally, automatically expanding quantity of Docker containers in a Docker container cluster according to the load predicated value by a Docker container scheduling module. By application of a load prediction technique to the field of Docker container scheduling, automatic expansion of the Docker container cluster according to real-time load conditions is realized, the problem of inadequate load capacity in an operating stage due to failure of the Docker container cluster in automatic resource regulation according to load conditions is effectively solved, and usability of the Docker container cluster is improved.

Description

technical field [0001] The invention belongs to the technical field of dynamic scheduling of Docker containers, and in particular provides a dynamic scheduling method of Docker containers based on load prediction. Background technique [0002] The microservice architecture splits an application into multiple independent services with business attributes. Each service runs in an independent process, and the services cooperate with each other through a lightweight communication mechanism to provide business value to end users. The deployment methods of applications based on the microservice architecture include manual deployment and script deployment based on cloud platforms, and image deployment based on Docker containers is currently a relatively mainstream method. [0003] As an open source application container engine, Docker containers enable developers to package applications and their dependencies into portable Docker containers, and then publish the Docker containers t...

Claims

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Application Information

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IPC IPC(8): G06F8/60G06F9/50
CPCG06F8/60G06F9/505
Inventor 刘发贵郑少斌欧嘉敏
Owner SOUTH CHINA UNIV OF TECH
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