A coandor task mapping method of a remote sensing image processing Docker cluster

Abstract

The invention relates to a coandor task mapping method of a remote sensing image processing Docker cluster, and belongs to the field of cloud computing. During a whole treatment process, the association between a Docker and a Conor and a task is realized by automatically generating a Docker universe mode task submitting description file meeting Class Ad resource matching through a system, a Dockercontainer is started on a contractor execution host to operate a remote sensing image processing algorithm, a contractor cluster is deployed on a physical machine, and a docker-engine is deployed ona contractor execution client, and a Connor cluster environment is constructed; metadata of a Docker mirror image is stored, and IP address information and a mirror image name of a physical machine are stored in a MySQL; according to a Conor Class AD task description method and the information recorded in the MySQL, a visual page provided by a front-end page enables a user to form a task submission file generation guide meeting the requirements of the user. The container mode is adopted, the resource utilization rate of remote sensing image processing nodes in a distributed environment is improved, and a plurality of remote sensing tasks can run on the same physical machine.

Description

technical field [0001] The invention relates to a condor task mapping for remote sensing image processing Docker clusters, belonging to the field of cloud computing. Background technique [0002] Docker can package remote sensing professional software, remote sensing image processing algorithms and dependent tools into a portable container to achieve fast startup of Docker clusters and convenient migration of Docker. Compared with virtual machines, Docker is characterized by fast startup and small resource usage. It can start multiple Docker containers on a physical machine or virtual machine, and run multiple remote sensing image processing algorithms on the same physical machine or virtual machine at the same time. On the other hand, multiple remote sensing image processing tasks can be easily implemented in parallel on the same physical node with resource isolation, which greatly utilizes the resources of the physical machine. However, when remote sensing users keep pack...

AI Technical Summary

Problems solved by technology

However, when remote sensing users keep packing a large number of remote sensing algorithms and their operating environments into Docker containers, causing users to manually start different Docker containers to perform hundreds, thousands or even more remote sensing tasks, manual management and configuration are almost impossible. The correspondence between thousands of Docker image names and computing nodes on ten or even hundreds of computing nodes is unacceptable to them, so they need a tool to automatically manage computing nodes and containers, and quickly deploy Their requirements are visually mapped to different Docker containers for execution

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