Strategies of Docker Images Optimization

Strategies of Docker Images Optimization

Large Docker images lengthen the time it takes to build and share images between clusters and cloud providers. When creating applications, it’s therefore worth optimizing Docker Images and Dockerfiles to help teams share smaller images, improve performance, and debug problems.

Background

Docker, an enterprise container platform is developers’ favourite due to its flexibility and ease-of-use. It makes it generally easy to create, deploy, and run applications inside of containers. With containers, you can gather applications and their core necessities and dependencies into a single package and turn it into a Docker image and replicate. Docker images are built from Dockerfiles, where you define what the image should look like, as well as the operating system and commands.

However, large Docker images lengthen the time it takes to build and share images between clusters and cloud providers. When creating applications, it’s therefore worth optimizing Docker Images and Dockerfiles to help teams share smaller images, improve performance, and debug problems. A lot of verified images available on Docker Hub are already optimized, so it is always a good idea to use ready-made images wherever possible. If you still need to create an image of your own, you should consider several ways of optimization it for production.

Task description

As a part of a larger project, we were asked to propose ways to optimize Docker images for improving performance. There are several strategies to decrease the size of Docker images to optimize for production. In this research project we tried to explore different possibilities that would yield the best boost of performance with less effort.

Image for post

Our approach

By optimization of Docker images, we mean two general strategies:

  • reducing the time of image building to speed up the CI/CD flow;
  • reducing the image size to speed up the image pull operations and cut costs of storing build artifacts.

Therefore, we proceeded along these two directions, trying to improve the overall performance. But first we need some tools to measure how effective is our process and to find bottlenecks.

2020 oct tutorials overviews docker optimization sciforce strategy

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