Container Orchestration
Last updated on 2024-06-27 | Edit this page
Container Orchestration
Although you can certainly manage research workflows that use multiple containers manually, there are a number of container orchestration tools that you may find useful when managing workflows that use multiple containers. We won’t go in depth on using these tools in this lesson but instead briefly describe a few options and point to useful resources on using these tools to allow you to explore them yourself.
- Docker Compose
- Kubernetes
- Docker Swarm
The Wild West
Use of container orchestration tools for research workflows is a relatively new concept and so there is not a huge amount of documentation and experience out there at the moment. You may need to search around for useful information or, better still, contact your friendly neighbourhood RSE to discuss what you want to do.
Docker Compose provides a way of constructing a unified workflow (or service) made up of multiple individual Docker containers. In addition to the individual Dockerfiles for each container, you provide a higher-level configuration file which describes the different containers and how they link together along with shared storage definitions between the containers. Once this high-level configuration has been defined, you can use single commands to start and stop the orchestrated set of containers.
- Using Docker Compose for the Simple Deployment of an Integrated Drug Target Screening Platform
- Docker Compose Overview
Kubernetes is an open source framework that provides similar functionality to Docker Compose. Its particular strengths are that is platform independent and can be used with many different container technologies and that it is widely available on cloud platforms so once you have implemented your workflow in Kubernetes it can be deployed in different locations as required. It has become the de facto standard for container orchestration.
Docker Swarm provides a way to scale out to multiple copies of similar containers. This potentially allows you to parallelise and scale out your research workflow so that you can run multiple copies and increase throughput. This would allow you, for example, to take advantage of multiple cores on a local system or run your workflow in the cloud to access more resources. Docker Swarm uses the concept of a manager container and worker containers to implement this distribution.