Applications are now increasingly distributed, running on multiple machines and accessed by multiple users from all over the world. By bundling the application code, the application runtime, and the libraries, containers, and container orchestrators, we have addressed many of the challenges of building distributed systems. With container runtimes like Docker, we can deploy our applications on different environments. And with container orchestration tools like Kubernetes, we can scale our applications. We frequently have to break our distributed application into a collection of multiple containers running on different machines. This requires us to coordinate execution and facilitate communication among different containers. A scenario where this situation is encountered is when we compose a workflow using multiple containers. In this article, we will learn how to build such workflows using a container workflow engine, Argo, for Kubernetes. We will develop workflows for the following two examples of so-called "embarrassingly parallel" problems for which Kubernetes is the ideal scaling platform.
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N-Queens using genetic algorithms
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