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· One min read
Yinghao Wang

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Chaos Mesh includes the StressChaos tool, which allows you to inject CPU and memory stress into your Pod. This tool can be very useful when you test or benchmark a CPU-sensitive or memory-sensitive program and want to know its behavior under pressure.

However, as we tested and used StressChaos, we found some issues with usability and performance. For example, why does StressChaos use far less memory than we configured? To correct these issues, we developed a new set of tests. In this article, I'll describe how we troubleshooted these issues and corrected them. This information will enable you to get the most out of StressChaos.

· One min read
Debabrata Panigrahi

LFX Mentorship Experience

I’m a junior undergraduate majoring in Biomedical Engineering in the Department of Biotechnology and Medical Engineering at the National Institute of Technology Rourkela, India. For someone who started to code only because I was fascinated by it, it was all a journey of self-learning, filled with various adversities. But when I started with open-source contributions, it was all very beginner-friendly and I came across a lot of people who helped me learn the tech stack better.

· One min read
Keao Yang

Chaos Engineering - How to simulate I/O faults at runtime

In a production environment, filesystem faults might occur due to various incidents such as disk failures and administrator errors. As a Chaos Engineering platform, Chaos Mesh has supported simulating I/O faults in a filesystem ever since its early versions. By simply adding an IOChaos CustomResourceDefinition (CRD), we can watch how the filesystem fails and returns errors.

· One min read

How-a-Top-Game-Company-Uses-Chaos-Engineering-to-Improve-Testing

NetEase Fuxi AI Lab is China’s first professional game AI research institution. Researchers use our Kubernetes-based Danlu platform for algorithm development, training and tuning, and online publishing. Thanks to the integration with Kubernetes, our platform is much more efficient. However, due to Kubernetes- and microservices-related issues, we are constantly testing and improving our platform to make it more stable.