Hey everyone! We're tackling the challenge of accurately setting CPU and memory requests and limits in Kubernetes. Instead of relying on gut feelings or default settings—both of which can lead to wasted resources or issues like throttling and out-of-memory errors—we're experimenting with a benchmarking approach in our CI/CD pipeline. By running benchmarks and analyzing workloads, we're aiming to derive optimal resource requests and limits based on actual performance metrics like HTTP requests per second. In our latest write-up, we cover: 1) The limitations of manual tuning for dynamic workloads, 2) The benefits of benchmarking CPU and memory under realistic loads, 3) How to incorporate these benchmarks into Kubernetes manifests, and 4) Some challenges related to autoscaling and metrics systems. I'm curious if anyone has implemented a similar 'shift-left' strategy for resource optimization or has integrated benchmarking into their workflows, and how that's gone for you! Check out the complete post for more details: [Kubernetes Resource Optimization: From Manual Tuning to Automated Benchmarking](https://benchtest.dev/blog/kubernetes-resource-optimization?utm_source=chatgpt.com)
3 Answers
Why not leverage a vertical pod autoscaler (VPA)? It can adjust requests and limits on the fly without the need to restart pods. This way, you're adapting to changes dynamically instead of relying solely on pre-set figures.
Have you looked into using Goldilocks? It can really help with tuning resource requests and limits. It provides recommendations based on actual usage, which can take some guesswork out of the process.
Awesome write-up! Bringing benchmarking into CI/CD is a game changer. At my company, we also focus on integrating security automation early in the pipeline, which helps keep our builds efficient. It's similar to what you're doing with resource optimization! If you're interested in a deeper dive, let me know!

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