What Does Multi-Dimensional Optimization Mean in Kubernetes?

In my experience, it's often linked to Pareto optimization. Someone once argued for that method for optimizing a cluster, but I'd say it's better to focus on changing one aspect at a time and see what that does before moving on to anything else.

From my chats in FinOps meetings related to Kubernetes, "multi-dimensional optimization" typically refers to fine-tuning multiple facets of your cluster together. This includes managing costs by sizing pods right, ensuring performance by avoiding memory and CPU limits, scaling effectively with HPA/KEDA, and being smart about monitoring to cut down on unnecessary data. We use tools like Prometheus and Alertmend to make it work.

I've never come across it either, but I'd guess it relates to things like bin-packing with requests and limits, and possibly also to horizontal and vertical pod autoscaling.

I haven't seen that term either, but usually when someone talks about "multi-dimensional" in this space, it relates to security aspects across various layers—like combining NetworkPolicy, ServiceAccount controls, RBAC, and encryption methods. Context is key, though; it might just be some marketing buzz.

I haven't really heard of that term before. It kinda sounds like jargon to me, basically just suggesting that we need better monitoring and automation throughout the entire stack.