How to provision jobs in Kubernetes with very wide range of memory use

user_1837574 0
Viewed 39

I am fairly new to Kubernetes, and I think I understand the basics of provisioning nodes and setting memory limits for pods. Here's the problem I have: my application can require dramatically different amounts of memory, depending on the input (and there is no fool-proof way to predict it). Some jobs require 50MB, some require 50GB. How can I set up my K8s deployment to handle this situation?

I have one strategy that I'd like to try out, but I don't know how to do it: start with small instances (nodes with not a lot of memory), and if the job fails with out-of-memory, then automatically send it to increasingly bigger instances until it succeeds. How hard would this be to implement in Kubernetes?

Thanks!

kubernetes
1 Answers

Natively K8S supports horizontal autoscalling i.e. automatically deplying more replicas of a deployment basing on chosen metric like CPU usage, memory usage etc.: Horizontal Pod Autoscaling

What you are describing here though is vertical scaling. It is not supported out of the box, but there is a subproject that seems to be able to fulfill your requirements: vertical-pod-autoscaler

user_90907510
Related