Accurately guessing CPU & memory requirements for workloads is hard! So, it is common for users to over-provision pods which leads to under-utilized clusters, and the need to scale up cluster size to accommodate workloads.

Recently added in-place pod resize feature brings the ability to right-size over-provisioned pods without restarting them. In this talk, Vinay will discuss how cluster autoscaler currently handles pods pending due to insufficient resources, then introduce a change to the autoscaling workflow that right-sizes over-provisioned pods, and show how it can help schedule pending pods more quickly while lowering costs & carbon footprint.

Haoran will talk about the latest research that leverages machine learning and reinforcement learning techniques to achieve multi-dimensional autoscaling, and discuss how this cutting-edge work can help proactively scale workloads to achieve optimal cluster utilization while meeting application SLOs by more precisely provisioning the pods.