tldr - powered by Generative AI

• Current auto scaling controllers available in the community are Horizontal Pod Auto Scaler (HPA) and Vertical Pod Auto Scaler (VPA)
• Multi-Dimensional Pod Auto Scaler (MPA) is a new in-house proposal for Kubernetes auto scaler
• MPA consists of three main controllers: Recommended, Updater, and Admission Controller
• Recommended analyzes historical usage patterns of the Pod and recommends resource usage and limit to be set based on the histogram of the resource usage observed in the previous time window
• Updater observes the difference between the recommended requests and limits and the current set request and limit and will evict the parts if the gap is too big
• Admission Controller is responsible for updating the pause request a limit when the past the victim pass are restarting according to the recommended values provided by the recommended
• MPA provides automatic vertical scaling for Kubernetes pod allowing them to adjust resource requests and limits based on actual usage

tldr - powered by Generative AI

• Updates on Horizontal Pod Autoscaler v2 API and deprecation notices for v2 beta1 and v2 beta2
• Introduction of gRPC editions in Cluster Autoscaler, including the grpc expander and provider
• Changes in Vertical Pod Autoscaler
• Encouragement for community involvement in bug reports, pull requests, and documentation improvement