Kubernetes Autoscaling without Compromise: A Bi-Dimensional Approach from StormForge that Works

Predictable, optimized scaling is a challenge that requires trade-offs in Kubernetes environments today. The Kubernetes autoscalers enable horizontal and vertical autoscaling, but it’s not easy for them to work together. This leads the vast majority of organizations to use Horizontal Pod Autoscaler only, with arbitrary CPU values determining the scale point. And for the VPA to work with the HPA, custom scaling metrics other than CPU and memory must be used.

This session explains how StormForge enables bi-dimensional pod autoscaling to address these limitations, enabling simultaneous use of HPA and VPA. It accomplishes this by issuing ML-based CPU and memory recommendations for VPA while setting the optimal CPU thresholds for HPA. Now organizations can autoscale more efficiently to minimize wasted resources and unnecessary cloud expense.

Presented by Brian Likosar, Director of Global Solutions Architecture, StormForge.

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