Discover how the standard Kubernetes Cluster Autoscaler's limitations in handling diverse server types lead to inefficiency and higher costs. This episode explores research using convex optimization to intelligently select the optimal mix of cloud instances based on real-time workload demands, costs, and even operational complexity penalties. Learn about the core technique that mathematically models these trade-offs, allowing for efficient problem-solving and significant cost reductions—up to 87% in some scenarios. We discuss how this approach drastically cuts resource over-provisioning compared to traditional autoscaling. Understand the key innovation involving a logarithmic approximation to penalize node type diversity while maintaining mathematical convexity. Finally, we touch upon the concept of an "Infrastructure Optimization Controller" aiming for proactive, continuous optimization of cluster resources.

Music: 'The Insider - A Difficult Subject'