Melendez C. Kubernetes Autoscaling. Build efficient,cost-optimized clusters 2025

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Melendez C. Kubernetes Autoscaling. Build efficient,cost-optimized clusters 2025

Textbook in PDF format Learn to scale workloads efficiently, reduce cloud costs, and optimize performance with real-world strategies for event-driven and infrastructure-level scaling Key Features: Autoscale Kubernetes workloads and infrastructure using KEDA and Karpenter Improve performance, reduce cloud costs, and eliminate resource waste with smarter scaling Work with hands-on labs, real-world use cases, and step-by-step guidance from the creator of Karpenter Blueprints Kubernetes is the backbone of modern containerized infrastructure, but scaling it efficiently remains a challenge. Kubernetes Autoscaling equips cloud professionals with this comprehensive guide to dynamically scaling applications and infrastructure using the powerful combination of Kubernetes Event-Driven Autoscaler (KEDA) and Karpenter, AWS’s next-generation cluster autoscaler. You’ll begin with autoscaling fundamentals, move through HPA and VPA, and then get hands-on KEDA for event-driven workloads and Karpenter for data plane scaling. With the help of real-world use cases, best practices, and detailed patterns, you’ll deploy resilient, scalable, and cost-effective Kubernetes clusters across production environments. By the end of this book, you’ll be able to implement practical autoscaling strategies to improve performance, reduce cloud costs, and eliminate over-provisioning. What you will learn - Gain a solid foundation in Kubernetes autoscaling and its components - Scale deployments, jobs, and StatefulSets using KEDA’s CRDs - Configure event-based scaling strategies using metrics and schedules - Deploy and manage Karpenter for on-demand infrastructure provisioning - Explore advanced node disruption and lifecycle techniques - Combine KEDA and Karpenter to implement full-stack autoscaling - Optimize costs using Spot Instances, scale-to-zero, and workload placement - Apply real-world patterns and monitor autoscaling performance in production