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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 Free with your book: PDF Copy, AI Assistant, and Next-Gen Reader
Book DescriptionKubernetes 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
Who this book is forThis book is ideal for DevOps engineers, SREs, cloud architects, and Kubernetes professionals who want to optimize resource usage and improve scalability. A basic understanding of Kubernetes concepts and cloud environments, i.e., AWS, GCP, and Azure, is assumed.
