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 Description:
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
Who this book is for:
This 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.
Table of Contents
- Introduction to Kubernetes Autoscaling
- Workload Autoscaling Overview
- Workload Autoscaling with HPA and VPA
- Kubernetes Event-Driven Autoscaling (KEDA)
- KEDA in Action on AWS EKS
- Metrics, Monitoring, and Observability
- Data Plane Autoscaling Overview
- Getting Started with Karpenter
- Karpenter on AWS
- Karpenter Management Operations
- Practical Use Cases
- Patterns and Best Practices
