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Total Size:
10.8 MB
Info Hash:
E07057E73EDDABBE163554BD4A7E70D7B92BE9E8
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Added:
Dec. 11, 2025, 12:14 p.m.
Stats:
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(Last updated: Dec. 11, 2025, 12:15 p.m.)
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| Melendez C. Kubernetes Autoscaling. Build efficient,cost-optimized clusters 2025.pdf | 10.8 MB |
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NOTE
SOURCE: Melendez C. Kubernetes Autoscaling. Build efficient,cost-optimized clusters 2025
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COVER
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MEDIAINFO
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
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