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  • Book Overview & Buying Kubernetes Autoscaling
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Kubernetes Autoscaling

Kubernetes Autoscaling

By : Christian Melendez
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Kubernetes Autoscaling

Kubernetes Autoscaling

By: Christian Melendez
Buy this Book

Overview of this book

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.
Table of Contents (20 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Get in touch
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1
Part 1: Getting started with Kubernetes Autoscaling
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Part 1: Getting started with Kubernetes Autoscaling
2
Introduction to Kubernetes Autoscaling
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Introduction to Kubernetes Autoscaling
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Technical requirements
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Scalability foundations
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Kubernetes architecture
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Efficient Kubernetes data planes
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Kubernetes autoscaling categories
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Hands-on lab: Creating a Kubernetes cluster
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Summary
3
Workload Autoscaling Overview
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Workload Autoscaling Overview
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Technical requirements
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Challenges of autoscaling workloads
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How does the Kubernetes scheduler work?
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Workload rightsizing
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Workload autoscalers
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Summary
4
Workload Autoscaling with HPA and VPA
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Workload Autoscaling with HPA and VPA
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Technical requirements
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The Kubernetes Metrics Server
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HPA basics
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HPA and custom metrics
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VPA basics
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How to work with HPA and VPA together
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Summary
5
Part 2: Workload Autoscaling and KEDA
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Part 2: Workload Autoscaling and KEDA
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Kubernetes Event-Driven Autoscaling – Part 1
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Kubernetes Event-Driven Autoscaling – Part 1
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Technical requirements
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KEDA: What it is and why you need it
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KEDA’s architecture
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KEDA scalers
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KEDA CRDs
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Scaling Deployments
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Scaling Jobs
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Summary
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Kubernetes Event-Driven Autoscaling – Part 2
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Kubernetes Event-Driven Autoscaling – Part 2
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Technical requirements
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Autoscaling in KEDA continued
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Scaling based on schedule
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KEDA’s HTTP add-on
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What if a KEDA scaler is not available?
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Advanced autoscaling features
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KEDA with cloud providers
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Summary
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Workload Autoscaling Operations
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Workload Autoscaling Operations
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Technical requirements
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Workload autoscaling operations
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Troubleshooting workload autoscaling
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Monitoring KEDA
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Upgrading KEDA
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Best practices for workload efficiency
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Summary
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Part 3: Node Autoscaling and Karpenter
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Part 3: Node Autoscaling and Karpenter
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Data Plane Autoscaling Overview
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Data Plane Autoscaling Overview
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Technical requirements
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What is data plane autoscaling?
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Data plane autoscalers
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CAS on AWS
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Relevant autoscalers
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Summary
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Node Autoscaling with Karpenter — Part 1
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Node Autoscaling with Karpenter — Part 1
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Technical requirements
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What is Karpenter?
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History of Karpenter
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Karpenter resources
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Launching nodes
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Deploying Karpenter
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Hands-on lab: Autoscaling with Karpenter
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Workload scheduling constraints
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Summary
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Node Autoscaling with Karpenter — Part 2
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Node Autoscaling with Karpenter — Part 2
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Technical requirements
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Removing nodes
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Disruption
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NodePool disruption budgets
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Karpenter best practices
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Summary
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Karpenter Management Operations
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Karpenter Management Operations
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Technical requirements
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Advanced operations and troubleshooting
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Common operational challenges
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Node Auto Repair for unhealthy node handling
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Upgrading Karpenter
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Observability and metrics
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Summary
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Part 4: Use Cases, Patterns, and Recommendations
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Part 4: Use Cases, Patterns, and Recommendations
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Practical Use Cases for Autoscaling in Kubernetes
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Practical Use Cases for Autoscaling in Kubernetes
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Technical requirements
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Common workloads for autoscaling
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Use case 1: Web applications
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Use case 2: Batch jobs
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Use case 3: Compute cost optimization
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Use case 4: GPU-based workloads
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Summary
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Patterns and Recommendations
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Patterns and Recommendations
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Technical requirements
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Scaling down to zero
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Fault-tolerant workloads
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Ending notes
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Future trends
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Summary
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Unlock Your Exclusive Benefits
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Unlock Your Exclusive Benefits
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Unlock this Book’s Free Benefits in 3 Easy Steps
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Other Books You May Enjoy
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Other Books You May Enjoy
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Index
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Index

Practical Use Cases for Autoscaling in Kubernetes

At this point, you’ve spent quite some time in the previous chapters exploring a lot of details about autoscaling in Kubernetes. You’ve learned the basics, you’ve explored how tools such as KEDA and Karpenter work, and I’ve shared several tips and tricks to help you get the most out of them. Now, it’s time to bring all those lessons together with practical, hands-on examples. In this chapter, we’ll spend time with the terminal, and you’ll see how KEDA and Karpenter can help you build efficient and cost-optimized clusters automatically, growing and shrinking as demand shifts.

The focus here isn’t just on completing the labs and copying/pasting the commands I’ve included. The focus is on understanding the impact these tools can have on different types of workloads and realizing the concrete benefits of autoscaling in real-world scenarios. For example, we’ll explore...

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