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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
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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
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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

The Kubernetes Metrics Server

Before diving into the details of how to use HPA, let me cover a crucial component: the Kubernetes Metrics Server. In the previous chapter, I explored the use of Prometheus and Grafana for rightsizing pods based on historical usage patterns. However, HPA, by default, utilizes a different source of metrics to determine when to scale pod replicas.The Metrics Server plays a crucial role in this process, providing real-time resource utilization data that HPA relies on for making scaling decisions. It serves as the primary source of container resource metrics for Kubernetes' autoscaling mechanisms.In the following sections, I'll cover the what and why of the Metrics Server in detail, discussing its purpose, importance, and implementation within a Kubernetes cluster.

Metric Server: The What and the Why

The Kubernetes Metrics Server is a cluster-wide aggregator of resource usage data. While not a built-in component of Kubernetes, it's a crucial add...

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