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The DevOps 2.5 Toolkit

You're reading from   The DevOps 2.5 Toolkit Monitoring, Logging, and Auto-Scaling Kubernetes: Making Resilient, Self-Adaptive, And Autonomous Kubernetes Clusters

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Product type Paperback
Published in Nov 2019
Publisher Packt
ISBN-13 9781838647513
Length 322 pages
Edition 1st Edition
Concepts
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Author (1):
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Viktor Farcic Viktor Farcic
Author Profile Icon Viktor Farcic
Viktor Farcic
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Toc

Table of Contents (9) Chapters Close

1. Autoscaling Deployments and StatefulSets Based on Resource Usage FREE CHAPTER 2. Auto-scaling Nodes of a Kubernetes Cluster 3. Collecting and Querying Metrics and Sending Alerts 4. Debugging Issues Discovered Through Metrics and Alerts 5. Extending HorizontalPodAutoscaler with Custom Metrics 6. Visualizing Metrics and Alerts 7. Collecting and Querying Logs 8. What Did We Do? 9. Other Books You May Enjoy

Scaling down the cluster

Scaling up the cluster to meet the demand is essential since it allows us to host all the replicas we need to fulfill (some of) our SLAs. When the demand drops and our nodes become underutilized, we should scale down. That is not essential given that our users will not experience problems caused by having too much hardware in our cluster. Nevertheless, we shouldn't have underutilized nodes if we are to reduce expenses. Unused nodes result in wasted money. That is true in all situations, especially when running in Cloud and paying only for the resources we used. Even on-prem, where we already purchased hardware, it is essential to scale down and release resources so that they can be used by other clusters.

We'll simulate a decrease in demand by applying a new definition that will redefine the HPAs threshold to 2 (min) and 5 (max).

 1  kubectl...
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