Book Image

Mastering Kubernetes - Fourth Edition

By : Gigi Sayfan
3.3 (3)
Book Image

Mastering Kubernetes - Fourth Edition

3.3 (3)
By: Gigi Sayfan

Overview of this book

The fourth edition of the bestseller Mastering Kubernetes includes the most recent tools and code to enable you to learn the latest features of Kubernetes 1.25. This book contains a thorough exploration of complex concepts and best practices to help you master the skills of designing and deploying large-scale distributed systems on Kubernetes clusters. You’ll learn how to run complex stateless and stateful microservices on Kubernetes, including advanced features such as horizontal pod autoscaling, rolling updates, resource quotas, and persistent storage backends. In addition, you’ll understand how to utilize serverless computing and service meshes. Further, two new chapters have been added. “Governing Kubernetes” covers the problem of policy management, how admission control addresses it, and how policy engines provide a powerful governance solution. “Running Kubernetes in Production” shows you what it takes to run Kubernetes at scale across multiple cloud providers, multiple geographical regions, and multiple clusters, and it also explains how to handle topics such as upgrades, capacity planning, dealing with cloud provider limits/quotas, and cost management. By the end of this Kubernetes book, you’ll have a strong understanding of, and hands-on experience with, a wide range of Kubernetes capabilities.
Table of Contents (21 chapters)
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Index

Bin packing and utilization

Cloud resources are expensive. Efficient usage of resources on Kubernetes has two parts: efficiently scheduling pods to nodes based on their resource requests, and pods actually using the resources they requested.

Bin packing means ensuring that the total sum of resource requests is as close as possible to the allocatable resources on the target node. Once a workload is scheduled to a node, it will not be evicted under normal conditions even if the node is highly underutilized, but components like the cluster autoscaler can help here.

Resource utilization measures what percentage of the requested resource is actually used. Resource utilization is in general not fixed as the resource usage of workloads may vary widely throughout their lifetimes.

There are a lot of nuances to bin packing, resource utilization, and the interplay between them. For example, there are different resources such as CPU, memory, disk, and network. A node may have 100...