Kubernetes offers multiple levels of capacity management control for autoscaling. So much so that the multitude of knobs can confuse even the most experienced administrators.
Kubernetes schedulers assign pods of containers to cluster nodes with the entire process controllable by configuration parameters in YAML files. Using those files, Kubernetes administrators can request and set maximum limits for the CPU and memory available for use by each container within a pod (also known as resource request and limits).
Administrators can also provide instructions for Kubernetes to automatically allocate more CPU and memory to a pod according to CPU and memory usage criteria (also known as vertical pod autoscaling). Furthermore, they can configure Kubernetes to automatically replicate pods for stateless application workloads (also known as horizontal pod autoscaling). Finally, they can also configure the cluster to add more nodes once the other nodes are fully used or reserved (also known as cluster autoscaler).
Additional controls exist to help administrators guide workloads to specific nodes or node groups (also known as taints and tolerations) and create logical administrative domains within a cluster for different teams (also known as namespaces), each with its maximum allowed usage of cluster CPU and memory (also known as resource quotas).
Despite (or maybe in spite) of the sophisticated autoscaling technology, a Kubernetes cluster often accumulates financial waste and creates performance bottlenecks over time.
The top three reasons administrators don’t get away from the burden of balancing performance and efficiency are:
In this guide, we explain the Kubernetes autoscaling and control functionality using examples and YAML configuration files and highlight the limitations that deserve the attention of administrators.
Kubernetes Vertical Pod Autoscaling doesn’t recommend pod limit values or consider I/O. Densify identifies mis-provisioned containers at a glance and prescribes the optimal configuration.
Densify has partnered with Intel to offer one year of free resource optimization software licensing to qualified companies.
Learn how VPA can recommend more CPU and memory for your pods.
Read Chapter 1Appreciate the power of replicating pods for applications ready to take advantage.
Read Chapter 2Learn to add nodes to expand the computing capacity of hosted clusters.
Read Chapter 3Learn how to define Kubernetes resource quotas, set limit ranges, and optimize resource usage.
Read Chapter 4Learn using Kubernetes resource quota in namespaces along with pod requests and limits.
Read Chapter 5Understand how Taints and Tolerations control node assignment.
Read Chapter 6Understand the difference between ReplicaSet, Deployment, DeamonSet, and more.
Read Chapter 7Become familiar with Kubernetes services and how to distribute service traffic.
Read Chapter 8Follow step by step instructions to configure a Kubernetes namespace.
Read Chapter 9Learn Kubernetes advanced scheduling using Node Affinity and Pod Affinity.
Read Chapter 10Explore all of the dimensions in planning for Kubernetes node capacity.
Read Chapter 11Understand the functionality of the Kubernetes Service Discovery by following examples.
Read Chapter 12Learn Kubernetes Labels use cases and best practices by following examples.
Read Chapter 13Kubernetes Vertical Pod Autoscaling doesn’t recommend pod limit values or consider I/O. Densify identifies mis-provisioned containers at a glance and prescribes the optimal configuration.
Densify has partnered with Intel to offer one year of free resource optimization software licensing to qualified companies.
