Kubernetes Autoscaling
Guide to Kubernetes Autoscaling
October 7, 2021 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).
| Visualize Utilization Metrics | Set Resource Requests & Limits | Set Requests & Limits with Machine Learning | Identify mis-sized containers at a glance & automate resizing | Get Optimal Node Configuration Recommendations | |
|---|---|---|---|---|---|
| Kubernetes |  |
|
|||
| Kubernetes + Densify | |
|
|
|
|
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).
- Vertical Pod Autoscaler (VPA)
- Increases and decreases pod CPU and memory
- Horizontal Pod Autoscaler (HPA)
- Adds and removes pods
- Cluster Autoscaler (CA)
- Adds and removes cluster nodes
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).
Pick the ideal instance type for your workload using an ML-powered visual catalog map
See how it works
Despite (or maybe in spite) of the sophisticated autoscaling technology, a Kubernetes cluster often accumulates financial waste and creates performance bottlenecks over time.
Identify under/over-provisioned K8s resources and use Terraform to auto-optimize
WATCH 3-MIN VIDEOThe top three reasons administrators don’t get away from the burden of balancing performance and efficiency are:
- Excessive container requests cause waste
- Users often request (or reserve) more CPU and memory for their containers than their application workloads use. Kubernetes respects such requests allowing waste to accumulate over time and magnify with automated replication.
- Resource configurations ignore IOPS and network
- Kubernetes focuses on CPU and memory; however, many performance bottlenecks are created within the I/O capacity to write to disk or the network bandwidth required to transfer data, thus complicating cluster node configuration.
- Static thresholds selected by humans pose a risk
- Manual thresholds (e.g., pod CPU request and limit) or autoscaling mechanisms based on moving averages (of coarsely aggregated data) aren’t a match for the machine learning algorithms required to ensure an accurate assessment of resource usage and lack in upstream open-source distributions.
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.
A free 30-day trial for cloud & Kubernetes resource control. Experience in your environment or use sample data. See optimization potential in 48 hours. No card required.
Free TrialThe Chapters
Chapter 1: Vertical Pod Autoscaling (VPA)
Learn how VPA can recommend more CPU and memory for your pods.
Chapter 2: Horizontal Pod Autoscaling (HPA)
Appreciate the power of replicating pods for applications ready to take advantage.
Chapter 3: The Kubernetes Cluster Autoscaler
Learn to add nodes to expand the computing capacity of hosted clusters.
Chapter 4: How to manage Kubernetes Resource Limits
Learn how to define Kubernetes resource quotas, set limit ranges, and optimize resource usage.
Chapter 5: Kubernetes Resource Quota
Learn using Kubernetes resource quota in namespaces along with pod requests and limits.
Chapter 6: Taints and Tolerations
Understand how Taints and Tolerations control node assignment.
Chapter 7: Kubernetes Workload
Understand the difference between ReplicaSet, Deployment, DeamonSet, and more.
Chapter 8: Service Load Balancer
Become familiar with Kubernetes services and how to distribute service traffic.
Chapter 9: Kubernetes Namespace
Follow step by step instructions to configure a Kubernetes namespace.
Chapter 10: Kubernetes Affinity
Learn Kubernetes advanced scheduling using Node Affinity and Pod Affinity.
Chapter 11: Kubernetes Node Capacity
Explore all of the dimensions in planning for Kubernetes node capacity.
Chapter 12: Kubernetes Service Discovery
Understand the functionality of the Kubernetes Service Discovery by following examples.
Chapter 13: Kubernetes Labels
Learn Kubernetes Labels use cases and best practices by following examples.
Continue Reading this Series
