Understanding Kubernetes Autoscaling for Improved Scalability and Cost Reduction: VPA, HPA, and CA
Executive Summary:
This document provides a comprehensive overview of Kubernetes autoscaling mechanisms, specifically Vertical Pod Autoscaler (VPA), Horizontal Pod Autoscaler (HPA), and Cluster Autoscaler (CA). These mechanisms play a critical role in ensuring efficient resource utilization and cost reduction in cloud environments. By simplifying these concepts, anyone can gain a clear understanding of how to optimize their infrastructure for better scalability and cost management.
Introduction:
Kubernetes is a powerful container orchestration platform that simplifies application deployment, scaling, and management. To effectively manage resources and reduce costs, Kubernetes offers three mechanisms that automatically scale infrastructure based on workload demands:
1. Vertical Pod Autoscaler (VPA)
2. Horizontal Pod Autoscaler (HPA)
3. Cluster Autoscaler (CA)
These autoscaling mechanisms provide service-capacity elasticity through VPA and HPA, and cluster-capacity elasticity through CA. By adapting to changing workloads, Kubernetes ensures efficient resource consumption and cost optimization.
Vertical Pod Autoscaler (VPA):
VPA optimizes resource allocation for container workloads by updating their resource requests and limits based on historical usage data. This mechanism helps avoid over-requesting resources while scaling up and under-requesting workloads as needed. Unlike HPA, VPA does not change the number of replicas. A suitable analogy would be from the movie Jaws:
From — small boat
To — Aircraft carrier
Horizontal Pod Autoscaler (HPA):
HPA adjusts the number of pods in a workload resource, such as a Deployment or StatefulSet, based on resource consumption metrics (e.g., CPU, memory). By adding or removing pods, HPA keeps resource usage within specified bounds, ensuring efficient scaling during varying workloads. A fitting analogy would be transitioning from a metro train in Melbourne to a local train in Mumbai (i.e., cramming more people into the same train).
From — Metro Melbourne with passengers as pod
To — Mumbai local with passengers as pod
Cluster Autoscaler (CA):
CA expands or contracts the size of a Kubernetes cluster by adding or removing worker nodes based on resource demand. By monitoring unschedulable pods and current resource utilization, CA ensures that no resources are wasted and cluster capacity remains elastic. This dynamic scaling process reduces infrastructure costs during idle periods and scales up during peak times.
Cluster API Project:
The Cluster API project aims to standardize CA support across major cloud providers and on-premises installations, eliminating vendor lock-in and promoting consistent autoscaling features. As a result, CA can be seamlessly integrated with AWS, IBM Cloud, Azure, GCE, vSphere, OpenStack, and more.
Amazon EKS Cluster Autoscaling:
In an Amazon EKS cluster, CA closely interacts with the Kubernetes API, AWS Auto Scaling groups, and AWS Identity and Access Management (IAM) to ensure efficient resource allocation. Deploying CA involves configuring it with flags that fine-tune its behavior, such as — cloud-provider, — node-group-auto-discovery, and others.
CA’s scaling process consists of two main steps:
Scaling Up:
a. Monitors the cluster for unschedulable pods
b. Identifies potential accommodating nodes
c. Communicates with the AWS Auto Scaling API
d. Calculates the number of nodes needed
e. Updates the desired capacity of the Auto Scaling group(s)
f. Launches additional EC2 instances as worker nodes
g. Assigns unschedulable pods to the new nodes
Scaling Down:
a. Monitors the resource utilization of nodes
b. Checks if pods on underutilized nodes can be rescheduled
c. Starts scale-down by marking nodes for termination and evicting pods
d. Evicted pods are rescheduled or temporarily become unschedulable
e. Waits for a grace period to ensure nodes remain underutilized
f. Updates the desired capacity of the Auto Scaling group(s) to remove nodes
g. Terminates instances corresponding to the underutilized nodes
h. Removed instances are detached from the cluster, and the remaining nodes continue to run the workloads
Conclusion:
Kubernetes autoscaling mechanisms, such as VPA, HPA, and CA, provide powerful ways to optimize resource consumption in cloud environments. By closely monitoring pod and node resource utilization, these components ensure that your infrastructure remains efficient, responsive, and cost-effective.
Technical Details — https://medium.com/@careerabhi/technical-design-document-terraform-module-cluster-autoscaler-for-kubernetes-55d5c5bd4660
