Kubernetes is a widely adopted open-source container orchestration platform for deploying, managing, and scaling applications in containerized environments.
Kubernetes comes with some complexities such as installation and setup, version compatibility, security, and so on. And a managed Kubernetes service can help you to avoid these complexities.
Elastic Kubernetes Service(EKS) is managed service by AWS that allows you to run Kubernetes without having to manage the underlying infrastructure.
EKS being a fully managed solution, AWS takes care of the provisioning, scaling, and management of your Kubernetes clusters.
To automatically meet the fluctuating demands of your application, EKS provides you Kubernetes’s Cluster Autoscaler, Horizontal POD Autoscaler(HPA), and Vertical Pod Autoscaler(VPA).
There are various other methods for autoscaling in Kubernetes but Horizontal POD Autoscaler(HPA) and Vertical Pod Autoscaler(VPA) are two prominent approaches.
In this blog post, we will be comparing HPA and VPA to understand how they can optimize resource utilization in your EKS Kubernetes clusters.
Understanding Horizontal Pod Autoscaler (HPA)
Horizontal Pod Autoscaler automatically scales the number of pods based on observed CPU utilization or custom metrics reported within Kubernetes or from sources outside the cluster.
HPA is installed as an API resource in your EKS cluster and you don’t need to install it to start scaling your applications.
When you employ HPA for a specific deployment, it consistently monitors the CPU usage of the pods. If the observed CPU utilization increases above a defined threshold, HPA automatically scales up the number of Pods to handle the workload.
Conversely, when CPU utilization by Pods goes below the configured threshold, HPA calls the deployment resource to cut down. Thus, optimizing resource utilization and reducing unnecessary costs.
Understanding Vertical Pod Autoscaler (VPA)
Vertical Pod Autoscaler(VPA) automatically adjusts memory and CPU resources for your pods with the right size to resource requests. It continuously monitors the resource usage of pods and analyzes the historical data to determine the optimal resource limits for each Pod. This enhances resource efficiency, improves application performance, and resource utilization.
Unlike HPA, VPA needs to be deployed on your EKS cluster to enable vertical scaling.
While HPA works by adjusting the number of pods to manage the load, VPA takes a different approach and optimizes resource allocation within individual pods.
HPA and VPA: A Look at the Key Differences
| Feature | Horizontal Pod Autoscaler (HPA) | Vertical Pod Autoscaler (VPA) |
| Metrics | CPU utilization and memory utilization | CPU utilization, memory utilization, and custom metrics |
| Scaling Thresholds | Minimum and maximum number of pods, as well as a target CPU or memory utilization | The minimum and maximum amount of CPU and memory, as well as a target CPU or memory utilization |
| Support for Custom Metrics | Yes | Yes |
| Reaction Time | Can be slow to react to changes in workload | Can be faster to react to changes in workload |
| Accuracy | Not always accurate | More accurate than HPA |
| Troubleshooting | Can be difficult to troubleshoot | Easier to troubleshoot than HPA |
| Ideal For | Applications that are CPU or memory intensive | Applications that are sensitive to resource utilization |
Both HPA and VPA have their strengths and weaknesses including scaling criteria, limited dimensions, resource fragmentation, and so on. Combining both HPA and VPA can address a broad range of autoscaling needs to mitigate the limitations.
Optimizing EKS Autoscaling With nOps Karpenter Solution (nKS)
Karpenter is an open-source and flexible Kubernetes cluster autoscaler built with AWS. It simplifies Kubernetes infrastructure by launching the right compute resources to handle your cluster’s applications. While Karpenter offers many advantages over standard cluster autoscalers, it comes with some limitations including:
- Limited support for custom metrics
- Lack of integration with some cloud providers
- Complexities in configuration
nKS by nOPS is a powerful autoscaling solution built over Karpenter that addresses various limitations of HPA, VPA, Cluster Autoscaler, and open source version of Karpenter combined. It provides organizations with efficient and cost-effective Kubernetes autoscaling for optimized resource management. nKS also offers a single cluster view to monitor the entire AWS ecosystem and schedule resources to maximize cost savings. The notable features of nKS by nOPS include:
- 60-minute advance spot prediction
- Spot Instance price monitoring
- Cluster node rebalancing
- User-friendly interface
With its ML-driven cost optimization and other industry-grade functionalities, nKS can help you reduce your EKS infrastructure costs by 50%.
Wrapping Up
Leveraging a managed Kubernetes service like EKS and incorporating intelligent autoscaling solutions like nKS, can enhance your Kubernetes autoscaling operations significantly. This streamlined approach ensures optimized resource management and cost-effectiveness, so you can focus on delivering high-quality applications without worrying about infrastructure management.