Horizontal and Vertical Pod Autoscaling

Horizontal and Vertical Pod Autoscaling in Kubernetes: A Deep Dive

Kubernetes offers a powerful mechanism for managing and scaling containerized applications. Two key components of this mechanism are Horizontal Pod Autoscaling (HPA) and Vertical Pod Autoscaling (VPA). These tools dynamically adjust the resources allocated to your application based on demand, ensuring optimal performance and cost efficiency. This article provides a comprehensive exploration of HPA and VPA, covering their functionalities, prerequisites, advantages, disadvantages, and usage with examples.

1. Introduction

In a modern microservices architecture deployed on Kubernetes, applications face fluctuating workloads. Predicting these fluctuations accurately and manually scaling pods to meet the changing demands is a complex and time-consuming task. HPA and VPA automate this process, relieving operational burden and optimizing resource utilization.

• Horizontal Pod Autoscaling (HPA): Adjusts the number of pod replicas based on observed CPU utilization, memory consumption, or custom metrics. It scales out (increases replicas) during peak load and scales in (decreases replicas) during periods of low demand. HPA aims to maintain a desired average resource utilization across all pods.

• Vertical Pod Autoscaling (VPA): Analyzes the resource usage of pods over time and recommends or automatically adjusts the CPU and memory requests and limits of individual pods. Unlike HPA, VPA modifies the resource constraints of existing pods rather than changing the number of pods. VPA can ensure that each pod has the appropriate resources to function optimally.

2. Prerequisites

Before implementing HPA and VPA, ensure the following prerequisites are met:

• Kubernetes Cluster: A functioning Kubernetes cluster (version 1.8 or higher) is essential. Cloud providers like AWS, Google Cloud, and Azure offer managed Kubernetes services (EKS, GKE, and AKS, respectively) that simplify cluster deployment and management.

• Metrics Server or Resource Metrics API: HPA and VPA rely on metrics collected from pods. The metrics-server is a standard component that provides CPU and memory utilization metrics through the Resource Metrics API. Install it using kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml.

• Custom Metrics Adapter (Optional): For scaling based on application-specific metrics (e.g., requests per second, queue length), you'll need a custom metrics adapter like Prometheus Adapter. This adapter exposes Prometheus metrics to the Kubernetes API, allowing HPA to use them for scaling decisions.

• VPA Admission Controller: VPA requires the ValidatingAdmissionPolicy feature gate and the VPA admission controller to be enabled. These are usually enabled by default in recent Kubernetes versions. Check your cluster's documentation for specific instructions.

3. Horizontal Pod Autoscaling (HPA)

3.1 Features of HPA

• Resource-Based Scaling: Scales based on CPU and memory utilization.
• Custom Metrics Scaling: Scales based on application-defined metrics using a custom metrics adapter.
• External Metrics Scaling: Scales based on metrics from external sources.
• Multiple Metrics: Supports scaling based on a combination of metrics.
• Target Utilization: Allows you to define the desired average utilization level for CPU and memory.

3.2. Creating an HPA

Here's an example of creating an HPA that scales a Deployment named my-app based on CPU utilization:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: my-app-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: my-app
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70

Explanation:

• scaleTargetRef: Specifies the Deployment (or other scalable resource) that the HPA manages.
• minReplicas: The minimum number of pod replicas.
• maxReplicas: The maximum number of pod replicas.
• metrics: Defines the scaling metric. In this case, it's CPU utilization.
• averageUtilization: The target CPU utilization across all pods (70%).

Apply the YAML file using kubectl apply -f hpa.yaml.

3.3 Advantages of HPA

• Automatic Scaling: Dynamically adjusts the number of pods based on workload.
• Improved Resource Utilization: Optimizes resource allocation, reducing wasted resources.
• Enhanced Application Availability: Maintains application performance under varying loads.
• Simplified Operations: Reduces manual intervention for scaling.

3.4 Disadvantages of HPA

• Scaling Latency: It takes time for HPA to detect load changes and scale pods. This delay can lead to performance degradation during sudden traffic spikes.
• Potential for Over-Scaling: If the target utilization is set too low, HPA might over-scale, leading to increased resource costs.
• Requires Accurate Resource Requests: HPA relies on accurate resource requests for pods. If requests are not properly configured, HPA may not function effectively.
• Thundering Herd Problem: Scaling up replicas can sometimes exacerbate resource contention, particularly when pods share common dependencies.

4. Vertical Pod Autoscaling (VPA)

4.1 Features of VPA

• Automatic Resource Request/Limit Adjustment: Automatically adjusts CPU and memory requests and limits for pods.
• Recommendation Mode: Provides recommendations for resource requests and limits without automatically applying them. This allows for manual review and approval.
• Auto Mode: Automatically updates resource requests and limits for pods.
• Off Mode: Disables VPA for a specific pod.
• Resource History: Collects resource usage data over time to improve recommendations.

4.2. Creating a VPA

apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: my-app-vpa
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: my-app
  updatePolicy:
    updateMode: "Auto"

Explanation:

• targetRef: Specifies the Deployment that the VPA manages.
• updatePolicy: Defines how VPA updates the pod's resource requests and limits.
  • Off: VPA only makes recommendations, but does not apply them.
  • Initial: VPA assigns resource requests only on pod creation, but doesn't update them later.
  • Recreate: VPA evicts pods to update resource requests, resulting in a temporary downtime.
  • Auto: VPA will attempt to update resource requests and limits without evicting the pod, if possible. Otherwise, it will recreate the pod.

Apply the YAML file using kubectl apply -f vpa.yaml.

4.3 Advantages of VPA

• Optimized Resource Allocation: Ensures that each pod has the appropriate resources, reducing wasted resources and improving performance.
• Reduced Manual Tuning: Automates the process of setting resource requests and limits.
• Improved Application Stability: Prevents out-of-memory errors and CPU throttling by providing adequate resources.
• Simplified Capacity Planning: VPA simplifies capacity planning by providing accurate resource usage data.

4.4 Disadvantages of VPA

• Pod Restarts: VPA, particularly in Auto or Recreate mode, can restart pods to apply new resource configurations, which can cause temporary downtime.
• Potential for Resource Oversizing: VPA might overestimate resource requirements, leading to increased resource costs if not carefully monitored.
• Complexity: VPA adds complexity to the deployment process.
• Incompatibility with HPA (Sometimes): Using VPA with HPA in certain configurations can lead to conflicts. It's generally recommended to use HPA for scaling the number of replicas and VPA for optimizing resource allocation per pod when using them together. Consider using HPA with custom metrics that aren't directly related to CPU/Memory for the best experience in these scenarios.

5. HPA and VPA: Working Together

HPA and VPA can complement each other. VPA optimizes the resource allocation for individual pods, while HPA adjusts the number of pods based on overall demand. A common approach is to use VPA to optimize resource requests and limits and HPA to scale the number of pods based on custom metrics (e.g., request latency). When using both, carefully consider the scaling metrics used by HPA to avoid conflicts with VPA.

6. Conclusion

Horizontal and Vertical Pod Autoscaling are crucial tools for managing and scaling applications in Kubernetes. HPA automatically adjusts the number of pods based on workload, while VPA optimizes the resource allocation for individual pods. By understanding their functionalities, advantages, and disadvantages, you can effectively utilize HPA and VPA to achieve optimal performance, resource utilization, and cost efficiency in your Kubernetes deployments. Remember to carefully monitor your HPA and VPA configurations to ensure they are functioning as expected and to avoid unintended consequences, such as over-scaling or excessive pod restarts.