How Kubernetes Horizontal Pod Autoscaler (HPA) Scales Up and Down: A Step-by-Step Guide

Kubernetes’ Horizontal Pod Autoscaler (HPA) is a powerful tool that automatically adjusts the number of pods in a deployment based on resource utilization. It ensures your application scales seamlessly to meet demand, while optimizing resources by scaling down when demand drops. This article walks through the scaling process in detail, explaining how HPA scales up and down based on a target resource utilization.

Understanding HPA Configuration

When configuring the HPA, you set the following main parameters:

• Minimum Pods: The smallest number of pods HPA will maintain for the application.
• Maximum Pods: The maximum number of pods that HPA can deploy to handle increased demand.
• Target Utilization: The target resource utilization level (CPU or memory) at which HPA aims to keep each pod.

For example, let’s say you configure HPA with:

• Minimum Pods: 1
• Maximum Pods: 10
• Target Utilization: 60%

This setup instructs HPA to add or remove pods to keep average resource utilization at 60%. Let’s explore how this configuration affects HPA’s scaling actions when demand fluctuates.

Scaling Up: Meeting Increased Demand

Scaling up happens when user activity spikes, driving up CPU or memory utilization in each pod. Here’s a detailed breakdown of the scaling-up process.

1. Monitoring Resource Usage
   Kubernetes’ HPA continuously monitors CPU or memory usage across your application’s pods. Let’s assume the application initially has 1 pod (the minimum configuration) running.
2. Detecting Increased Demand
   As traffic grows, CPU or memory utilization of the single pod begins to rise. If the utilization exceeds 60% — the target set in the HPA configuration — the HPA determines that additional resources are needed to manage the demand effectively.
3. Triggering Scale-Up
   The HPA responds by incrementally adding pods. It calculates that by adding more pods, each pod’s average utilization should decrease and ideally align with the 60% target.
4. Adding Pods Gradually
   Instead of adding all 10 pods at once, the HPA scales gradually, adding one or a few pods at a time. This approach avoids overprovisioning and allows HPA to adjust more accurately to real-time demand:

• After adding each new pod, HPA waits briefly, monitoring how the additional resources impact overall utilization.
• If average utilization is still above 60%, it adds another pod and repeats the process.

5. Reaching the Maximum Pod Limit
As long as demand keeps pushing average utilization above 60%, HPA will continue to scale up, gradually adding pods until it hits the maximum limit (in this case, 10 pods).
At the maximum limit of 10 pods, if demand keeps rising, HPA cannot add more pods. Utilization may then exceed the target, as resources have reached the maximum cap.

Scaling Down: Responding to Decreased Demand

Just as HPA scales up to manage higher traffic, it also scales down when demand falls. This ensures efficient resource use, preventing over-provisioning. Here’s how scaling down works.

1. Monitoring Decreased Resource Usage
   As user demand drops, the load on each pod decreases. HPA detects this change by monitoring resource utilization levels.
2. Detecting Low Utilization
   If the average utilization falls below the 60% target, HPA realizes that fewer pods are needed to handle the current demand.
3. Triggering Scale-Down
   To adjust, HPA begins removing pods incrementally. The goal is to reduce the number of pods while keeping average utilization close to the 60% target:

• For example, if utilization across 10 pods drops to 30%, HPA will calculate that removing a few pods should bring utilization per pod back closer to 60%.

1. Reducing Pods Gradually
   Scaling down, like scaling up, is a gradual process to avoid disrupting the application. HPA removes one or a few pods at a time, monitoring utilization after each adjustment:

• If utilization remains below 60% after removing a pod, HPA may remove another pod and repeat the process.
• This continues until utilization is balanced around 60% or the pod count reaches the minimum limit.

1. Reaching the Minimum Pod Limit
   When demand is very low, HPA may scale down to its configured minimum (in this case, 1 pod). Even if utilization falls below the target with 1 pod, HPA won’t go below this minimum setting, ensuring the application is always available.

Example Walkthrough

Let’s look at a practical example to see HPA’s behavior in action.

Initial State: 1 Pod at 40% Utilization

• The application is running on 1 pod, and utilization is steady at 40%.
• HPA does nothing since utilization is below the 60% target.

Demand Surge: Utilization Jumps to 90%

• User activity increases, causing the pod’s utilization to jump to 90%.
• HPA detects this and decides to add another pod.

Intermediate State: 2 Pods at 45% Utilization Each

• With 2 pods, the load is spread, and each pod now has 45% utilization.
• Utilization is now below the target, so HPA stops adding pods.

Continuous Increase: Utilization Rises to 70%

• Demand continues to grow, driving each pod’s utilization to 70%.
• HPA adds another pod, bringing the total to 3 pods.

Sustained Demand: HPA Scales Up to the Max of 10 Pods

• As demand keeps rising, HPA adds more pods gradually until reaching the max of 10 pods.
• With 10 pods running, if demand continues to grow, utilization may exceed 60%, but HPA won’t scale up further due to the set maximum.

Drop in Demand: Utilization Falls Below 60%

• After peak hours, user demand decreases, and utilization drops to 40% across the 10 pods.
• HPA gradually scales down, removing pods in small batches until utilization stabilizes around 60%.

Low-Demand State: HPA Scales Down to the Minimum of 1 Pod

• Eventually, demand drops significantly, and HPA scales down to the minimum of 1 pod.
• Even if utilization drops below 60%, HPA maintains at least 1 pod for availability.

Key Benefits of HPA’s Incremental Scaling

The gradual, demand-responsive scaling of HPA offers several benefits:

• Resource Optimization: By scaling up and down incrementally, HPA aligns resources closely with actual demand, minimizing idle capacity.
• Cost Efficiency: Scaling down when demand decreases reduces resource costs, while scaling up ensures you don’t under-provision.
• Improved Reliability: Incremental scaling prevents sudden fluctuations in resource availability, offering a more stable application experience.
• Flexibility and Responsiveness: HPA’s dynamic adjustment keeps your application responsive to varying loads, providing scalability without requiring manual intervention.

Final Thoughts

With HPA, Kubernetes offers a powerful way to handle fluctuations in demand, ensuring applications scale as needed while optimizing resource use. By setting appropriate minimums, maximums, and utilization targets, you can configure HPA to align perfectly with your workload’s requirements, achieving both high availability and cost efficiency.