Autoscaling

Autoscaling automatically adjusts the number of pods or resources allocated to your application based on demand. Instead of manually scaling your applications up and down, Kubernetes can monitor metrics and automatically scale to meet workload requirements, improving resource utilization and ensuring performance during traffic spikes.

What Is Autoscaling?

Autoscaling in Kubernetes means automatically changing the number of pods (horizontal scaling) or the resources allocated to pods (vertical scaling) based on observed metrics. This ensures your applications have enough resources during high demand and don’t waste resources during low demand.

graph TB A[Autoscaling] --> B[Horizontal Pod Autoscaler] A --> C[Vertical Pod Autoscaler] A --> D[Cluster Autoscaler] B --> E[Scales Pod Replicas] C --> F[Adjusts Pod Resources] D --> G[Scales Cluster Nodes] style A fill:#e1f5ff style B fill:#fff4e1 style C fill:#fff4e1 style D fill:#fff4e1

Types of Autoscaling

Kubernetes provides several autoscaling mechanisms:

Horizontal Pod Autoscaler (HPA)

Scales the number of pod replicas based on CPU, memory, or custom metrics. Most commonly used for scaling stateless applications.

graph LR A[High CPU Usage] --> B[HPA Detects] B --> C[Increase Replicas] C --> D[More Pods Created] D --> E[Load Distributed] E --> F[CPU Usage Drops] G[Low CPU Usage] --> H[HPA Detects] H --> I[Decrease Replicas] I --> J[Pods Terminated] J --> K[Resources Freed] style A fill:#ffe1e1 style C fill:#fff4e1 style D fill:#e8f5e9 style G fill:#e1f5ff style I fill:#fff4e1

Vertical Pod Autoscaler (VPA)

Adjusts CPU and memory requests and limits for pods based on historical usage. Useful for optimizing resource allocation.

graph LR A[Pod Running] --> B[VPA Monitors Usage] B --> C[Analyzes Patterns] C --> D[Recommends Resources] D --> E{Update Needed?} E -->|Yes| F[Update Pod Resources] E -->|No| A F --> G[Pod Restarted with New Resources] style A fill:#e1f5ff style C fill:#fff4e1 style F fill:#e8f5e9

Cluster Autoscaler

Automatically adds or removes nodes from the cluster based on pod scheduling needs. Works with cloud providers to manage the cluster size.

Horizontal vs Vertical Scaling

Understanding when to use each approach:

graph TB subgraph horizontal[Horizontal Scaling HPA] A[More Pods] --> B[Distribute Load] B --> C[Better for Stateless Apps] B --> D[Handles Traffic Spikes] end subgraph vertical[Vertical Scaling VPA] E[More Resources per Pod] --> F[Optimize Resource Usage] F --> G[Better for Stateful Apps] F --> H[Cost Optimization] end style A fill:#e1f5ff style E fill:#fff4e1 style C fill:#e8f5e9 style G fill:#f3e5f5

Use Horizontal Scaling (HPA) when:

Application is stateless
Can distribute load across multiple pods
Need to handle traffic spikes
Most common use case

Use Vertical Scaling (VPA) when:

Application is stateful (can’t easily scale horizontally)
Want to optimize resource allocation
Need to right-size resource requests
Cost optimization is important

How Autoscaling Works

The autoscaling process involves monitoring, evaluation, and action:

graph TD A[Autoscaler Active] --> B[Monitor Metrics] B --> C[Collect Data] C --> D[Evaluate Against Targets] D --> E{Scaling Needed?} E -->|Yes| F[Calculate Desired Replicas] E -->|No| B F --> G[Update Workload] G --> H[Pods Created/Terminated] H --> I[Monitor Results] I --> B style A fill:#e1f5ff style D fill:#fff4e1 style F fill:#e8f5e9 style H fill:#f3e5f5

Metrics for Autoscaling

Autoscalers use various metrics to make scaling decisions:

CPU and Memory

Most common metrics, available by default with Metrics Server:

CPU utilization percentage
Memory utilization percentage

Custom Metrics

Can use custom application metrics:

Request rate (requests per second)
Queue length
Application-specific metrics
External metrics from monitoring systems

Multiple Metrics

HPA can scale based on multiple metrics simultaneously, scaling to satisfy the metric with the highest demand.

Autoscaling Considerations

Scaling Speed

Scale-up delay: Avoid rapid scaling up that could cause instability
Scale-down delay: Conservative scale-down to avoid thrashing
Cool-down periods: Prevent rapid oscillation

Resource Availability

Ensure cluster has capacity for scale-up
Consider using Cluster Autoscaler for node capacity
Monitor resource quotas and limits

Application Readiness

Use readiness probes to ensure pods are ready before receiving traffic
Consider startup probes for slow-starting applications
Health checks prevent scaling unhealthy pods

Cost Management

Set appropriate min/max replica limits
Monitor scaling behavior and costs
Optimize resource requests to reduce unnecessary scaling

When to Use Autoscaling

Use autoscaling when:

✅ Variable workload - Traffic or demand varies over time
✅ Cost optimization - Want to minimize resource usage during low demand
✅ Performance requirements - Need to maintain performance during spikes
✅ Resource efficiency - Want to optimize resource allocation
✅ Production workloads - Critical applications that need automatic scaling

Consider manual scaling when:

❌ Predictable, constant load - Workload is stable and predictable
❌ Development/testing - Non-production environments
❌ Stateful applications - May not benefit from horizontal scaling
❌ Very small clusters - May not have capacity for autoscaling

Best Practices

Set resource requests - Autoscalers need resource requests to calculate metrics
Configure min/max replicas - Prevent over-scaling or under-scaling
Use health probes - Ensure pods are ready before scaling
Monitor scaling behavior - Watch how autoscaler responds to changes
Test scaling - Verify autoscaling works correctly before production
Set appropriate metrics - Choose metrics that accurately reflect demand
Consider scaling delays - Allow time for pods to become ready
Use custom metrics carefully - Ensure custom metrics are reliable
Combine with Cluster Autoscaler - For cloud environments, use Cluster Autoscaler
Review and optimize - Regularly review autoscaling configuration and adjust

Topics

HPA - Horizontal Pod Autoscaler for scaling pod replicas
VPA - Vertical Pod Autoscaler for optimizing pod resources