Vertical Pod Autoscaler concept in Kubernetes - Step-by-Step Execution

Process Flow - Vertical Pod Autoscaler concept

Pod runs with initial resources

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VPA monitors pod resource usage

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VPA analyzes CPU and memory usage

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VPA recommends new resource requests

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If pod restarts, new resources applied

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Pod runs with updated resources

The Vertical Pod Autoscaler watches pod resource use, suggests better CPU/memory settings, and applies them when pods restart.

Execution Sample

Kubernetes

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

This YAML defines a Vertical Pod Autoscaler that automatically adjusts resource requests for a specific pod.

Process Table

Step	Action	Pod CPU Usage	Pod Memory Usage	VPA Recommendation	Pod Resource Update
1	Pod starts with 200m CPU, 256Mi memory	150m	200Mi	No recommendation yet	Pod runs with initial resources
2	VPA monitors usage over time	180m	220Mi	Analyzing usage	No change yet
3	VPA detects sustained higher usage	210m	300Mi	Recommend CPU=250m, Memory=350Mi	Waiting for pod restart
4	Pod restarts with new resource requests	N/A	N/A	Resources updated	Pod runs with CPU=250m, Memory=350Mi
5	VPA continues monitoring	230m	340Mi	No further change	Pod runs stably with updated resources
Exit	No further resource changes needed	230m	340Mi	Stable	Execution stops

💡 Pod resources are stable and match usage; no further updates needed

Status Tracker

Variable	Start	After 1	After 2	After 3	After 4	Final
Pod CPU Request	200m	200m	200m	250m	250m	250m
Pod Memory Request	256Mi	256Mi	256Mi	350Mi	350Mi	350Mi
VPA Recommendation	None	None	Analyzing	Recommend 250m/350Mi	Applied	Stable

Key Moments - 3 Insights

Why doesn't the pod resource update immediately when VPA recommends new values?

The pod resource update happens only when the pod restarts, as shown in step 3 and 4 of the execution_table.

What does VPA monitor to decide resource recommendations?

VPA monitors actual CPU and memory usage over time, as seen in the Pod CPU Usage and Pod Memory Usage columns in the execution_table.

Can VPA reduce resources if usage drops?

Yes, VPA can recommend lower resources if usage drops, but changes apply only after pod restarts, similar to how it updates in step 4.

Visual Quiz - 3 Questions

Look at the execution_table at step 3, what resource values does VPA recommend?

A CPU=180m, Memory=220Mi

B CPU=200m, Memory=256Mi

C CPU=250m, Memory=350Mi

D CPU=300m, Memory=400Mi

💡 Hint

Check the 'VPA Recommendation' column at step 3 in the execution_table.

At which step does the pod actually get updated with new resource requests?

A Step 3

B Step 4

C Step 2

D Step 5

💡 Hint

Look for 'Pod restarts with new resource requests' in the 'Action' column.

If the pod never restarts, what happens to the resource requests?

A They never change

B They update after a delay

C They update immediately

D They update only if usage drops

💡 Hint

Refer to the explanation in key_moments about when resource updates apply.

Concept Snapshot

Vertical Pod Autoscaler (VPA) watches pod CPU and memory use.
It suggests better resource requests based on usage.
Changes apply only when pods restart.
VPA helps pods get enough resources without manual tuning.
Useful for workloads with changing resource needs.

Full Transcript

The Vertical Pod Autoscaler (VPA) in Kubernetes helps pods adjust their CPU and memory requests automatically. It watches how much CPU and memory a pod uses over time. When it sees the pod needs more or less resources, it recommends new values. However, these new resource requests only take effect when the pod restarts. This means VPA monitors usage, suggests changes, and waits for the pod to restart before applying updates. This process helps keep pods running efficiently without manual resource tuning.