gke-workload-scaling
Manages scaling for GKE workloads using HPA and VPA.
Skill by googleOfficial
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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Files it touches
~/.claude/skills/gke-workload-scaling
What's inside
- Skill name
gke-workload-scaling- Description
- Manages scaling for GKE workloads using HPA and VPA. Use when configuring Horizontal Pod Autoscaler (HPA), configuring Vertical Pod Autoscaler (VPA), or applying best practices for GKE workload autoscaling. Do not use for cluster-level autoscaling (Cluster Autoscaler), static cluster sizing, or configuring node-level machine styles directly.
- License
- Apache-2.0
- Metadata
- version: 1.0.1 · category: Containers
- Instructions
- 133 lines in SKILL.md · view on GitHub
Files (4)
- SKILL.md
- hpa-example.yaml · assets/
- vpa-example.yaml · assets/
- attribution.md · references/
SKILL.md (first lines)
# GKE Workload Scaling
This skill provides workflows and best practices for scaling applications on
Google Kubernetes Engine (GKE). It covers manual scaling, Horizontal Pod
Autoscaling (HPA), and Vertical Pod Autoscaling (VPA).
## Attribution
**MUST READ**: Before executing any `gcloud`, REST API, or MCP command, you
MUST read [Usage Attribution](references/attribution.md). For commands executed
directly in the terminal (never in generated scripts, Terraform, or user code),
prefix `gcloud` with
`CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:gke-workload-scaling)"`
and append `agent-skills/1.0 (skill:gke-workload-scaling)` to `User-Agent` headers.
## Workflows
### 1. Manual Scaling
Scale a deployment to a fixed number of replicas. Useful for immediate manual
intervention or testing.
**Command:**
```bash
kubectl scale deployment {deployment_name} --replicas={number} -n {namespace}
# Verify the scale event
kubectl get deployment {deployment_name} -n {namespace}
```
### 2. Horizontal Pod Autoscaling (HPA)
Automatically scale the number of pods based on observed CPU utilization, memory
utilization, or custom metrics.
**Prerequisites:**
- Metrics Server must be running (enabled by default on GKE).
- Containers clearly define resource requests/limits.
**Quick Command:**
```bash
kubectl autoscale deployment {deployment_name} --cpu-percent=50 --min=1 --max=10
```
**Manifest Approach (Recommended):** Use a YAML manifest for version-controlled
configuration. See [assets/hpa-example.yaml](assets/hpa-example.yaml) for a
template.
```bash
kubectl apply -f assets/hpa-example.yaml
# Verify HPA is created and fetching metrics
kubectl get hpa
```
**Custom Metrics & External Metrics:** For GKE, the modern and recommended
approach for scaling based on Cloud Monitoring metrics (e.g., Pub/Sub queue
length) is to use the **External** metric type, which is natively supported by
the GKE control plane without requiring the Custom Metrics Adapter. For
application-specific metrics exposed via Prometheus, you can use **Google Cloud
Managed Service for Prometheus** or the Prometheus Adapter.
### 3. Vertical Pod Autoscaling (VPA)
Automatically adjust the CPU and memory reservations for your pods to match
actual usage. This is critical for right-sizing workloads.
**Prerequisites:**
- VPA must be enabled on the cluster.
- **Autopilot:** Enabled by default.
- **Standard:** Must be enabled manually.
**Enable VPA on Standard Cluster:**
```bash
gcloud container clusters update {cluster_name} --enable-vertical-pod-autoscaling --zone {zone}
```
**Update Modes:**
- `Off`: Calculates recommendations but does not apply them. Good for "dry
run" analysis.
- `Initial`: Assigns resources only at pod creation time.
- `Auto`: Updates running pods by restarting them if recommendations differ
significantly from requests.
- `InPlaceOrRecreate`: Attempts to update Pod resources without recreating the
Pod. If in-place update is not possible, it reverts to `Auto` mode (requires
GKE 1.34+).
**Example:** See [assets/vpa-example.yaml](assets/vpa-example.yaml) for a
configuration template.
## Best Practices
1. **Define Resource Requests:** HPA and VPA rely on accurate resource
requests. Always define them in your container specs.
2. **Avoid Metric Conflicts:** Do not configure HPA and VPA to use the same
metric (e.g., both CPU). This causes thrashing.
- *Typical Pattern:* HPA on CPU, VPA on Memory.
3. **Pod Disruption Budgets (PDBs):** Define PDBs to ensure application
availability during scaling events or node upgrades.
4. **HPA Lag:** HPA has a stabilization window (default 5 mins) to prevent
rapid fluctuation.
5. **VPA "Auto" Mode Risks:** In "Auto" mode, VPA restarts pods to change
resources. Ensure your application handles restarts gracefully (e.g.,
handles SIGTERM).
- *Note:* By default, VPA requires at least 2 replicas to perform
evictions (to prevent a situation where the only running replica is
evicted, causing downtime). In GKE 1.22+, you can override this by
setting `minReplicas` in `PodUpdatePolicy`.
## Rightsizing Workflow
1. Deploy VPA in `Off` mode for 24+ hours
2. Read recommendations: `kubectl describe vpa {deployment_name}-vpa -nFrom google/skills, Apache-2.0.
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Signals
- Stars
- 21,542
- Forks
- 1,821
- Open issues
- 21
- Last commit (this folder)
- Oct 19, 2026 (20 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- Version
- 1.0.1
- License
- Apache-2.0
- Repository
- github.com/google/skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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gke-workload-scaling skill. Agent Mart. https://agentmart.dev/skills/google-gke-workload-scaling (last updated 2026-10-24)