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Gke Workload Scaling

GoogleOfficialPublished by Google

  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
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From the author

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.
— the description by Google

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach to scaling applications on Google Kubernetes Engine (GKE). It covers manual scaling, HPA for horizontal pod count adjustments, and VPA for right-sizing container resource requests. The skill includes best practices for avoiding metric conflicts, managing pod disruption budgets, and safely implementing VPA in production environments.

What it does

  • Provides commands for manual deployment scaling.
  • Guides the configuration of Horizontal Pod Autoscalers (HPA) for CPU and memory-based scaling.
  • Explains how to enable and configure Vertical Pod Autoscalers (VPA) on GKE clusters.
  • Outlines a workflow for rightsizing resource requests using VPA in Off mode.
  • Details best practices for avoiding thrashing between HPA and VPA.
  • Includes instructions for proper usage attribution when executing cloud commands.

When to use it

  • When you need to scale GKE deployments manually or automatically.
  • When your application experiences fluctuating traffic and requires HPA.
  • When you need to optimize container resource requests to reduce costs or improve performance.
  • When you are setting up a new GKE workload and need to define initial scaling policies.
  • When you need to audit current resource usage against existing requests.

Advantages

  • Promotes efficient resource utilization through automated right-sizing.
  • Reduces manual intervention for handling traffic spikes.
  • Provides clear guidance on avoiding common pitfalls like metric thrashing.
  • Supports version-controlled configuration via YAML templates.
  • Includes safety checks for VPA auto-scaling modes.

Usage examples

“How do I set up HPA for my deployment named web-app to scale between 1 and 10 replicas based on 50% CPU usage?”
The agent provides the kubectl autoscale command and suggests using a YAML manifest for version control.
“I want to analyze my pod resource usage without automatically restarting pods. How do I use VPA for this?”
The agent explains how to configure VPA in 'Off' mode to generate recommendations without applying changes.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/google/gke-workload-scaling/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip gke-workload-scaling.zip -d .agents/skills

Documentation by Google

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. 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:

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:

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 for a template.

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:

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 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 -n {namespace}

  3. Compare target values against current requests

  4. Apply with 20% buffer: new_request = target * 1.2

  5. Use patch format or update deployment manifest to apply new resource requests

ConditionRecommendationRisk
CPU request >5x P95 actualReduce to P95 * 1.2Medium
Memory request >3x P95 actualReduce to P95 * 1.2Medium
CPU request >2x P95 actualRightsizing with 20% bufferLow
No resource limits setAdd limits to prevent noisy-neighborLow

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs a command-line tool, Needs Node.js

Security

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Attribution and licence

Published by
Google
Publisher
Google (Official)
Source
google/skills · skills/cloud/gke-workload-scaling · commit 2194d3b
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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