Auto-Scaling Microservice: Homelab + AWS Hybrid

A production-style microservice that scales automatically from my home lab to AWS during real-world traffic spikes.

Role: Cloud & DevOps Engineer
Stack: Kubernetes · Docker · AWS EC2/ECS · Application Load Balancer · Prometheus/Grafana · GitHub Actions · IaC
Environment: Homelab + AWS Hybrid

TL;DR

• Built a fully containerized microservice running inside my homelab Kubernetes cluster, with the ability to “burst” into AWS when traffic exceeds local capacity.

• Configured auto-scaling on both sides: Kubernetes HPA in the homelab and AWS Auto Scaling in the cloud.

• Designed CI/CD pipelines that build, test, deploy, and update both environments from one GitHub Actions workflow.

• Implemented full observability: metrics, dashboards, logs, alerts, and health checks.

• Stress-tested the system using load-testing tools to validate scaling, failover behavior, and latency.

The Problem

Most small teams operate on a single VM or a cluster that’s “fine… until it isn’t.”
Traffic spikes hit, CPU jumps, response times tank, and suddenly someone is panic-logging into a server at midnight trying to figure out why everything is on fire.

I wanted to solve a realistic version of that problem:
What if a service could start in a local environment and seamlessly expand to AWS the moment demand increases — without human intervention?

The goal wasn’t just to “learn Kubernetes.”
The goal was to design a system that behaves like production: resilient, observable, automated, and scalable.

Solution Overview

I built a microservice that runs in a Kubernetes cluster inside my homelab, fronted by an ingress and monitored with Prometheus.
When the cluster approaches its resource limits or incoming traffic spikes, the system can scale, and if needed, AWS acts as the overflow zone.

AWS mirrors the homelab architecture using EC2 or ECS, with an Application Load Balancer distributing traffic across both environments.
A single CI/CD pipeline handles builds, tests, and deployments for both targets, ensuring consistency and reproducibility through Infrastructure as Code.

The end result: a hybrid microservice that behaves like a tiny production platform, not just a lab experiment.

Architecture

Microservice Layer

• Microservice built with a lightweight framework (FastAPI / Express / Go — stack-agnostic design).

• Packaged as a Docker image.

• Deployed to Kubernetes in the homelab and to AWS (ECS/EKS or EC2 + containers).

• Stateless design, so scaling is clean and predictable.

Homelab Cluster

• Local Kubernetes cluster (k3s or kubeadm) running on VMware VMs provisioned via Vagrant.

• Ingress Controller + Service + Deployment patterns.

• Kubernetes Horizontal Pod Autoscaler based on CPU and request volume.

AWS Environment

• Mirrored environment in AWS with an Application Load Balancer, target groups, and auto-scaling policies.

• Infrastructure provisioned via CloudFormation/Terraform stored in GitHub.

• Health checks ensure only healthy targets receive traffic.

Traffic Handling

• Traffic hits ALB ingress or homelab ingress.

• If local pods saturate, AWS tasks/instances take over additional load.

• Zero downtime thanks to health checks and rolling updates.

CI/CD & Automation

I structured the pipeline in GitHub Actions to behave like an actual production workflow:

• On each pull request:

  • Run linting, unit tests, and container build verification.

• On merge to main:

  • Build the Docker image.

  • Push to the container registry.

  • Apply Infrastructure as Code changes.

  • Deploy to the homelab cluster using kubeconfig secrets.

  • Deploy to AWS using environment-specific manifests or ECS task definitions.

  • Validate deployment via smoke tests.

This creates a single, unified pipeline that keeps both environments deployed, tested, and healthy — no manual steps, no “SSH and pray.”

Auto-Scaling & Resilience

• Kubernetes HPA automatically adds pods as CPU or traffic increases.

• AWS Auto Scaling Groups/ECS Service scaling expands cloud capacity during peak load.

• Defined min/max capacity ranges to balance cost and responsiveness.

• Liveness and readiness probes protect against routing traffic to unhealthy pods.

• ALB performs independent health checks and routes traffic only to healthy targets.

• Rollbacks are triggered automatically if deployments fail or health checks downgrade.

Overall, the system flexes under pressure but never breaks.

Observability & Monitoring

I treated observability like a first-class feature:

• Metrics scraped through Prometheus from both local and cloud services.

• Dashboards built in Grafana to track CPU, memory, request rate, latency, and error counts.

• Centralized logs via CloudWatch or a local Loki/ELK stack.

• Alerts configured for:

  • Increased error rate

  • Rising latency

  • Pod/node restarts

  • Failing health checks

This gave me a clear view of how the system behaves under load — and where bottlenecks emerge.

Load Testing & Results

To validate performance, I used load-testing tools (k6/Locust/JMeter) to simulate real-world traffic:

• Baseline load to measure normal stability.

• Sudden 10× traffic spikes.

• Sustained high concurrency scenarios.

Results:

• System scaled from a small starting footprint to multiple pods/instances within minutes.

• P95 latency stayed stable even during traffic spikes after scaling kicked in.

• No failed health checks at the load balancer.

• AWS resources only expanded when the homelab was truly saturated.

In other words, the service behaved like a real application under real pressure.

Business Impact (If Deployed in a Real Company)

If this architecture were powering a SaaS or e-commerce product, the advantages would be immediate:

• Auto-scaling eliminates outages during promotions or customer onboarding rushes.

• Fewer late-night incidents due to automatic failover and health-aware routing.

• Faster, safer deployments powered by automated CI/CD.

• Lower cloud spend because AWS acts as an overflow instead of the primary engine.

• More reliable releases, fewer fire drills, and a happier engineering team.

My Role

• Designed the hybrid homelab + AWS architecture.

• Built Docker images and Kubernetes manifests.

• Provisioned AWS infrastructure using IaC.

• Implemented GitHub Actions pipelines for automated deployments.

• Set up observability dashboards, alerting rules, and health checks.

• Executed load tests and iterated based on performance results.

What I Learned

• How to design auto-scaling across two environments with different constraints.

• Why observability must be designed up front — not bolted on later.

• How health checks, scaling policies, and deployment strategies work together.

• How to use CI/CD to maintain parity across environments without configuration drift.

• The real-world value of practicing failure scenarios and chaos testing.

Next Iteration

• Add canary deployments with progressive rollouts.

• Introduce feature flags to test changes safely in production-like environments.

• Add cost dashboards to track AWS spend vs. traffic patterns.

• Expand this into a multi-service architecture to demonstrate service-to-service communication.