GCP HTTP Load Balancer with MIG Backends

Global HTTP(S) load balancer routes traffic via URL map to a managed instance group running Apache. Firewall rules and h...

103 views
Viewed 103 times
0

Create a free account to remix this architecture

Sign up freeor sign in

GCP HTTP Load Balancer with MIG Backends

Global HTTP(S) load balancer routes traffic via URL map to a managed instance group running Apache. Firewall rules and health checks ensure only healthy VMs receive requests.

intermediate
GCP

Tags

#gcp
#http-load-balancing
#managed-instance-group
#health-checks
#compute-engine

Architecture Description

1. VM Instances (Compute Engine) You created three individual VMs: www1, www2, www3 Each has Apache web server installed and serves a unique homepage (<h3>Web Server: wwwX</h3>). Tagged with network-lb-tag so firewall rules can target them. Purpose: Serve HTTP traffic. 2. Firewall Rules www-firewall-network-lb Allows incoming HTTP traffic (port 80) to VMs with tag network-lb-tag. fw-allow-health-check Allows incoming traffic from Google health check IP ranges to VMs tagged allow-health-check (so the load balancer knows which VMs are healthy). 3. Instance Template & Managed Instance Group (MIG) Template: lb-backend-template Defines configuration for new VMs (Apache, SSL enabled, metadata script serving hostname). Managed Instance Group: lb-backend-group 2 instances created from the template in us-west1-a. Ensures autoscaling, autohealing, and consistency. Purpose: Backend for the application load balancer. 4. Health Check http-basic-check Regularly pings the backend VMs on port 80. Only healthy instances get traffic from the load balancer. 5. Load Balancer (Application L7) Frontend Global static IP: lb-ipv4-1 HTTP traffic received via forwarding rule: http-content-rule HTTP Proxy http-lb-proxy routes traffic to URL map URL Map web-map-http sends requests to backend service: web-backend-service Backend Service web-backend-service connected to MIG lb-backend-group Only sends traffic to healthy instances (health-checked) Purpose: Distribute user traffic efficiently across backend VMs. 6. Traffic Flow User → Global IP (lb-ipv4-1) Forwarding rule → HTTP Proxy (http-lb-proxy) Proxy → URL Map (web-map-http) URL Map → Backend Service (web-backend-service) Backend Service → MIG VMs (lb-backend-group) → Apache serves web page Health check ensures only healthy VMs get traffic

Community Discussion

Sign in to join the discussion

Sign in to comment

Be the first to comment

Share your thoughts on this architecture

AI Analysis(1)

AI Review
Tradeoff

2 months ago

I appreciate the detailed architecture you've designed using GCP's HTTP Load Balancer and Managed Instance Groups (MIGs). However, I have concerns about the reliance on individual VM instances (www1, www2, www3) alongside the MIG.

MA

Moustafa Allouhaibi

@af9593

View portfolio
Try it in the Playground

Open an interactive version — fork it, generate AI variants, or share it with your team.

What You'll Get

Editable diagram in your workspace
Interactive cost estimates
AI-powered customization
Save and share your version

Start Customizing

Make this template your own

Takes 30 seconds • No credit card required

Share

Statistics

Views
103
Clones
0
Likes
0

Details

Category
web app

Estimated monthly cost

$36.14/month

Published
2/9/2026

Services Used

9 cloud services in this architecture

GCEInstance
InstanceTemplate
InstanceGroup
GCPFirewall
Unknown
CloudLB
Gcp-proxy
Gcp-urlmap
Gcp-bes

Ready to build this?

Clone this architecture into your workspace and deploy it to your cloud account.

Takes 30 seconds • No credit card required

You Might Also Like

Global E-Commerce Platform with Multi-Cloud Failover
194

Global E-Commerce Platform with Multi-Cloud Failover

Create a global e-commerce platform with multi-cloud redundancy. Use AWS in us-east-1 for the primary application with…

AWS
AZURE
GCP
End-to-End ML Platform with AutoML
190

End-to-End ML Platform with AutoML

Build an ML training and deployment platform on GCP with Vertex AI, Cloud Storage, BigQuery, Cloud Functions, and…

GCP
Production Microservices on Kubernetes
166

Production Microservices on Kubernetes

Design a microservices architecture on GCP with GKE cluster, Cloud Load Balancing, Cloud SQL, Pub/Sub for messaging,…

GCP
Private VPC Web App with GPU AI Processing on GCP
97

Private VPC Web App with GPU AI Processing on GCP

High-Level Overview Public Access: The only public entry point is via HTTPS through a Cloud Load Balancer, which…

GCP