Google Cloud Walkthrough: Implementing Load Balancing on Compute Engine

This walkthrough documents a comprehensive series of Google Cloud Platform labs focused on implementing load balancing solutions using Compute Engine. The progression moves systematically from basic virtual machine provisioning through advanced load balancing configurations, providing practical experience with GCP’s infrastructure capabilities.

Lab 1: Create a Virtual Machine

The foundation of any load balancing implementation begins with understanding virtual machine provisioning and management within Google Cloud Platform.

Objective of the lab

Provision a Linux-based virtual machine and establish secure connectivity using Google Cloud’s integrated tools.

Implementation Steps

1. Access Compute Engine Console

   • Navigate to Compute Engine > VM instances
   • Select Create Instance to initiate the provisioning workflow

2. Configure Virtual Machine Specifications

   • Instance Name: linux-vm-lab1
   • Region/Zone: us-central1-a
   • Machine Type: e2-micro
   • Boot Disk: Debian GNU/Linux 11 (Bullseye)
   • Firewall: Enable HTTP and HTTPS traffic

3. Deploy and Connect

   • Click Create to provision the instance
   • Once active, establish connection via browser-based SSH

Key Observations

The browser-based SSH functionality eliminates local configuration requirements while maintaining security standards. This approach proves particularly valuable for rapid provisioning and initial server configuration tasks.

Lab 2: Compute Engine Quickstart - Windows

Building upon Linux VM experience, this lab explores Windows Server provisioning to demonstrate cross-platform capabilities and alternative access methodologies.

Lab Objective

Deploy a Windows Server instance and configure Remote Desktop Protocol (RDP) access through Google Cloud’s security framework.

Implementation Steps

1. Initiate Windows Instance Creation

   • Return to VM instances > Create Instance
   • Configure Windows-specific parameters:
     • Instance Name: windows-vm-lab2
     • Boot Disk: Windows Server 2019 Datacenter
     • Machine Type: e2-standard-2 (minimum recommended for Windows)

2. Security Configuration

   • After successful deployment, select Set Windows Password
   • Generate secure credentials through Google Cloud’s password management system
   • Document credentials for RDP access

3. Establish Remote Connection

   • Configure local Remote Desktop Connection client
   • Connect using generated credentials and external IP address

Key Observations

Google Cloud’s automated password generation provides secure access while streamlining Windows Server deployment. This functionality supports enterprise environments requiring mixed Linux and Windows infrastructure.

Lab 3: Getting Started with Cloud Shell and Networking

This lab transitions from console-based management to command-line infrastructure automation, introducing Google Cloud Shell and fundamental networking concepts.

Lab Objective

Develop proficiency with Google Cloud Shell and implement basic networking configurations using the gcloud CLI.

Cloud Shell Activation

Access Cloud Shell via the terminal icon in the Google Cloud Console header. This provides a pre-configured environment with:

• gcloud CLI tools
• Standard development utilities
• Persistent storage across sessions

Networking Implementation

Create Firewall Rules for HTTP Traffic:

gcloud compute firewall-rules create allow-http-traffic \
  --allow tcp:80 \
  --source-ranges 0.0.0.0/0 \
  --target-tags http-server \
  --description "Allow HTTP traffic to tagged instances"

Create Firewall Rules for Load Balancer Health Checks:

gcloud compute firewall-rules create allow-health-check \
  --allow tcp:80 \
  --source-ranges 130.211.0.0/22,35.191.0.0/16 \
  --target-tags http-server \
  --description "Allow Google Cloud health check traffic"

Key Observations

Cloud Shell provides consistent environment management across projects and regions. The CLI approach enables infrastructure-as-code practices and supports automation requirements for production environments.

Lab 4: Set Up Network Load Balancers

This lab implements Layer 4 (TCP/UDP) load balancing to distribute traffic across multiple backend instances within a single region.

Objective

Configure a regional network load balancer to distribute TCP traffic across multiple Compute Engine instances.

Infrastructure Preparation

Create Multiple Backend Instances:

# Create instance template
gcloud compute instance-templates create web-server-template \
  --machine-type e2-micro \
  --image-family debian-11 \
  --image-project debian-cloud \
  --tags http-server \
  --metadata startup-script='#!/bin/bash
    apt-get update
    apt-get install -y apache2
    echo "Web Server $(hostname)" > /var/www/html/index.html
    systemctl start apache2'

# Create managed instance group
gcloud compute instance-groups managed create web-server-group \
  --base-instance-name web-server \
  --size 3 \
  --template web-server-template \
  --zone us-central1-a

Network Load Balancer Configuration

Create Health Check:

gcloud compute health-checks create tcp tcp-health-check \
  --port 80 \
  --check-interval 10s \
  --timeout 5s \
  --healthy-threshold 2 \
  --unhealthy-threshold 3

Configure Backend Service:

gcloud compute backend-services create web-backend-service \
  --protocol TCP \
  --health-checks tcp-health-check \
  --region us-central1

Create Load Balancer:

gcloud compute forwarding-rules create web-lb-forwarding-rule \
  --region us-central1 \
  --ports 80 \
  --backend-service web-backend-service

Key Observations

Network load balancers operate at the transport layer, providing high-performance traffic distribution with minimal latency overhead. This approach suits applications requiring session persistence and direct server return capabilities.

Lab 5: Implement Load Balancing on Compute Engine - Challenge Lab

The challenge lab synthesises previous learning into a comprehensive load balancing solution, incorporating HTTP load balancing, auto-scaling, and multi-region deployment strategies.

Lab Objective

Deploy a global HTTP(S) load balancer with auto-scaling capabilities and implement advanced traffic management policies.

Global HTTP Load Balancer Implementation

Create Global Backend Service:

gcloud compute backend-services create global-web-backend \
  --protocol HTTP \
  --health-checks http-health-check \
  --global

Configure URL Map:

gcloud compute url-maps create web-url-map \
  --default-service global-web-backend

Create HTTP Proxy:

gcloud compute target-http-proxies create web-http-proxy \
  --url-map web-url-map

Establish Global Forwarding Rule:

gcloud compute forwarding-rules create web-global-forwarding-rule \
  --global \
  --target-http-proxy web-http-proxy \
  --ports 80

Auto-Scaling Configuration

gcloud compute instance-groups managed set-autoscaling web-server-group \
  --max-num-replicas 10 \
  --min-num-replicas 2 \
  --target-cpu-utilization 0.6 \
  --zone us-central1-a

Key Observations

Global HTTP load balancers provide intelligent traffic routing based on geographic proximity and backend capacity. The integration with Cloud CDN and auto-scaling capabilities enables enterprise-grade application delivery with optimal performance characteristics.

Architecture Insights and Best Practices

Through this progressive lab series, several architectural principles emerge:

1. Load Balancer Selection Strategy

• Network Load Balancers: Optimal for TCP/UDP traffic requiring minimal latency
• HTTP(S) Load Balancers: Suitable for web applications requiring content-based routing

2. Scalability Considerations

• Auto-scaling policies should account for application startup time
• Health check configurations must balance responsiveness with stability

3. Security Implementation

• Firewall rules should follow least-privilege principles
• Health check source ranges require specific configuration for Google Cloud infrastructure

Conclusion

This comprehensive walkthrough demonstrates the progression from basic virtual machine management to sophisticated load balancing implementations. The combination of console-based learning and CLI automation provides a complete foundation for production-ready Google Cloud infrastructure deployment.

The skills developed through these labs directly apply to enterprise scenarios requiring high availability, scalability, and performance optimisation. The systematic approach ensures thorough understanding of both individual components and integrated system architecture.

Future exploration should focus on advanced topics including Cloud CDN integration, SSL certificate management, and multi-cloud traffic distribution strategies.