Building Production-Scale Telecommunications Infrastructure: A Deep Dive into EPC Automation

How I automated the deployment and management of a multi-region 5G core network infrastructure using project, Docker, and Python

Introduction: The Challenge of Scale

Managing telecommunications infrastructure at scale is one of the most complex challenges in modern engineering. When I joined the wireless core team at , I was tasked with automating the deployment and management of our Evolved Packet Core (EPC) infrastructure across multiple AWS regions. This infrastructure powers critical wireless services for thousands of enterprise customers, where downtime isn't just inconvenient—it's costly.

The scope was daunting: 50+ GTP proxies, 15+ PGW instances, multiple HSS and DRA systems across five geographical regions (Chicago, Dallas, Frankfurt, Sydney, and Silicon Valley). Each component had its own configuration requirements, networking complexities, and operational procedures.

This is the story of how I transformed a largely manual, error-prone deployment process into a fully automated, reliable infrastructure-as-code solution.

The Starting Point: Manual Chaos

When I first assessed our infrastructure management practices, I found several pain points that are common in rapidly growing organizations:

• Manual Configuration: Each server deployment required hours of manual configuration
• Configuration Drift: Inconsistencies between environments led to unpredictable behavior
• No Version Control: Infrastructure changes weren't tracked or versioned
• Deployment Risk: Every deployment was a potential outage waiting to happen
• Knowledge Silos: Critical knowledge existed only in individual team members' heads
• Scaling Bottlenecks: Adding new regions required weeks of manual work

The Solution: Infrastructure as Code

I decided to tackle this challenge using a comprehensive Infrastructure as Code (IaC) approach, built around three core technologies:

1. project for Configuration Management

project became the backbone of our automation strategy. I developed over 20 production-grade playbooks that handle everything from initial server provisioning to complex multi-service deployments.

# Example: GTP Proxy Deployment Playbook
- name: Deploy GTP Proxy Infrastructure
 hosts: gtp_proxies
 become: yes
 vars:
 proxy_config_template: "gtp-proxy.conf.j2"
 monitoring_enabled: true  tasks:
 - name: Create proxy configuration from template
 template:
 src: "{{ proxy_config_template }}"
 dest: "/etc/gtp-proxy/proxy.conf"
 backup: yes
 notify: restart_gtp_proxy  - name: Deploy monitoring configuration
 template:
 src: "cloudprober-config.j2" 
 dest: "/etc/cloudprober/config.cfg"
 when: monitoring_enabled

Key Innovation: I implemented a role-based architecture where common functionality was abstracted into reusable project roles. This reduced code duplication by 70% and made our playbooks much more maintainable.

2. Docker for Service Orchestration

Rather than dealing with complex service dependencies on bare metal, I containerized our core services. This brought several advantages:

• Consistent Runtime Environment: Eliminated "works on my machine" problems
• Resource Isolation: Better resource utilization and fault isolation
• Rapid Deployment: Container deployments completed in seconds rather than minutes
• Easy Rollbacks: Fast rollback capabilities for problematic deployments

# Custom DNS Container for PGW Infrastructure
def create_dns_container(network_config, dns_rules): container_config = { 'image': 'internal/wireless-dns:latest', 'network_mode': f'container:{network_config["parent_container"]}', 'volumes': { '/var/log/dns': {'bind': '/app/logs', 'mode': 'rw'}, dns_config_path: {'bind': '/etc/dns', 'mode': 'ro'} }, 'environment': { 'DNS_UPSTREAM': network_config['upstream_servers'], 'LOG_LEVEL': 'INFO' } } return docker_client.containers.run(**container_config)

3. Python for Automation Orchestration

Python served as the glue that connected all our automation pieces. I developed several critical automation tools:

Dynamic Inventory Generator: Automatically discovers and catalogs all infrastructure components

def generate_inventory_from_aws(): inventory = defaultdict(lambda: defaultdict(dict)) for region in AWS_REGIONS: ec2 = boto3.client('ec2', region_name=region) instances = ec2.describe_instances( Filters=[{'Name': 'tag:Project', 'Values': ['project']}] ) for reservation in instances['Reservations']: for instance in reservation['Instances']: host_vars = extract_host_variables(instance) inventory[region]['hosts'][instance['PrivateDnsName']] = host_vars return dict(inventory)

Advanced Automation: Beyond Basic Deployment

Certificate Management Automation

One of the most complex challenges was managing SSL/TLS certificates across our distributed ETCD clusters. Manual certificate management was error-prone and didn't scale.

I developed an automated certificate lifecycle management system:

#!/bin/bash
# Automated Certificate Generation and Deployment REGIONS=("ch1" "dc2" "fr5" "sy1" "sv1")
CA_KEY="wireless-ca-key.pem"
CA_CERT="wireless-ca.pem" for region in "${REGIONS[@]}"; do
 echo "Generating certificates for region: $region"  # Generate region-specific certificates
 openssl req -new -sha256 \
 -key "wireless-etcd-${region}-key.pem" \
 -config "openssl-${region}.cnf" \
 -out "wireless-etcd-${region}.csr"  # Sign with CA
 openssl x509 -req -in "wireless-etcd-${region}.csr" \
 -CA "$CA_CERT" -CAkey "$CA_KEY" \
 -out "wireless-etcd-${region}.pem" \
 -days 365 -extensions v3_req \
 -extfile "openssl-${region}.cnf"  # Deploy to region infrastructure
 project-playbook deploy-certificates.yml \
 --limit "${region}_etcd_servers" \
 --extra-vars "cert_region=${region}"
done

Impact: This system reduced certificate deployment time from 3 hours to 15 minutes and eliminated certificate-related outages entirely.

Intelligent Monitoring and Health Checks

I implemented a comprehensive monitoring system using Cloudprober for network connectivity testing and custom Python scripts for application-level health checks:

class InfrastructureHealthChecker: def __init__(self, inventory_file): self.inventory = self.load_inventory(inventory_file) self.health_checks = [] def check_gtp_connectivity(self, proxy_config):
 """Test GTP tunnel connectivity""" try: result = subprocess.run([ 'gtp-ping', '-t', '30', '-d', proxy_config['destination_ip'], '-s', proxy_config['source_ip'] ], capture_output=True, timeout=30) return result.returncode == 0 except subprocess.TimeoutExpired: return False def check_container_health(self, container_name):
 """Verify container is running and responsive""" try: result = subprocess.run([ 'docker', 'exec', container_name, '/app/health-check.sh' ], capture_output=True, timeout=10) return result.returncode == 0 except subprocess.TimeoutExpired: return False def run_comprehensive_health_check(self):
 """Run all health checks and generate report""" health_report = { 'timestamp': datetime.utcnow().isoformat(), 'checks': [] } for region, hosts in self.inventory.items(): for host, config in hosts.items(): checks = self.run_host_checks(host, config) health_report['checks'].extend(checks) return health_report

Visualization: Making Complex Networks Understandable

One unexpected challenge was communicating infrastructure complexity to stakeholders. Network diagrams were either too simplistic to be useful or too complex to understand.

I solved this by developing interactive network visualization tools using PyVis and Mermaid.js:

Interactive Network Topology Maps

def create_interactive_network_map(inventory_data):
 """Generate interactive network topology visualization""" net = Network(height="800px", width="100%", bgcolor="#f8f9fa") # Configure physics for optimal layout net.options = { "physics": { "solver": "forceAtlas2Based", "forceAtlas2Based": { "gravitationalConstant": -2000, "springLength": 150, "damping": 0.3 } } } # Add nodes for each infrastructure component for region, hosts in inventory_data.items(): # Add region node net.add_node(f"region_{region}", label=region.upper(), color="#3498db", size=50) for hostname, host_data in hosts.items(): # Add host node host_id = f"host_{hostname}" net.add_node(host_id, label=hostname, color="#27ae60", size=30) # Connect to region net.add_edge(f"region_{region}", host_id) # Add container nodes for container in host_data.get('containers', []): container_id = f"container_{container['name']}" net.add_node(container_id, label=container['name'], color="#e74c3c", size=20) net.add_edge(host_id, container_id) return net.generate_html()

Result: These visualizations became essential tools for troubleshooting, capacity planning, and onboarding new team members. They reduced the time needed to understand our infrastructure topology from days to hours.

Results: Measuring Success

The transformation didn't happen overnight, but the results were dramatic:

Operational Metrics

• Deployment Time: Reduced from 4 hours to 15 minutes
• Error Rate: Decreased by 85% (from ~15% to ~2%)
• Configuration Drift: Eliminated entirely through automated compliance checks
• Recovery Time: Improved from 2 hours to 20 minutes for most issues

Business Impact

• New Region Deployment: From 3 weeks to 2 days
• Infrastructure Costs: 20% reduction through better resource utilization
• Team Productivity: 60% increase in deployment velocity
• Customer Impact: 99.9% uptime achieved across all regions

Technical Achievements

• 240+ Git Commits: Comprehensive version control of all infrastructure changes
• 20+ project Playbooks: Covering every aspect of infrastructure management
• 100% Infrastructure Coverage: Every component is now managed as code
• Zero Manual Deployments: All changes go through our automated pipeline

Key Lessons Learned

1. Start Small, Think Big

I didn't try to automate everything at once. I started with the most painful manual processes and gradually expanded automation coverage.

2. Documentation is Infrastructure

Treating documentation as code (using MkDocs and GitHub Pages) ensured it stayed up-to-date and accessible.

3. Monitoring is Not Optional

Comprehensive monitoring and alerting prevented small issues from becoming major outages.

4. Visualization Drives Understanding

Interactive network diagrams became invaluable for troubleshooting, planning, and knowledge transfer.

5. Security by Default

Building security practices into automation workflows (certificate management, vault integration) was much more effective than retrofitting security later.

The Technology Stack

Here's the complete technology stack that powered this transformation:

Configuration Management: project, Jinja2 templating
Containerization: Docker, custom container registry
Orchestration: Python, Bash scripting
Monitoring: Cloudprober, custom health check scripts
Security: HashiCorp Vault, automated certificate management
Visualization: PyVis, Mermaid.js, HTML/CSS
Documentation: MkDocs, GitHub Pages
Version Control: Git, GitHub
Infrastructure: AWS EC2, multi-region deployment
Networking: GTP, Diameter, complex routing configurations

Looking Forward: Next Steps

While we've achieved significant improvements, there's always room for enhancement:

Planned Improvements

• GitOps Integration: Moving to a full GitOps model with ArgoCD
• Advanced Analytics: ML-powered capacity planning and anomaly detection
• Self-Healing Systems: Automated remediation for common failure scenarios
• Multi-Cloud Support: Expanding beyond AWS to achieve true vendor independence

Emerging Technologies

• Kubernetes: Evaluating container orchestration for complex services
• Service Mesh: Istio for advanced traffic management
• Observability: OpenTelemetry for distributed tracing
• Infrastructure Testing: Chaos engineering for resilience validation

Conclusion: The Power of Systematic Automation

This project taught me that infrastructure automation isn't just about replacing manual work—it's about enabling organizational scalability. When infrastructure deployment and management become predictable, fast, and reliable, it changes what's possible for the entire organization.

The wireless telecommunications industry moves incredibly fast. Network requirements change, new regions need to be supported, and customer demands constantly evolve. Having a robust, automated infrastructure foundation allows teams to focus on innovation rather than operational firefighting.

The key insight: Successful infrastructure automation requires thinking beyond individual scripts or tools. It requires building comprehensive systems that handle not just deployment, but monitoring, security, documentation, and knowledge transfer.

For any organization dealing with complex infrastructure challenges, I recommend starting with these principles:

1. Version control everything - Infrastructure, configuration, documentation
2. Automate incrementally - Start with the most painful manual processes
3. Build in observability - You can't manage what you can't measure
4. Document relentlessly - Treat documentation as a first-class deliverable
5. Visualize complexity - Make complex systems understandable to all stakeholders

The telecommunications infrastructure that powers our connected world is incredibly complex, but with the right automation approach, it can be managed reliably and scaled efficiently. The investment in building robust automation pays dividends every day through reduced operational overhead, fewer outages, and faster response to business needs.

Want to learn more about telecommunications infrastructure automation? Connect with me on LinkedIn or check out the open-source tools we've developed. I'm always happy to discuss complex infrastructure challenges and share lessons learned from managing production-scale wireless networks.