Cloud Infrastructure Documentation

This document provides a comprehensive overview of cloud infrastructure design, security, scaling, and automation using platforms such as AWS, Microsoft Azure, and Google Cloud Platform (GCP). It is structured for DevOps teams, system architects, and administrators managing scalable and secure cloud environments.

Cloud Architecture Models

Three leading providers (AWS, Azure, GCP) offer similar core services but vary in tools, billing models, and ecosystem depth. Common architecture patterns include:

• Multi-tiered architecture: Web, application, and database layers separated
• Event-driven architecture: Built using services like AWS Lambda or Azure Functions
• Microservices-based: Containerized apps using Kubernetes (EKS, AKS, GKE)
• Hybrid cloud: Mix of on-premise and cloud infrastructure

IAM & Permission Management

Identity and Access Management (IAM) is foundational. Best practices include:

• Use least privilege principles
• Group roles and assign policies to roles
• Use SSO integration with corporate identity providers
• Audit all privilege escalations

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": ["s3:GetObject"],
      "Resource": "arn:aws:s3:::example-bucket/*"
    }
  ]
}

Backup & Scaling Policies

All cloud-native applications should include automated backup and scalable infrastructure:

• Enable daily snapshots of databases
• Use autoscaling groups for compute services (EC2, App Service, etc.)
• Distribute load with managed load balancers
• Use lifecycle policies to delete old backups

Serverless vs IaaS

Serverless: Fully managed compute, great for variable loads and low ops overhead

• No server provisioning or maintenance
• Pay-per-use billing
• Cold start latency concerns

IaaS (Infrastructure as a Service): Offers full control and flexibility, better for consistent workloads.

• Full access to OS-level configurations
• Longer setup time
• Manual scaling or autoscale groups

Deployment Pipelines

Automating deployments increases speed and reduces human error. Typical pipeline includes:

1. Code commit (GitHub, GitLab, Bitbucket)
2. Continuous Integration (CI) with tools like Jenkins or GitHub Actions
3. Automated testing
4. Artifact packaging and containerization (Docker)
5. Deploy to staging and production via IaC tools like Terraform

Monitoring & Logging

Every cloud deployment must include observability features:

• Enable AWS CloudWatch / Azure Monitor / GCP Stackdriver
• Set thresholds and alarms
• Log shipping to centralized systems (ELK, Datadog, Splunk)
• Track billing metrics

Cloud Security Best Practices

• Use security groups and NSGs to limit traffic
• Encrypt data at rest and in transit
• Enable multi-factor authentication (MFA)
• Perform regular vulnerability scans

Cost Management

• Use cost calculators before deployment
• Set up budgets and cost alerts
• Use spot instances/preemptible VMs when appropriate
• Tag resources for cost allocation

Recommended Tools

• Terraform for Infrastructure as Code
• Jenkins / GitHub Actions for CI/CD
• Kubernetes / Helm for orchestration
• Cloudflare / AWS WAF for security
• Grafana / Prometheus for metrics