FreeCompute_24_00008 - itnett/FTD02H-N GitHub Wiki
IP Address Plan for Year 3 (continued):
Google Cloud (cont'd):
Prod: 192.168.3.x range (e.g., 192.168.3.0/24 for AI/ML models).
Dev: 192.168.4.x range (e.g., 192.168.4.0/24 for orchestration and development environments).
This IP address scheme ensures there are no conflicts between different cloud environments and keeps each service isolated within its range.
Milestone 5: Implement Multi-Cloud Failover and High Availability
Objective: Design and implement a highly available (HA) architecture with multi-cloud failover strategies to ensure application uptime and fault tolerance.
Tools:
AWS Route 53 (for DNS-based failover)
Google Cloud Load Balancing
Azure Traffic Manager
Action Plan:
DNS-Based Failover:
Use AWS Route 53 to set up DNS-based failover routing policies, ensuring traffic automatically switches between AWS, GCP, and Azure in case of failures.
Global Load Balancing:
Implement global load balancing across multiple clouds using Google Cloud Load Balancing and Azure Traffic Manager.
Health Checks:
Configure health checks to monitor the availability of services on each cloud provider, ensuring automatic failover if an instance or service becomes unavailable.
Replicate Data Across Clouds:
Use AWS S3 Cross-Region Replication, Google Cloud Storage, and Azure Blob Replication to ensure your data is replicated and available in case of a regional failure.
Example Setup:
Route 53 Failover Configuration:
- name: MyAppFailover type: "failover" primary: region: AWS service: EC2 secondary: region: GCP service: GCE
Outcome: Your application is now highly available with automatic failover and load balancing across AWS, GCP, and Azure, ensuring uptime even in the event of outages.
Milestone 6: Implement Advanced CI/CD with GitHub Actions and Jenkins
Objective: Scale up your CI/CD pipelines by integrating Jenkins with GitHub Actions for more complex workflows, ensuring continuous testing and deployment across multiple clouds.
Tools:
GitHub Actions (for pipeline automation)
Jenkins (for complex multi-stage workflows)
Terraform (for infrastructure provisioning)
Action Plan:
Jenkins Setup:
Set up a Jenkins server and integrate it with your GitHub repository for advanced CI/CD workflows.
GitHub Actions + Jenkins:
Use GitHub Actions for lightweight jobs (e.g., testing, linting) and Jenkins for more complex, multi-stage deployment pipelines.
Example Multi-Stage Jenkins Pipeline:
pipeline { agent any stages { stage('Build') { steps { sh 'npm install' } } stage('Test') { steps { sh 'npm test' } } stage('Deploy to AWS') { steps { sh 'terraform apply -target=aws_instance.my_instance' } } stage('Deploy to GCP') { steps { sh 'gcloud compute instances create my-instance' } } } }
Outcome: By using both GitHub Actions and Jenkins, you have a scalable CI/CD pipeline that can deploy to multiple clouds simultaneously, ensuring continuous delivery across complex workflows.
Tagging, Naming, and IP Address Plan for Milestones in Year 3
Tagging Strategy for Advanced CI/CD, Multi-Cloud Failover, and AI/ML
Tags for CI/CD Resources:
- Provider: AWS, Azure, GCP
- Type: CI/CD, Jenkins, GitHubActions
- Environment: Dev, Test, Prod
- Project: MultiCloudCI
- Phase/Year: Year3
- Owner: YourName
Tags for AI/ML Resources:
- Provider: AWS, Azure, GCP
- Type: AI/ML, ModelTraining, PredictionAPI
- Environment: Dev, Test, Prod
- Project: AIApp
- Phase/Year: Year3
- Owner: YourName
Naming Conventions for Milestones:
[Provider]-[ServiceType]-[Environment]-[Project]-[Region]-[Phase]
Example Names for AI/ML Resources:
AWS-AI-PROD-MLModelTraining-USWEST1-YEAR3 GCP-AI-DEV-DataPrediction-USEAST1-YEAR3 AZURE-AI-PROD-TextAnalysis-WESTEUROPE-YEAR3
Example Names for CI/CD Resources:
GCP-CICD-DEPLOY-PROD-DataProcessing-YEAR3 AWS-CICD-TEST-AIApp-YEAR3 AZURE-CICD-BUILD-MLPipeline-YEAR3
IP Address Plan for Year 3:
AWS:
Prod: 10.0.4.x range (e.g., 10.0.4.0/24 for CI/CD pipelines).
Dev: 10.1.4.x range (e.g., 10.1.4.0/24 for testing infrastructure).
Azure:
Prod: 10.2.4.x range (e.g., 10.2.4.0/24 for multi-cloud failover setups).
Dev: 10.3.4.x range (e.g., 10.3.4.0/24 for AI/ML development).
Google Cloud:
Prod: 192.168.5.x range (e.g., 192.168.5.0/24 for AI/ML model deployments).
Dev: 192.168.6.x range (e.g., 192.168.6.0/24 for development services and orchestration).
Summary for Year 3:
Automate Everything: Fully automate multi-cloud infrastructure with Terraform and orchestrate using Ansible.
Advanced CI/CD: Use both GitHub Actions and Jenkins for multi-stage deployment pipelines across cloud providers.
Optimize Costs: Implement cost management tools to prevent overspending and stay within free-tier limits.
Multi-Cloud Failover: Design and implement a failover strategy with DNS-based routing and load balancing across AWS, GCP, and Azure.
Leverage AI/ML: Build and deploy AI-powered applications using cloud-native machine learning services like AWS SageMaker, Google AI Platform, and Azure Cognitive Services.
Year 3 sets you up with highly automated, scalable, and cost-optimized infrastructure, preparing for the next step of mastering advanced cloud architectures in Year 4.