FreeCompute_24_00005 - itnett/FTD02H-N GitHub Wiki

Here’s a comprehensive tagging, naming, and IP address plan for organizing cloud resources, services, and accounts across different cloud providers over the 4-year period. The goal is to ensure consistency, ease of identification, and scalability across platforms, especially as you manage multi-cloud environments for CI/CD, storage, compute, and services like AI and APIs.

Tagging and Naming Strategy Overview

The strategy revolves around naming conventions that identify:

Provider: AWS, Azure, GCP, DO (DigitalOcean), etc.

Resource Type: Compute, Storage, Networking, AI, API, CI/CD, etc.

Environment: Dev (Development), Test (Testing), Prod (Production), etc.

Project/Service Name: Related to the specific application, project, or service.

Year/Phase: Each resource is tagged based on the year or phase to keep track of the evolution over time.

Tagging Strategy

Tags will help manage, filter, and identify resources across multiple providers. Consistent tags allow you to organize your resources based on functionality, cost, project, or environment.

Example Tag Set for Resources:

Provider: AWS, Azure, GCP, DO, etc.
Type: Compute, Storage, Network, API, AI, CI/CD
Environment: Dev, Test, Prod
Project: ProjectA, ProjectB
Phase/Year: Year1, Year2, Year3, Year4
Owner: YourName
CostCenter: Free, Paid, Credit

Sample Tags for a Compute Resource in AWS:

Provider: AWS Type: Compute Environment: Dev Project: MyApp Phase/Year: Year1 Owner: YourName CostCenter: Free

Sample Tags for a CI/CD Pipeline in Azure:

Provider: Azure Type: CI/CD Environment: Prod Project: MyApp Phase/Year: Year2 Owner: YourName CostCenter: Free

Naming Conventions

Naming conventions should be standardized across all cloud platforms to ensure resources are easily identifiable.

General Structure:

[Provider]-[ServiceType]-[Environment]-[Project]-[Region]-[Phase]

Example Naming Convention by Resource Type:

Compute:

AWS-COMPUTE-DEV-MyApp-USWEST1-YEAR1 GCP-COMPUTE-PROD-APIService-USCENTRAL1-YEAR2 AZURE-COMPUTE-TEST-DataProcessing-WESTEUROPE-YEAR3

Storage:

AWS-STORAGE-DEV-BackupService-USWEST2-YEAR1 AZURE-STORAGE-PROD-DataWarehouse-NORTHEUROPE-YEAR4 GCP-STORAGE-TEST-FileArchive-USEAST1-YEAR2

Networking:

AWS-NETWORK-PROD-VPC1-USWEST1-YEAR1 GCP-NETWORK-DEV-VPC1-USCENTRAL1-YEAR3

AI/ML Services:

AZURE-AI-PROD-MLService-WESTEUROPE-YEAR4 AWS-AI-DEV-AIPipeline-USWEST2-YEAR2

CI/CD Pipelines:

AZURE-CICD-DEV-DeployPipeline-WESTEUROPE-YEAR1 GCP-CICD-PROD-BuildPipeline-USEAST1-YEAR2

API Services:

AWS-API-DEV-RESTAPI-USWEST1-YEAR2 AZURE-API-PROD-GraphQL-WESTEUROPE-YEAR4

Username and Access Plan

To ensure that user accounts are standardized and can be easily tracked across different platforms, here is a suggested username convention that can be adapted for CI/CD systems, service accounts, and administrative access.

General Structure:

[Platform]-[Role]-[Environment]-[Initials]-[Year]

Example Usernames:

Administrator Access:

AWS-ADMIN-PROD-JD-YEAR1 AZURE-ADMIN-DEV-JD-YEAR1 GCP-ADMIN-TEST-JD-YEAR2

Service Accounts (for automation):

AWS-SVC-CICD-DEPLOY-YEAR1 AZURE-SVC-ML-AUTOMATION-YEAR2 GCP-SVC-AI-TASKRUNNER-YEAR3

Developer Accounts:

AWS-DEV-PROD-ABC-YEAR1 AZURE-DEV-DEV-XYZ-YEAR2

IP Address Planning

Planning IP addresses is important when dealing with multi-cloud environments to avoid conflicts and ensure ease of management. Here’s a suggested approach for IP address allocation in the different cloud environments:

IP Address Allocation Plan:

Use private IP ranges in the 10.x.x.x or 192.168.x.x ranges for internal resources.

Segment IP ranges by provider and environment to avoid conflicts.

Example IP Addressing Scheme:

AWS:

Prod: 10.0.x.x range (e.g., 10.0.1.0/24 for compute instances, 10.0.2.0/24 for storage).

Dev: 10.1.x.x range (e.g., 10.1.1.0/24 for compute instances, 10.1.2.0/24 for storage).

Azure:

Prod: 10.2.x.x range (e.g., 10.2.1.0/24 for compute instances).

Dev: 10.3.x.x range (e.g., 10.3.1.0/24 for VMs, 10.3.2.0/24 for storage).

Google Cloud:

Prod: 192.168.1.x range (e.g., 192.168.1.0/24 for compute instances).

Dev: 192.168.2.x range (e.g., 192.168.2.0/24 for VMs).

Organizational Units (OU) and Folder Structure

For organizing your projects and environments, especially in platforms like AWS Organizations or GCP Folders, follow a structured OU naming convention. This is especially useful for segregating billing, monitoring, and permissions.

General Structure for OUs or Folders:

[Provider]/[Environment]/[Project]/[ServiceType]/[Phase/Year]

Example Structure:

AWS OU Structure:

AWS/ ├── Prod/ │ ├── MyApp/ │ │ ├── Compute/ │ │ └── Storage/ │ └── APIService/ └── Dev/ └── DataProcessing/

GCP Folder Structure:

GCP/ ├── Prod/ │ └── MyApp/ │ ├── Compute/ │ └── Storage/ └── Test/ └── BackupService/

CI/CD Pipeline Naming Plan

For the CI/CD pipelines, the naming should be clear about what the pipeline is doing, which environment it deploys to, and which project it is part of.

General Structure:

[Platform]-[PipelineType]-[Environment]-[Project]-[Year]

Example CI/CD Pipeline Names:

Deploy Pipeline:

GCP-CICD-DEPLOY-PROD-MyApp-YEAR2 AWS-CICD-DEPLOY-DEV-APIService-YEAR1 AZURE-CICD-BUILD-TEST-DataProcessing-YEAR3

Test Pipeline:

AWS-CICD-TEST-PROD-MyApp-YEAR1 GCP-CICD-TEST-DEV-BackupService-YEAR2

Combining AI/ML Services, APIs, and Cloud Compute

When integrating AI/ML services with APIs across multiple cloud providers, the naming and resource tagging should clearly reflect the service type, cloud provider, and function.

Example Resource Names:

AWS AI Service:

AWS-AI-PROD-MLPipeline-USWEST1-YEAR3 AWS-API-PROD-MLInference-USWEST1-YEAR4

GCP AI & API Service:

GCP-AI-DEV-MLModel-USEAST1-YEAR2 GCP-API-DEV-APIEndpoint-USEAST1-YEAR2

Azure AI Service:

AZURE-AI-PROD-TextAnalytics-WESTEUROPE-YEAR3

Final Notes:

Ease of Identification: Each naming convention includes the cloud provider, resource type, environment, project, and year/phase, making it easy to identify and manage resources across different