FreeCompute_24_00006 - itnett/FTD02H-N GitHub Wiki

Outline for Year 2: Scaling Applications and Exploring Advanced Cloud Services

Focus: In Year 2, we will focus on scaling applications, exploring advanced cloud services, automating infrastructure using Terraform and CI/CD pipelines, and leveraging serverless, container orchestration, and monitoring tools. The goal is to enhance multi-cloud setups, ensuring optimal use of free-tier services while integrating advanced services like Kubernetes, APIs, and AI.

🛠️ Core Objectives for Year 2:

Scale Applications: Expand from basic cloud services to more advanced cloud-native services like Kubernetes and serverless architectures.

Explore Advanced Services: Utilize AI, APIs, and monitoring tools across multiple platforms.

Enhance CI/CD Pipelines: Set up pipelines that automatically scale, monitor, and deploy to multi-cloud environments.

Optimize Costs: Ensure that free-tier services are fully utilized, and that there is no overlap or cost overrun between cloud providers.

Key Milestones for Year 2:

Milestone 1: Scale Applications with Kubernetes and Containers

Objective: Implement containerization across multiple clouds using Kubernetes for orchestrating containerized apps and services.

Tools:

AWS EKS (Elastic Kubernetes Service)

GCP GKE (Google Kubernetes Engine)

Azure AKS (Azure Kubernetes Service)

Action Plan:

Deploy and manage multi-cloud Kubernetes clusters.

Containerize existing apps using Docker.

Scale containerized applications across multiple clouds.

Example Setup:

Create a Kubernetes cluster on AWS and deploy a containerized web application.

Extend Kubernetes clusters to GCP GKE and Azure AKS for a fully multi-cloud setup.

Milestone 2: Explore Serverless and API Services

Objective: Build event-driven, serverless architectures using Lambda (AWS), Cloud Functions (GCP), and Azure Functions.

Tools:

AWS Lambda

Google Cloud Functions

Azure Functions

Action Plan:

Build a serverless API for a web or mobile app using AWS API Gateway.

Use Google Cloud Functions to process background tasks.

Integrate serverless functions with Azure AI services for real-time inference.

Example Setup:

Create an API service that triggers AWS Lambda functions to process requests and store results in DynamoDB.

Use Google Cloud Functions to handle file processing triggered by Google Cloud Storage events.

Milestone 3: Enhance CI/CD Pipelines for Multi-Cloud Deployments

Objective: Automate deployments using GitHub Actions to manage multi-cloud deployments and ensure continuous delivery.

Tools:

GitHub Actions

Terraform (for Infrastructure as Code)

Action Plan:

Extend existing CI/CD pipelines to deploy applications across AWS, Azure, and Google Cloud.

Use Terraform scripts to provision infrastructure across clouds.

Set up automated testing and deployments on multi-cloud infrastructure.

Example Setup:

Build a GitHub Actions pipeline that deploys an application to AWS Lambda, Google Cloud Functions, and Azure Functions simultaneously.

Use Terraform to manage infrastructure as code and deploy across different regions and cloud platforms.

Milestone 4: Set Up Monitoring and Logging

Objective: Implement monitoring and logging for performance and error tracking across all cloud services.

Tools:

Datadog (for multi-cloud monitoring)

Sentry (for error logging)

AWS CloudWatch, Google Cloud Monitoring, and Azure Monitor

Action Plan:

Set up Datadog for real-time monitoring across AWS, GCP, and Azure.

Configure Sentry for error tracking in your applications.

Monitor resource usage and performance using native tools like CloudWatch, Azure Monitor, and Google Cloud Monitoring.

Example Setup:

Deploy Datadog agents on all cloud instances (AWS EC2, GCP Compute Engine, Azure VMs) to monitor performance metrics.

Set up Sentry to log and track errors in application code across all environments (Dev, Test, Prod) and cloud providers.

Detailed Year 2 Plan

  1. Scale Applications with Kubernetes and Containers

Objective: Leverage Kubernetes and containers to scale applications across multiple cloud providers.

Steps:

Containerize Applications:

Use Docker to containerize existing applications, ensuring portability across cloud environments.

Deploy Kubernetes Clusters:

Deploy a Kubernetes cluster on AWS using EKS.

Extend Kubernetes clusters to GKE on Google Cloud and AKS on Azure for full multi-cloud orchestration.

Manage and Scale:

Use Kubernetes auto-scaling features to dynamically scale workloads based on demand.

Implement Helm charts to manage Kubernetes applications more easily.

Outcome: Applications are now containerized and running in a Kubernetes environment, making them scalable and cloud-agnostic.

  1. Explore Serverless and API Services

Objective: Build highly efficient, cost-effective, serverless applications and APIs that can automatically scale based on traffic or events.

Steps:

AWS Lambda & API Gateway:

Set up an API using AWS API Gateway to trigger Lambda functions for serverless processing.

Google Cloud Functions:

Use Google Cloud Functions for handling events from Cloud Storage or database changes.

Azure Functions:

Implement Azure Functions for real-time processing in conjunction with Azure AI services (e.g., text or image analysis).

Automate with CI/CD:

Integrate these serverless functions into GitHub Actions to automate deployments when code changes.

Outcome: A fully serverless architecture capable of handling traffic spikes and executing background tasks without provisioning dedicated servers.

  1. Enhance CI/CD Pipelines for Multi-Cloud Deployments

Objective: Automate and streamline the process of deploying to multiple cloud environments using continuous integration and delivery pipelines.

Steps:

GitHub Actions Pipelines:

Extend existing CI/CD pipelines to deploy across multiple clouds (AWS, GCP, and Azure) in a single workflow.

Example GitHub Actions YAML:

name: Multi-Cloud Deployment

on: push: branches: - main

jobs: build: runs-on: ubuntu-latest steps: - name: Checkout Code uses: actions/checkout@v2

  - name: Deploy to AWS Lambda
    run: terraform apply -target=aws_lambda_function.my_function

  - name: Deploy to Google Cloud Functions
    run: gcloud functions deploy my-function

  - name: Deploy to Azure Functions
    run: az functionapp deploy --name myapp

Terraform Multi-Cloud Automation:

Write Terraform scripts to provision infrastructure on AWS, GCP, and Azure.

Use Terraform’s multi-provider capabilities to ensure all infrastructure is created and managed as code.

Outcome: Applications are deployed consistently and automatically across multiple cloud platforms, reducing manual effort and speeding up releases.

  1. Set Up Monitoring and Logging

Objective: Implement comprehensive monitoring and logging solutions to track performance, errors, and resource utilization across all environments and cloud providers.

Steps:

Datadog for Multi-Cloud Monitoring:

Install Datadog agents on all cloud instances (AWS EC2, GCP Compute Engine, Azure VMs) to monitor system performance.

Set up dashboards to monitor CPU usage, memory, and network traffic across all providers.

Sentry for Error Tracking:

Integrate Sentry into your application to log and track errors.

Set up alerts for critical issues that require immediate attention.

Cloud-Native Monitoring:

Use AWS CloudWatch, Google Cloud Monitoring, and Azure Monitor to track cloud-specific performance metrics.

Set up automated alerts for unusual activity (e.g., high CPU usage or unexpected traffic spikes).

Outcome: A fully monitored environment that tracks application performance, detects errors, and alerts on any critical failures across multiple clouds.

Tagging, Naming, and IP Address Plan for Year 2

Tagging Strategy:

  • Provider: AWS, Azure, GCP
  • Type: Kubernetes, Serverless, API, CI/CD, AI
  • Environment: Dev, Test, Prod
  • Project: MultiCloudApp
  • Phase/Year: Year2
  • Owner: YourName

Naming Conventions:

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

Kubernetes Cluster Example:

AWS-K8S-PROD-MultiCloudApp-USWEST1-YEAR2 GCP-K8S-TEST-DataProcessing-USCENTRAL1-YEAR2

Serverless Function Example:

AWS-Lambda-DEV-MyAPI-USWEST1-YEAR2 GCP-CloudFunc-PROD-DataHandler-USEAST1-YEAR2

API Gateway Example:

AWS-API-DEV-MyAppAPI-USWEST2-YEAR2 AZURE-API-PROD-MLInference-WESTEUROPE-YEAR2

IP Address Plan for Year 2:

AWS:

Prod: 10.0.x.x range (e.g., 10.0.1.0/24 for Kubernetes, 10.0.2.0/24 for Lambda).

Dev: 10.1.x.x range (e.g., 10.1.1.0/24 for Kubernetes, 10.1.2.0/24 for S3).

Azure:

Prod: 10.2.x.x range (e.g., 10.2.1.0/24 for Functions, 10.2.2.0/24 for storage).

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

Google Cloud:

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

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

Summary for Year 2:

Scale Applications: Use Kubernetes across AWS, GCP, and Azure to containerize and scale applications.

Serverless and APIs: Build serverless applications and APIs to reduce infrastructure management.

Enhance CI/CD: Automate deployments across multiple clouds using GitHub Actions and Terraform.

Monitor Everything: Implement robust monitoring and error tracking using Datadog, Sentry, and cloud-native tools.

This outline sets the stage for an advanced, multi-cloud environment that scales automatically, is cost-efficient, and is fully monitored for performance and reliability.