sdlc-green-software

name	sdlc-green-software
description	Green software engineering: sustainability, carbon-aware computing, energy-efficient architecture, eco-friendly development.
version	6.3.0
author	Dinoudon
license	MIT
platforms	["linux","macos","windows"]
metadata	{"hermes":{"tags":["green-software","sustainability","carbon-aware","energy-efficient","eco-friendly","climate"]}}

---

name: sdlc-green-software description: "Green software engineering: sustainability, carbon-aware computing, energy-efficient architecture, eco-friendly development." version: 6.3.0 author: Dinoudon license: MIT platforms: [linux, macos, windows] metadata: hermes: tags: [green-software, sustainability, carbon-aware, energy-efficient, eco-friendly, climate]

Green Software Skill

Build sustainable, energy-efficient software systems. Covers carbon-aware computing, energy-efficient a

When to Use This Skill

• Reducing software carbon footprint
• Implementing carbon-aware systems
• Optimizing energy efficiency
• Measuring environmental impact
• Sustainable development practices
• Green cloud computing
• Carbon-aware scheduling
• Eco-friendly architecture

Overview

Green software engineering applies sustainability principles to software design, development, and operation. It aims to minimize the environmental impact of software systems while maintaining functionality and performance.

Carbon footprint:
  - CO2 equivalent emissions
  - Direct (energy consumption)
  - Indirect (cloud, infrastructure)
  - Embodied (hardware manufacturing)

Carbon-aware:
  - Adjust behavior based on carbon intensity
  - Use clean energy when available
  - Shift workloads to greener times
  - Location-aware computing

Energy efficiency:
  - Minimize energy consumption
  - Optimize resource usage

Step 1: Measuring Carbon Footprint

Scope 1: Direct emissions
  - On-site energy consumption
  - Company vehicles
  - Refrigerants
  
Scope 2: Indirect emissions (electricity)
  - Purchased electricity
  - Data center power
  - Cloud computing
  
Scope 3: Other indirect emissions
  - Business travel
  - Employee commuting
  - Supply chain
  - End-of-life disposal

Step 2: Carbon-Aware Computing

Definition:
  - CO2 emitted per kWh of electricity
  - Varies by location and time
  - Lower with renewable energy
  - Measured in gCO2/kWh

Factors:
  - Energy source (coal, gas, nuclear, solar, wind)
  - Time of day (peak vs off-peak)
  - Season (heating, cooling demand)
  - Grid mix (regional differences)

Data sources:
  Watttime:
    - Real-time carbon intensity

Step 3: Energy-Efficient Architecture

Lazy loading:
  - Load resources only when needed
  - Reduce initial load
  - Save bandwidth and energy
  
  Example:
    // JavaScript
    const image = new Image();
    image.loading = 'lazy';
    image.src = 'large-image.jpg';

Caching:
  - Cache frequently accessed data
  - Reduce computation
  - Save network requests

Step 4: Green Cloud Computing

Right-sizing:
  - Match resources to workload
  - Avoid over-provisioning
  - Use appropriate instance types
  
  Tools:
    - AWS Compute Optimizer
    - Azure Advisor
    - Google Recommender
  
  Example:
    # AWS right-sizing
    aws compute-optimizer get-ec2-instance-recommendations \
        --instance-arns arn:aws:ec2:us-east-1:123456789:instance/i-1234567890abcdef0

Step 5: Sustainable Development Practices

Code efficiency:
  - Write efficient algorithms
  - Optimize database queries
  - Reduce unnecessary computation
  - Profile and optimize hot paths
  
  Example:
    # Bad: Multiple database queries
    def get_user_orders_slow(user_id):
        user = db.query(f"SELECT * FROM users WHERE id = {user_id}")
        orders = db.query(f"SELECT * FROM orders WHERE user_id = {user_id}")
        return {'user': user, 'orders': orders}
    
    # Good: Single query with join
    def get_user_orders_fast(user_id):

Step 6: Green Data Management

Data minimization:
  - Collect only necessary data
  - Delete old data
  - Compress stored data
  - Use appropriate data types
  
  Example:
    # Bad: Store everything
    user_data = {
        'name': 'John',
        'email': '[e-mail]',
        'address': '123 Main St',
        'phone': '555-1234',
        'ssn': '123-45-6789',
        'preferences': {...},

Step 7: Green CI/CD

Pipeline optimization:
  - Cache dependencies
  - Parallelize stages
  - Skip unnecessary steps
  - Use efficient tools
  
  Example (GitHub Actions):
    name: CI
    
    on: [push, pull_request]
    
    jobs:
      build:
        runs-on: ubuntu-latest
        steps:

Step 8: Green Monitoring

Efficient logging:
  - Log only necessary data
  - Use structured logging
  - Set appropriate log levels
  - Rotate and archive logs
  
  Example:
    # Structured logging
    import structlog
    
    logger = structlog.get_logger()
    
    logger.info("user_login",
        user_id=123,
        ip="192.168.1.1",

Step 9: Green Software Patterns

Carbon-aware pattern:
  - Monitor carbon intensity
  - Adjust workload timing
  - Use clean energy
  - Shift to greener regions
  
  Implementation:
    def carbon_aware_execute(task):
        carbon_intensity = get_carbon_intensity()
        
        if carbon_intensity > THRESHOLD:
            # Defer to cleaner time
            schedule_for_later(task)
        else:
            # Execute now

Step 10: Green Software Tools

Green Metrics Tool:
  - Measure energy consumption
  - Track carbon emissions
  - Compare implementations
  - Open source
  
  Usage:
    gmt run my-application
    gmt report my-application

Scaphandre:
  - Power consumption monitoring
  - Process-level tracking
  - RAPL integration
  - Prometheus exporter

Related Skills

• sdlc-architecture-design: Sustainable architecture patterns
• sdlc-deployment: Green deployment strategies
• sdlc-observability: Efficient monitoring
• sdlc-platform-engineering: Green platform practices