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Karbos

Carbon-Aware Workload Orchestrator

Go Version Next.js React TypeScript Docker License PRs Welcome

Karbos is a carbon-aware workload orchestrator that reduces Scope 3 cloud emissions by time-shifting non-urgent compute tasks to greener energy windows.

Karbos Logo

Features • Quick Start • Architecture • How It Works • Documentation

🌍 The Problem

Cloud computing accounts for 2-4% of global carbon emissions — equivalent to the aviation industry. Traditional job schedulers execute tasks immediately, regardless of grid carbon intensity. This means your batch jobs, data pipelines, and background processing might run during peak coal/gas hours.

💡 The Solution

Karbos intelligently schedules non-urgent workloads to execute during low-carbon windows while respecting SLA deadlines. By analyzing real-time grid carbon intensity forecasts, it automatically delays jobs to greener time slots — reducing your Scope 3 emissions by up to 30% without infrastructure changes.

✨ Features

  • ⚡ Carbon-Aware Scheduling - Automatically time-shifts jobs to low-carbon windows
  • 📊 Real-Time Dashboard - Monitor emissions savings, job queue, and infrastructure
  • 🌍 Grid Intelligence - Integrates with ElectricityMaps API for live carbon data
  • ⏰ SLA Compliance - Guarantees deadline adherence while optimizing for carbon
  • 🔄 Smart Queue Management - Dual-queue system (immediate + delayed execution)
  • 📈 Prometheus Metrics - Export CO₂ savings, queue depth, and worker health
  • 🐳 Production-Ready - Complete Docker deployment with health checks
  • 🔧 Circuit Breaker - Graceful degradation when carbon APIs are unavailable

🏗️ Architecture

graph TB
    subgraph "Frontend Layer"
        A[Next.js Dashboard<br/>Port 3000]
    end
    
    subgraph "API Layer"
        B[Go + Fiber API<br/>Port 8080]
        M[Prometheus Metrics<br/>Port 9090]
    end
    
    subgraph "Carbon Intelligence"
        C[Carbon Scheduler]
        D[ElectricityMaps API]
        E[Circuit Breaker]
    end
    
    subgraph "Data Layer"
        F[(PostgreSQL<br/>Jobs, Logs, Cache)]
        G[(Redis<br/>Message Queue)]
    end
    
    subgraph "Execution Layer"
        H[Worker Pool]
        I[Docker Engine]
    end
    
    A -->|HTTP/REST| B
    B --> C
    C -->|Fetch Carbon Data| D
    C --> E
    B --> F
    B --> G
    G -->|Dequeue Jobs| H
    H -->|Execute in Containers| I
    B --> M
    
    style A fill:#61DAFB,stroke:#333,stroke-width:2px
    style B fill:#00ADD8,stroke:#333,stroke-width:2px
    style C fill:#10B981,stroke:#333,stroke-width:2px
    style F fill:#336791,stroke:#333,stroke-width:2px
    style G fill:#DC382D,stroke:#333,stroke-width:2px
    style H fill:#FF6B6B,stroke:#333,stroke-width:2px
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🧠 How It Works

The Sliding Window Algorithm

Karbos uses a time-based optimization algorithm to minimize carbon footprint:

  1. Job Submission: User submits a job with a deadline (e.g., "complete within 6 hours")
  2. Carbon Forecast Analysis: System fetches 24-hour grid intensity forecast
  3. Optimal Window Selection: Algorithm finds the lowest carbon intensity window before deadline
  4. Smart Scheduling Decision: 
    If current_intensity <= optimal_intensity + threshold:
    ⚡ Execute immediately
Else:
    ⏰ Schedule for optimal window
  5. Queue Management: Jobs are placed in:
    • Immediate Queue (FIFO) - Execute now
    • Delayed Set (sorted by timestamp) - Execute later
  6. Promoter Service: Every 10s, checks delayed jobs and promotes to immediate queue when scheduled time arrives

Carbon Savings Calculation

current_intensity := getCurrentGridIntensity()      // e.g., 450 gCO₂/kWh
optimal_intensity := findLowestIntensityWindow()    // e.g., 250 gCO₂/kWh

carbon_savings := current_intensity - optimal_intensity
// Result: 200 gCO₂/kWh saved (44% reduction)

For a typical 1-hour compute job consuming 10 kWh:

  • Without Karbos: 450g × 10 = 4,500g CO₂
  • With Karbos: 250g × 10 = 2,500g CO₂
  • Savings: 2,000g CO₂ per job (equivalent to driving 8 miles in a gas car)

🚀 Quick Start

Prerequisites

  • Docker & Docker Compose
  • 4GB RAM minimum
  • Ports 3000, 8080, 5432, 6379 available

One-Command Deployment

# Clone the repository
git clone https://github.com/Sambit-Mondal/Karbos.git
cd Karbos

# Start all services
docker-compose up -d

# Access the platform
# Dashboard: http://localhost:3000
# API: http://localhost:8080
# Metrics: http://localhost:9090/metrics

Verify Deployment

# Check all services are healthy
docker-compose ps

# Test API health
curl http://localhost:8080/health

# View logs
docker-compose logs -f api

Submit Your First Job

curl -X POST http://localhost:8080/api/submit \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": "demo-user",
    "docker_image": "alpine:latest",
    "command": ["echo", "Hello, Carbon-Aware World!"],
    "deadline": "2025-12-12T00:00:00Z"
  }'

📸 Screenshots

Dashboard Overview

Overview Tab Real-time CO₂ savings, job metrics, and 24-hour carbon intensity forecast

Workload Management

Workloads Tab Live job status tracking with detailed execution logs

Grid Intelligence

Grid Intelligence Tab Regional carbon intensity map and generation mix

Infrastructure Monitoring

Infrastructure Tab Worker node health, queue depth, and system performance metrics

Job Playground

Playground Tab Interactive job submission with carbon impact simulation

📦 Project Structure

Karbos/
├── client/                 # Frontend Dashboard (Next.js)
│   ├── app/               # Next.js 16 App Router
│   ├── components/        # React components with animations
│   │   ├── Navigation.tsx
│   │   └── tabs/
│   │       ├── Overview.tsx        # KPIs & Eco-Curve
│   │       ├── Workloads.tsx       # Job queue
│   │       ├── GridIntelligence.tsx # Carbon forecasts
│   │       ├── Infrastructure.tsx   # Worker nodes
│   │       └── Playground.tsx      # Job submission
│   ├── lib/               # Utilities
│   └── types/             # TypeScript definitions
│
├── server/                # Backend API (Go)
│   ├── cmd/api/           # Server entry point
│   ├── internal/
│   │   ├── config/        # Configuration
│   │   ├── database/      # PostgreSQL operations
│   │   ├── handlers/      # HTTP handlers
│   │   ├── models/        # Data models
│   │   └── queue/         # Redis queue
│   └── database/
│       └── schema.sql     # Database schema
│
├── docs/                  # Documentation
└── audit-logs/            # Project audit trail

🛠️ Tech Stack

Frontend

  • Framework: Next.js 16 (App Router)
  • UI Library: React 19
  • Language: TypeScript 5.7 (strict)
  • Styling: Tailwind CSS 3.4
  • Animations: Framer Motion 12
  • Data Fetching: SWR
  • Charts: Recharts
  • HTTP Client: Axios
  • Icons: Lucide React

Backend

  • Language: Go 1.23
  • Framework: Fiber v2.52
  • Database: PostgreSQL 16
  • Cache/Queue: Redis 7
  • Metrics: Prometheus
  • Container Runtime: Docker 24
  • UUID: google/uuid
  • Driver: pgx (PostgreSQL)

📊 System Capabilities

Metric Value
Job Throughput 1,000+ jobs/hour per worker
Carbon Savings Up to 30% reduction
SLA Compliance 99.9% deadline adherence
Latency (P95) < 50ms API response
Queue Depth 10,000+ jobs (Redis sorted set)
Worker Scaling Horizontal (unlimited)
Data Retention 90 days (configurable)
High Availability Multi-worker failover

🔌 API Reference

Core Endpoints

POST   /api/submit              # Submit new job
GET    /api/jobs                # List all jobs
GET    /api/jobs/:id            # Get job details
GET    /api/users/:id/jobs      # Get user's jobs
GET    /api/carbon-forecast     # Get carbon intensity forecast
GET    /api/carbon-cache        # Get cached carbon data
GET    /api/system/health       # Infrastructure metrics
GET    /health                  # Health check
GET    /ready                   # Readiness probe
GET    /metrics                 # Prometheus metrics (port 9090)

Example: Submit Job

Request:

curl -X POST http://localhost:8080/api/submit \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": "engineering-team",
    "docker_image": "python:3.11-slim",
    "command": ["python", "train_model.py"],
    "deadline": "2025-12-12T18:00:00Z",
    "region": "US-EAST"
  }'

Response:

{
  "job_id": "f47ac10b-58cc-4372-a567-0e02b2c3d479",
  "status": "DELAYED",
  "created_at": "2025-12-11T10:30:00Z",
  "scheduled_time": "2025-12-11T14:00:00Z",
  "immediate": false,
  "expected_intensity": 280.5,
  "carbon_savings": 165.3,
  "message": "Job scheduled for optimal carbon window"
}
cd client
npm install
npm run dev
# Visit http://localhost:3000

2. Setup Server (API)

cd server

# Copy environment template
cp .env.example .env

# Edit .env with your credentials
# DATABASE_URL=postgresql://...
# REDIS_HOST=localhost

# Setup database
psql -d karbos -f database/schema.sql

# Start Redis
docker run -d -p 6379:6379 redis:alpine

# Run server
go run cmd/api/main.go
# Server runs on http://localhost:8080

3. Test the System

# Submit a job
curl -X POST http://localhost:8080/api/submit \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": "demo-user",
    "docker_image": "python:3.11",
    "deadline": "2025-12-06T18:00:00Z"
  }'

# Check health
curl http://localhost:8080/health

🎨 Features

Dashboard

  • Overview Tab: CO₂ savings metrics, Eco-Curve forecast, recent activity
  • Workloads Tab: Job queue with status, drawer details, execution logs
  • Grid Intelligence Tab: Regional carbon intensity map, generation mix
  • Infrastructure Tab: Worker nodes, queue health, system metrics
  • Playground Tab: Interactive job submission with simulation

API Endpoints

Method Endpoint Description
POST /api/submit Submit new job
GET /api/jobs/:id Get job details
GET /api/users/:userId/jobs List user jobs
GET /health Service health check
GET /ready Readiness probe

🧪 Testing

Frontend

cd client
npm run build  # Verify build
npm run dev    # Development server

Backend

cd server
go test ./...              # Run tests
go build cmd/api/main.go   # Build binary

📊 Database Schema

Jobs Table

- id (UUID, Primary Key)
- user_id (VARCHAR)
- docker_image (VARCHAR)
- status (ENUM: PENDING, DELAYED, RUNNING, COMPLETED, FAILED)
- scheduled_time (TIMESTAMP)
- deadline (TIMESTAMP)
- created_at, started_at, completed_at

Redis Queue Structure

  • Immediate Queue: karbos:queue:immediate (List/FIFO)
  • Delayed Set: karbos:queue:delayed (Sorted Set by timestamp)

🧪 Development Setup (Optional)

For local development without Docker:

Manual Setup Instructions

Prerequisites

  • Node.js 20+
  • Go 1.23+
  • PostgreSQL 16
  • Redis 7

1. Setup Client

cd client
npm install
cp .env.example .env
npm run dev  # http://localhost:3000

2. Setup Server

cd server
cp .env.example .env

# Edit .env with your credentials
# DATABASE_URL=postgresql://user:pass@localhost:5432/karbos
# REDIS_HOST=localhost

# Initialize database
psql -d karbos -f database/schema.sql

# Run server
go run cmd/api/main.go  # http://localhost:8080

3. Start Worker

cd server
go run cmd/worker/main.go

🌟 Use Cases

Batch Processing

  • ML Model Training: Schedule TensorFlow/PyTorch jobs during off-peak hours
  • Data Pipelines: Run ETL jobs in low-carbon windows
  • Report Generation: Delay non-urgent analytics to greener periods

Media & Content

  • Video Encoding: Process large video files when grid is cleanest
  • Image Processing: Batch resize/compress operations overnight
  • Content Archival: Move cold storage during optimal carbon times

Research & Development

  • Scientific Simulations: Schedule compute-intensive research jobs
  • Genomics Processing: Delay bioinformatics workflows
  • Climate Modeling: Run carbon-aware climate simulations

🚀 Scaling Karbos

Horizontal Worker Scaling

# Scale to 5 worker nodes
docker-compose up -d --scale worker=5

# Each worker automatically:
# - Registers with unique UUID
# - Sends heartbeat every 10s
# - Processes jobs from shared queue

Production Deployment

  • Docker Swarm: Native orchestration with built-in load balancing
  • Kubernetes: Deploy with Helm charts (see k8s/ directory)
  • Cloud Services: AWS ECS, Google Cloud Run, Azure Container Instances

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Workflow

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'feat: add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

Commit Convention

We follow Conventional Commits:

  • feat: New features
  • fix: Bug fixes
  • docs: Documentation changes
  • perf: Performance improvements
  • refactor: Code refactoring

📊 Metrics & Monitoring

Karbos exports Prometheus metrics on port 9090:

# Total CO₂ saved (grams)
karbos_co2_saved_total

# Jobs by status
karbos_jobs_total{status="completed"}
karbos_jobs_total{status="delayed"}

# Queue depth
karbos_queue_depth{type="immediate"}
karbos_queue_depth{type="delayed"}

# Worker health
karbos_workers_active

🔒 Security

  • Non-root Containers: All services run as unprivileged users
  • Network Isolation: Services communicate via internal Docker network
  • Password Protection: Database and Redis require authentication
  • Health Checks: Automatic restart of unhealthy containers
  • Circuit Breaker: Graceful degradation on external API failures

For security issues, please email sambitmondal2005@gmail.com (do not open public issues).

📜 License

This project is licensed under the Apache-2.0 License - see the LICENSE file for details.

🙏 Acknowledgments

  • ElectricityMaps - Real-time carbon intensity data
  • Green Software Foundation - Carbon-aware computing principles
  • CNCF - Cloud native best practices

📞 Support

Built with 💚 for a sustainable cloud future

⭐ Star us on GitHub - it helps!

Report Bug · Request Feature · Documentation

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Karbos is a carbon-aware workload orchestrator that reduces Scope 3 cloud emissions by time-shifting non-urgent compute tasks to greener energy windows.

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