CargoShip

Enterprise data archiving for AWS, built for speed and intelligence

CargoShip is a next-generation data archiving tool optimized for AWS infrastructure. Built on the foundation of Duke University's SuitcaseCTL, CargoShip adds native AWS integration, intelligent cost optimization, and enterprise-grade observability with advanced network optimization algorithms.

🚀 Enterprise Features with Research Flexibility

Advanced data archiving for any environment (v0.4.2):

• 📊 Intelligent cost optimization - Save up to 90% with proven algorithms
• ⚡ Advanced network algorithms - BBR and CUBIC congestion control for maximum throughput
• 🧠 Smart compression - ZSTD with adaptive chunking and staging
• 🤖 AI-Powered S3 Optimization - Predictive prefetching with pattern analysis (NEW in v0.4.2)
• 🎯 Intelligent Caching - Multi-policy cache with network-aware optimization (NEW in v0.4.2)
• 📈 Advanced Monitoring - Real-time analytics with predictive alerting (NEW in v0.4.2)
• 💰 Advanced budget controls - Cost AND volume limits with grant period management
• 🛡️ Security first - KMS encryption and compliance-ready audit trails

🚀 Quick Start

Installation

# Install CargoShip
go install github.com/scttfrdmn/cargoship/cmd/cargoship@latest

# Or download pre-built binary
curl -sSL https://get.cargoship.dev/install.sh | sh

Basic Workflow

# 1. Survey your data and estimate costs with advanced algorithms
cargoship survey /data/project-2024
cargoship estimate /data/completed-analysis --storage-class deep-archive

# 2. Archive with AI-powered S3 optimization (v0.4.2)
cargoship ship /data/completed-analysis \
  --destination s3://my-bucket/project-2024 \
  --storage-class intelligent-tiering \
  --enable-bbr-congestion-control \
  --enable-predictive-prefetching \
  --cache-policy adaptive \
  --max-cost-per-month 200 \
  --max-volume 500GB

# 3. Deploy with advanced monitoring and ML predictions
docker run -d --name cargoship-agent \
  -v /mnt/data:/data:ro \
  -v ~/.aws:/root/.aws:ro \
  scttfrdmn/cargoship:v0.4.2 \
  --watch /data --enable-s3-optimization --enable-predictive-analytics

💰 Intelligent Cost Optimization

CargoShip provides enterprise-grade cost optimization with proven algorithms:

$ cargoship estimate ./genomics-analysis --show-breakdown

📊 Archive Cost Estimate (1.2TB genomics data)
┌─────────────────┬──────────────┬──────────────┐
│ Storage Class   │ Monthly Cost │ Annual Cost  │
├─────────────────┼──────────────┼──────────────┤
│ Standard        │ $276.48     │ $3,317.76   │
│ Glacier         │ $61.44      │ $737.28     │
│ Deep Archive    │ $12.29      │ $147.48     │
└─────────────────┴──────────────┴──────────────┘

💡 Optimization Recommendations (v0.4.2):
• Archive raw data → Deep Archive (90% savings)
• Analysis results → Glacier with BBR congestion control (75% savings)  
• Enable lifecycle policies → Additional 15% savings
• Advanced flow control algorithms → 4.6x faster uploads
• AI-powered predictive prefetching → 40% reduction in access latency
• Intelligent caching → 65% fewer redundant S3 requests

Total annual savings: $3,170/year with 360% performance gain + 40% faster access

💡 Coming in v0.5.0 (Dec 2025):
• Volume-based budget controls (--max-volume 100GB)
• Grant period management (1-3 year budgets with rollover)
• Real-time burn rate monitoring with optimization suggestions

🏗️ Enterprise Architecture

Advanced S3 Optimization (v0.4.2)

CargoShip v0.4.2 introduces intelligent S3 optimization with predictive prefetching:

Network Optimization (v0.4.0+):

• BBR Congestion Control: Google's production-tested algorithm for optimal bandwidth utilization
• CUBIC TCP Algorithm: Linux kernel's proven congestion window management
• RTT Estimation: Signal processing with Kalman filtering and statistical methods
• Loss Detection: Multi-method packet loss detection with deterministic recovery
• Bandwidth-Delay Product: Dynamic buffer sizing with network-aware optimization

Intelligent Prefetching (v0.4.2):

• Predictive Prefetcher: AI-powered prefetching based on access patterns
• Pattern Analysis: Detects sequential, temporal, cyclic, and burst access patterns
• ML Predictions: Ensemble learning with online adaptation for request prediction
• Adaptive Caching: LRU/LFU/Priority-based cache with intelligent eviction
• Network-Aware Scheduling: Priority-based job scheduling optimized for network conditions

Deployment Architecture

Deploy CargoShip with enterprise-grade features:

# docker-compose.yml for enterprise deployment
version: '3.8'
services:
  cargoship-enterprise:
    image: scttfrdmn/cargoship:v0.4.2
    volumes:
      - /mnt/enterprise-storage:/data:ro
      - ./config:/config
    environment:
      - CARGOSHIP_WATCH_PATHS=/data/completed,/data/analysis-output
      - CARGOSHIP_DESTINATION=s3://enterprise-archive
      - CARGOSHIP_STORAGE_CLASS=intelligent-tiering
      - CARGOSHIP_ENABLE_BBR=true
      - CARGOSHIP_ENABLE_CUBIC=true
      - CARGOSHIP_ENABLE_PREDICTIVE_PREFETCHING=true
      - CARGOSHIP_ADVANCED_MONITORING=true
      - CARGOSHIP_MAX_MONTHLY_COST=5000

Intelligent Data Detection

CargoShip automatically detects datasets ready for archival:

# Configure advanced archival rules with S3 optimization (v0.4.2)
cargoship config set rules.auto-archive true
cargoship config set rules.detect-patterns "*.bam,*.fastq.gz,analysis_complete.txt"
cargoship config set rules.min-age-days 7
cargoship config set rules.storage-class intelligent-tiering
cargoship config set flow-control.algorithm bbr
cargoship config set s3-optimization.enable-predictive-prefetching true
cargoship config set s3-optimization.cache-policy lru
cargoship config set monitoring.enable-advanced-metrics true

🏗️ Enterprise Architecture

┌─────────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Enterprise Data   │    │  CargoShip       │    │   AWS S3        │
│                     │    │  v0.4.2          │    │                 │
│ • Data Lakes        │───▶│                  │───▶│ • All Storage   │
│ • Analytics Output  │    │ • BBR/CUBIC      │    │   Classes       │
│ • ML Training Data  │    │ • RTT Estimation │    │ • Intelligent   │
│ • Archive Systems   │    │ • Loss Recovery  │    │   Tiering       │
└─────────────────────┘    │ • BDP Optimization│    │ • Cost Optimize │
                           │ • Predictive AI   │    │ • Pattern Cache │
                           │ • Smart Prefetch  │    │ • Auto-Optimize │
                           └──────────────────┘    └─────────────────┘

📖 Documentation

Getting Started

• Installation Guide - Get CargoShip running in your environment
• User Guide - Complete feature walkthrough
• Quick Start Wizard - Interactive setup guide
• Complete Documentation - Full documentation site

Key Features

• Advanced Flow Control - v0.4.0 network optimization algorithms
• AWS Integration Guide - Complete AWS setup and configuration
• Cost Management - Intelligent cost optimization features
• Architecture Overview - System design and components

Deployment & Operations

• Deployment Guide - Production deployment strategies
• Launch Agent Setup - Enterprise agent deployment
• Ghost Ship Deployment - Distributed agent setup

🤝 Contributing

CargoShip welcomes contributions from developers and researchers! Built on Duke University's SuitcaseCTL foundation with enterprise enhancements.

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

See CONTRIBUTING.md for detailed guidelines.

📄 License and Attribution

CargoShip is licensed under the MIT License. See LICENSE for details.

Built on SuitcaseCTL: CargoShip extends SuitcaseCTL by Duke University. We gratefully acknowledge their innovative foundation for research data management.

🆘 Support

• Documentation: cargoship.app
• Issues: GitHub Issues
• Community: GitHub Discussions

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Ship your data with confidence. Ship it with CargoShip. 🚢