Linux Swap Optimizer - Training Data

This directory contains training data and configuration templates for the Linux Swap Optimizer system. These files provide pre-configured settings and reference data for different system types and use cases.

Directory Contents

Configuration Templates

• ai-workload-config.json - Configuration for AI/ML development workstations
• low-end-system-config.json - Configuration for low-end systems with limited resources
• gaming-config.json - Configuration for gaming systems with low latency focus
• server-config.json - Configuration for production servers with stability focus

Reference Data

• performance-baselines.json - Performance targets and optimization results for different system types
• process-priority-mappings.json - Default process categorization and priority settings
• kernel-parameter-presets.json - Pre-configured kernel parameter sets for different scenarios

Usage

Applying Configuration Templates

1. Copy the appropriate configuration template to your system:

cp tranning/ai-workload-config.json config.json

2. Modify the configuration as needed for your specific requirements

3. Restart the swap optimizer service:

sudo systemctl restart swap-optimizer

Customizing Process Priority Mappings

Edit the process-priority-mappings.json file to add custom process patterns:

{
  "custom_mappings": {
    "mappings": [
      {
        "name": "my-application",
        "patterns": ["myapp", "my-process"],
        "tier": "ai",
        "nice_value": -10
      }
    ]
  }
}

Selecting Kernel Parameter Presets

Choose from the available presets in kernel-parameter-presets.json:

• conservative - Minimize swap usage, prioritize RAM
• balanced - Balance between RAM and swap usage
• aggressive - Maximize swap usage for better RAM utilization
• performance - Minimize latency, prioritize responsiveness
• server - Prioritize stability and throughput

System Types

AI Workstation

• Hardware: 32GB RAM, 16 CPU cores, 16GB swap
• Optimization: Aggressive AI process prioritization
• Expected Results: 60% CPU reduction, 40% memory efficiency

Low-End System

• Hardware: 4GB RAM, 2 CPU cores, 8GB swap
• Optimization: Conservative memory management
• Expected Results: 30% CPU reduction, 25% memory efficiency

Gaming System

• Hardware: 16GB RAM, 8 CPU cores, 16GB swap
• Optimization: Low latency and responsiveness
• Expected Results: 45% CPU reduction, 35% memory efficiency

Server System

• Hardware: 64GB RAM, 32 CPU cores, 32GB swap
• Optimization: Stability and throughput
• Expected Results: 35% CPU reduction, 30% memory efficiency

Performance Monitoring

Use the performance baselines to compare your system's performance against expected results:

# Check current system performance
python3 swap_optimizer.py --status

# Compare with baseline
python3 swap_optimizer.py --benchmark

Customization Guidelines

Memory Management

• Systems with sufficient RAM: Use conservative preset
• Systems with limited RAM: Use aggressive preset
• Balanced approach: Use balanced preset

Process Prioritization

• AI/ML workloads: Prioritize AI tier processes
• Development work: Prioritize IDE tier processes
• Gaming: Prioritize interactive tier processes
• Server: Prioritize normal tier processes

Kernel Parameters

• Always test kernel parameter changes in a safe environment
• Monitor system performance after applying changes
• Revert to default settings if issues occur

Training and Learning

Understanding Priority Tiers

1. AI Tier (-10 nice): Highest priority for AI/ML processes
2. IDE Tier (-5 nice): High priority for development tools
3. Interactive Tier (0 nice): Normal priority for user applications
4. Normal Tier (10 nice): Lower priority for system services
5. Background Tier (19 nice): Lowest priority for maintenance tasks

Kernel Parameter Tuning

• Start with conservative settings
• Gradually adjust based on system performance
• Monitor swap usage and memory pressure
• Adjust swappiness based on available RAM

Process Pattern Matching

• Use regex patterns for flexible process matching
• Test patterns before deployment
• Consider process name variations
• Account for different Linux distributions

Troubleshooting

Configuration Issues

• Verify JSON syntax is correct
• Check file permissions
• Ensure configuration file path is correct
• Review system logs for errors

Performance Issues

• Compare against performance baselines
• Check system resource usage
• Verify process priority assignments
• Review kernel parameter settings

Compatibility Issues

• Ensure Linux kernel version compatibility
• Check systemd service status
• Verify Python version requirements
• Review system dependencies

Contributing

When adding new configurations or data:

1. Follow the existing JSON structure
2. Include descriptive comments
3. Test configurations before submission
4. Document expected performance results
5. Update this README with new information

Support

For additional support:

• Check the main project README
• Review the documentation in the wiki
• Open an issue on GitHub
• Consult the performance reports

Version Information

• Training Data Version: 1.0.0
• Compatible with: Linux Swap Optimizer v2.0.0
• Last Updated: 2026-07-04