Adaptation Status

Completed

1. Imports Updated

• All imports use relative imports (from .module)
• No from app.ml.* or from app.core.* imports found
• Package structure matches AI Routing Lab

2. Latency/Jitter Prediction Models Created

• LatencyPredictor - Random Forest model for latency prediction
• JitterPredictor - Random Forest model for jitter prediction
• RoutePredictionEnsemble - Combines latency/jitter for route selection
• Target: >92% accuracy (R² score)

3. Laboratory Experiment Framework

• LaboratoryExperiment class for experiment management
• Integration with Model Registry
• Data preprocessing and feature extraction
• Example experiments created

4. Examples Updated

• example_experiment.py - Updated to use LatencyPredictor
• latency_jitter_experiment.py - Complete workflow example

Pending Adaptation

1. Legacy Models

The following models from CloudBridge AI Service are copied but may need adaptation:

• models/prediction/load_ensemble.py - Currently for load prediction
• models/prediction/lstm_forecast.py - Could be adapted for latency time series
• models/prediction/arima_model.py - Could be adapted for latency forecasting
• models/prediction/prophet_model.py - Could be adapted for latency forecasting

2. Routing Models

• models/routing/route_ensemble.py - May need updates for latency/jitter-based selection
• models/routing/random_forest_route.py - May need adaptation
• models/routing/neural_network_route.py - May need adaptation
• models/routing/multi_armed_bandit.py - May need adaptation
• models/routing/q_learning_route.py - May need adaptation

3. Integration with quic-test

• Data collectors need to map quic-test metrics to model features
• Validation framework needs quic-test integration
• Real-time prediction API needs to be created

Next Steps

1. Test new models with real quic-test data
2. Adapt LSTM/ARIMA/Prophet for latency time series prediction
3. Update route ensemble to use latency/jitter predictions
4. Create validation framework with quic-test integration
5. Build prediction API for CloudBridge Relay integration

Current Status

• Models Created: 3 (LatencyPredictor, JitterPredictor, RoutePredictionEnsemble)
• Experiments Ready: 2 (example_experiment.py, latency_jitter_experiment.py)
• Integration: Pending quic-test data collection
• Target Accuracy: >92% (to be validated with real data)