🧠 Restima: Predictive Resource Estimation Engine

Restima is a hybrid resource estimation system designed for modern infrastructure and AI workloads. It combines rule-based logic with machine learning to predict CPU, memory, and I/O requirements based on runtime metrics. Built with modularity, observability, and MLOps in mind, Restima adapts to evolving workloads and improves over time.

Restima follows a Model-as-a-Service architecture:

┌────────────────────┐
│ monitor_service.py │ ← Collects live metrics
└────────┬────────────┘
         ↓
┌────────────────────┐
│ trainer.py         │ ← Feature engineering + model training
└────────┬────────────┘
         ↓
┌────────────────────┐
│ registry.py        │ ← Stores model metadata
└────────┬────────────┘
         ↓
┌────────────────────┐
│ estimator.py       │ ← Loads model + predicts resources
└────────┬────────────┘
         ↓
┌────────────────────┐
│ cli.py             │ ← User interface for training, inference, evaluation
└────────────────────┘

1. Collect runtime metrics using monitor_service.py or FlowAudit
2. Engineer features: lag, rolling averages, time-based signals
3. Train a model using trainer.py and serialize it
4. Load the model in estimator.py for live predictions
5. Evaluate and monitor model performance and drift
6. Retrain periodically to adapt to new workloads

Restima expects structured runtime metrics in JSONL format:

{
  "timestamp": "2025-09-06T13:00:01Z",
  "avg_cpu_percent": 65.2,
  "peak_memory_mb": 1450,
  "total_io_mb": 320,
  "call_depth": 12,
  "branching_factor": 3.1,
  "recursion_detected": true,
  "avg_latency_sec": 0.24
}

Restima produces a prediction block:

{
  "ram_mb": 1536.2,
  "cpu_cores": 3.7,
  "io_mb": 320.0,
  "confidence": "High"
}

restima monitor --duration 60
restima train --data data/training_data.jsonl
restima estimate --metrics data/live_metrics.jsonl --output prediction.json
restima evaluate --model models/predictor_model.pkl --data data/eval_data.jsonl
restima drift --baseline data/baseline.jsonl --live data/live_metrics.jsonl

git clone https://github.com/your-org/restima.git
cd restima
pip install -e .

Ensure the following directories exist:

• models/ → stores trained models
• data/ → stores training and live metric files

• ✅ Improved Accuracy: Learns from historical patterns and adapts to new data
• ✅ Adaptability: Retrains on fresh metrics to handle evolving workloads
• ✅ Cost Efficiency: Reduces over-provisioning and optimizes cloud spend
• ✅ Observability: Logs predictions and tracks model performance
• ✅ MLOps Foundation: Ready for integration with MLflow, Airflow, or CI/CD
• ✅ Lightweight Deployment: Fast inference with minimal overhead

Restima transforms runtime metrics into actionable infrastructure intelligence. It empowers teams to:

• Predict resource needs before deployment
• Optimize provisioning based on real usage
• Detect architectural inefficiencies
• Automate scaling decisions with confidence

MIT License

Restima is now a living system — continuously learning, adapting, and optimizing. Let me know if you’d like to generate a sample training dataset, simulate a full CI/CD run, or scaffold a dashboard integration next.