Capacity Planning Toolkit

Capacity Planning Toolkit

A Python toolkit for infrastructure load forecasting, resource utilization tracking, scaling decisions, and cost modeling. All implementations use Python standard library only — no numpy, pandas, or scipy required.

Features

• Load Forecasting — Linear regression, seasonal decomposition, double exponential smoothing, all implemented from scratch
• Resource Utilization Tracking — Fleet-wide CPU/memory/disk monitoring with anomaly detection via z-score analysis
• Scaling Advisor — Headroom-based scaling recommendations with tier-specific policies and cost-aware decisions
• Cost Modeling — Unit economics computation, scaling cost projections, and optimization opportunity identification
• Auto-Method Selection — Forecaster automatically picks the best algorithm based on data characteristics

File Structure

capacity-planning-toolkit/
├── README.md                             # This file
├── LICENSE                               # MIT License
├── src/
│   ├── __init__.py                       # Package initialization
│   ├── forecaster.py                     # Time-series forecasting (regression, seasonal, exponential smoothing)
│   ├── utilization.py                    # Resource utilization tracking and fleet analysis
│   ├── scaling_advisor.py                # Scaling recommendation engine
│   └── cost_model.py                     # Infrastructure cost modeling and optimization
├── tests/
│   └── test_forecaster.py               # Unit tests for forecasting engine
├── configs/
│   └── thresholds.yaml                  # Configurable thresholds and policy parameters
├── examples/
│   └── sample_utilization.csv           # Realistic utilization data (64 rows, multiple resources)
└── guides/
    └── capacity_review_guide.md         # How to conduct effective capacity reviews

Requirements

• Python 3.10+ (uses modern type hints, union syntax)
• Standard library only — zero external dependencies

Quick Start

1. Forecast Future Load

from src.forecaster import Forecaster, TimeSeriesPoint
from datetime import datetime, timedelta

# Load your historical utilization data
data = [
    TimeSeriesPoint(timestamp=datetime(2024, 1, 1) + timedelta(days=i), value=cpu_pct)
    for i, cpu_pct in enumerate(your_daily_cpu_data)
]

forecaster = Forecaster()
result = forecaster.auto_forecast(data, horizon_days=90)

print(f"Method selected: {result.method}")
print(f"Model fit (R²): {result.r_squared:.3f}")
for pt in result.forecast_points[:7]:  # First week
    print(f"  {pt.timestamp.date()}: {pt.value:.1f}%")

*... continues with setup instructions, usage examples, and more.*