An advanced machine learning system that predicts server scaling needs, forecasts future load, detects anomalies, and generates intelligent alerts for cloud infrastructure.
Cloud systems must dynamically scale based on CPU usage, memory consumption, and network traffic. Manual scaling causes resource waste and performance bottlenecks. This project builds an AI-powered auto-scaling decision engine to solve this problem.
- Ensemble Auto-Scaling Prediction (Random Forest + Gradient Boosting + Soft Voting)
- Feature Engineering (CPU/Memory Ratio, Load Index)
- Future Load Forecasting (Linear Regression)
- Anomaly Detection (Isolation Forest)
- Hybrid Smart Scaling Engine
- Intelligent Alert System
- Production-Ready Model Saving (Joblib)
Data Cleaning → Feature Engineering → Train/Test Split → Ensemble Modeling → Forecast Modeling → Anomaly Detection → Real-Time Decision Engine → Model Serialization
Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Joblib, Jupyter Notebook
cloud_auto_scaling/ ├── data/server_metrics.csv ├── notebook/eda.ipynb ├── cloud_auto_scaling_model.pkl ├── load_forecast_model.pkl ├── anomaly_detection_model.pkl └── README.md
Install dependencies: pip install pandas numpy scikit-learn matplotlib seaborn joblib
Run notebook: Kernel → Restart & Run All
cloud_ai_engine(92, 80, 1600)
Output: AUTO SCALE UP 🚀
Designed and implemented an AI-driven cloud auto-scaling platform integrating ensemble learning, anomaly detection, and predictive load forecasting to simulate intelligent infrastructure scaling.
Streamlit Deployment • REST API • Docker • Kubernetes Simulation • AWS Deployment