Enterprise-grade load balancer observability platform with ML-powered retry prediction, real-time analytics, SQL Server integration, and Power BI dashboards for high-scale traffic management.
This repository contains two complementary projects that together provide a comprehensive solution for load balancer monitoring, analytics, and intelligent optimization:
Real-time monitoring and analytics platform for load balancer infrastructure with:
- Live telemetry processing and visualization
- Comprehensive KPI computation and anomaly detection
- SQL Server data warehousing with enterprise-grade performance
- Power BI dashboard integration for executive and operational views
- Automated alerting and notification systems
Machine learning solution for predicting client retry behavior with:
- Predictive analytics for client retry patterns
- Production-ready API for real-time predictions
- Business impact analysis and ROI quantification
- Integration patterns for existing load balancer infrastructure
- Comprehensive model documentation and validation
- Python 3.8+
- SQL Server or SQL Server Express
- ODBC Driver 17 for SQL Server
# Clone the repository
git clone https://github.com/FCHEHIDI/Load-Balancer-Analytics-at-Hyperscale.git
cd Load-Balancer-Analytics-at-Hyperscale
# Setup observability dashboard
cd load-balancer-observability-dashboard
pip install -r requirements.txt
# Configure database credentials
cp .env.template .env
# Edit .env with your SQL Server credentials
# Run the pipeline
python src/observability_orchestrator.py# Run integration tests
python test_integration.py- Real-time Monitoring: Live telemetry processing from load balancers
- Predictive Analytics: ML-powered retry behavior prediction
- Enterprise Integration: SQL Server data warehousing
- Executive Dashboards: Power BI integration for stakeholder views
- Scalable Architecture: Designed for high-volume production environments
- Secure Configuration: Environment-based credential management
- Copy
.env.templateto.envin the observability dashboard directory - Configure your SQL Server credentials:
DB_SERVER=YOUR_SERVER_NAME DB_DATABASE=TrafficInsights DB_AUTH_TYPE=Windows Authentication DB_USERNAME=YOUR_DOMAIN\YOUR_USERNAME
Security Note: The .env file contains sensitive credentials and is excluded from version control.
- Complete Project Overview - Detailed architecture and integration guide
- Observability Dashboard Guide - Setup and usage instructions
- Retry Prediction Guide - ML model documentation
- Database Setup Guide - Database configuration instructions
- Power BI Dashboard (PDF) - Dashboard preview (no Power BI required)
The system provides end-to-end telemetry processing from data ingestion through analytics to actionable insights via dashboards and APIs.
Run the comprehensive test suite:
# Integration tests
python test_integration.py
# Individual component tests
cd load-balancer-observability-dashboard
python src/data_generation.py
python src/dashboard_engine.py- Real-time infrastructure monitoring
- Incident response and troubleshooting
- Performance optimization insights
- Executive dashboards and reporting
- Capacity planning and forecasting
- Business impact analysis
- API integration for retry prediction
- Circuit breaker optimization
- Intelligent traffic routing
This project is designed for enterprise load balancer environments. For contributions or questions:
- Author: Fares Chehidi
- Email: fareschehidi28@gmail.com
- LinkedIn: Connect with me
This project is licensed under the MIT License - see the LICENSE files in each project directory for details.
- Load Balancer Observability Dashboard - Real-time monitoring platform
- Load Balancer Retry Prediction - ML prediction system
Ready to transform your load balancer monitoring? Start with the Quick Start guide above!