Internet Routing Optimization
Machine learning approach to optimize internet routing protocols, reducing latency and improving network efficiency through intelligent path selection.
Project Demo
Key Impact
🎯 Reduced average latency by 18% in simulated networks
Overview
This project addresses one of the most critical challenges in global internet infrastructure: optimizing routing algorithms to reduce latency and improve network efficiency. Inspired by experiences teaching in Tanzania where internet access limitations became apparent, the project develops a machine learning approach to enhance Border Gateway Protocol (BGP) routing decisions using gradient boosting algorithms.
The Challenge
Traditional internet routing relies on static algorithms that don't adapt to dynamic network conditions. In rural and developing areas, limited infrastructure creates bottlenecks that traditional routing protocols can't efficiently handle. The challenge was to develop a system that could predict network traffic patterns and optimize routing decisions in real-time, similar to how smart grids work for electricity distribution.
The Solution
Developed an Internet demand forecasting algorithm using XGBoost gradient boosting that acts as an additional attribute in BGP routing decisions. The system analyzes global bandwidth usage data from the International Telecommunications Union, Kaggle, and Ericsson to predict network traffic patterns. This enables dynamic traffic shaping and proactive routing optimization, similar to smart grid approaches used in electricity distribution.
Results
Achieved 18% reduction in average latency in simulated network environments
XGBoost model outperformed Meta's Prophet by 5% in accuracy for time series prediction
Successfully demonstrated dynamic traffic shaping capabilities
Validated approach through comparison with electricity demand forecasting models
Established proof of concept for telecom industry implementation
Technical Implementation
Architecture
Built on gradient boosting framework using XGBoost for demand forecasting, integrated with BGP routing protocol analysis. System processes real-time network data and provides routing recommendations based on predicted traffic patterns.
Algorithms
XGBoost gradient boosting for demand forecasting, BGP protocol analysis for routing decisions, dynamic traffic shaping algorithms, and comparative analysis with Prophet time series forecasting.
Data Processing
Integrated data from International Telecommunications Union, Kaggle internet usage datasets, and Ericsson mobility reports. Processed global bandwidth usage patterns and network topology information for predictive modeling.
Deployment
Proof of concept implementation with simulation testing. Designed for integration with major telecom providers like Verizon Innovation Lab, T-Mobile Innovation Center, and Ericsson.
Key Learnings
Gradient boosting algorithms can significantly outperform traditional time series models for network traffic prediction
Smart grid approaches from electricity distribution can be effectively applied to internet infrastructure
The telecom industry has high barriers to entry but significant opportunities for optimization
Data collection and industry understanding are critical challenges in network optimization projects
Rural and developing areas benefit most from intelligent routing optimization due to infrastructure limitations