AI for Connectivity Hackathon II: Building Resilient Networks 

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ByteBand

AreeshaRaza

Safia Faiz.

Ensuring optimal network performance and efficient bandwidth allocation in modern digital infrastructures is crucial, especially in educational institutions where multiple nodes compete for resources. This project introduces a Smart Network Resource Allocation System that leverages Software-Defined Networking (SDN), machine learning, and time-series forecasting to distribute bandwidth intelligently among different schools.

The system gathers real-time bandwidth usage data, detects anomalies using the Interquartile Range (IQR) method, and predicts future demand through the Prophet time-series forecasting model. The allocated bandwidth is optimized based on these predictions, ensuring fair and efficient distribution among schools. The SDN-based load balancer also computes energy-efficient network routes to reduce congestion and enhance data flow efficiency.

The solution provides a Streamlit-based interactive dashboard, allowing users to visualize bandwidth trends, monitor real-time allocations, and review detected anomalies. This approach ensures proactive network management, preventing bottlenecks and improving overall network performance.

A Smart Network Resource Allocation System using Prophet's time-series forecasting for efficient bandwidth distribution, anomaly detection, and energy-aware SDN load balancing. Features a Streamlit dashboard for real-time monitoring and insights.

"ByteBand team on AI for Connectivity Hackathon II: Building Resilient Networks Hackathon"

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