Adaptive Bandwidth Management for Traffic Surge and Device Hunger Scenarios in LAN Environments

Abstract

Local Area Networks (LANs) are essential to modern organizational infrastructures, enabling the operation of a wide array of devices and services. With the increasing number of connected devices, particularly during peak hours or sudden demand spikes, networks often face "bandwidth hunger," where devices struggle to secure adequate resources for optimal performance. Traditional static bandwidth allocation methods fall short in addressing these dynamic challenges, often resulting in packet loss, high latency, and service degradation. To overcome these limitations, this paper proposes an adaptive bandwidth management framework that anticipates traffic surges and dynamically reallocates resources through congestion control, Quality of Service (QoS) mechanisms, and reinforcement learning (RL)-based scheduling. Simulation outcomes demonstrate that the proposed system significantly enhances bandwidth utilization, minimizes device starvation, and sustains service quality even during extreme traffic loads. The paper also explores the potential of integrating the framework with Software-Defined Networking (SDN) controllers to further improve network responsiveness and scalability.