AI-powered dynamic queue optimization in bursty multi traffic environment

Abstract

Modern broadband access networks increasingly carry heterogeneous, delay-sensitive traffic generated by cloud-assisted applications, real-time communication services, and short-form multimedia exchanges. These workloads have bursty ON/OFF packet dynamics, creating dense arrival clusters that conventional GPON/XGS-PON upstream schedulers struggle to handle. This work proposes an AI-driven Dynamic Bandwidth Allocation (AI-DBA) framework based on a Deep Q-Network (DQN) that observes global queue states for video, voice, and data traffic and performs adaptive admission/prioritization/scheduling decisions. The DQN agent learns an adaptive scheduling policy through experience replay and reward-driven interactions to anticipate burst formations, drain high-pressure queues, and smooth resource allocation. Simulation results show that DQN-DBA maintains near-zero buffer queues and eliminates observed packet loss across all traffic classes, and stablize the delay and jitter profiles, whereas TCP and QCT-ARED experience long queue buildup, increased loss rates, and high delay/jitter variability under the same bursty traffic conditions. Across both experiments, the proposed approach consistently outperforms all baselines. Against the Non-AI scheme, the AI-based model reduces packet loss by 72.84% and average waiting time (latency) by 41.73%. In the second experiment, across video, voice, and data traffic, DQN-DBA achieves a 100% reduction in observed packet loss compared to both TCP and QCT-ARED, reduces steady-state delay by approximately 74.7% relative to TCP and 66.3% relative to QCT-ARED, and improves throughput by about 558–634% over TCP and 343–384% over QCT-ARED.The results confirm that AI-driven upstream scheduling provides a robust, scalable, and highly adaptive solution for managing bursty multi-traffic loads in next-generation PON architectures.