Hybrid DQN–PPO control for joint queue management and bandwidth allocation under bursty network traffic
TL;DR
A hybrid reinforcement learning (RL) framework that jointly controls queue management and bandwidth allocation in bursty multi-service networks and demonstrates the effectiveness of coordinated learning-based control for stable and QoS-aware operation in bursty networked systems.
Abstract
Communication networks in today’s world must support heterogeneous services with strict delay and loss constraints under highly bursty traffic conditions. Traditional queue management and scheduling mechanisms are often unable to maintain stable performance when traffic exhibits strong short-term correlations and heavy-tailed arrivals. This paper proposes a hybrid reinforcement learning (RL) framework that jointly controls queue management and bandwidth allocation in bursty multi-service networks. A Deep Q-Network (DQN) dynamically regulates queue thresholds to suppress congestion and stabilize latency, while a Proximal Policy Optimization (PPO) agent selects from a small set of interpretable orthogonal bandwidth allocation templates to adapt resource sharing. Traffic arrivals are modeled using generalized exponential (GE) burst processes with heterogeneous per-class deadlines, enabling realistic evaluation of delay, jitter, and application-visible packet loss. Extensive simulation results demonstrate that the proposed approach consistently reduces delay, tail latency, jitter, and packet loss compared with widely used heuristic and RL scheduling schemes, while maintaining high utilization. The results highlight the effectiveness of coordinated learning-based control for stable and QoS-aware operation in bursty networked systems.