Hybrid Deep Reinforcement Learning with Kookaburra Optimization for QOS-Aware Bandwidth Allocation in GMPLS Optical Networks

Objectives: To address dynamic bandwidth allocation with strict Quality of Service (QoS) requirements in Generalized Multi-Protocol Label Switching (GMPLS) optical networks under strain from internet services, real-time multimedia, and cloud infrastructure. Method: A Hybrid Deep Reinforcement Learning (Hyb-DRL) framework combined with the Kookaburra Optimization Algorithm (KkOA) for adaptive weight adjustment is proposed. Dynamically generated input data, including user request rates, queue lengths, server availability, and link stability metrics, were used to simulate real-world traffic. The Hyb-DRL agent learned optimal routing and bandwidth provisioning policies while KkOA optimized model weights for faster convergence and stability. Findings: The simulation results show that the suggested Hyb-DRL-KkOA algorithm performs better than the conventional bandwidth allocation algorithms. In contrast to conventional algorithms, it reduces the blocking probability, makespan, cost, and energy utilization while improving the throughput; therefore, providing enhanced quality of service (QoS). The proposed framework achieves a lower blocking probability by 78%, makespan by 64%, energy consumption by 51%, and operational cost by 47%. In addition to this, it provides better throughput performance by 69% than other conventional techniques. Moreover, it provided a delay of 0.0189 s, minimal energy consumption of 33 mJ, and maximal throughput of 950 Mbps. Novelty: A combination of reinforcement learning and meta-heuristic optimization leads to adaptive decision-making regarding routing and bandwidth allocation in the face of different traffic demands. The performance gain in terms of QoS is due to optimal utilization of network resources with low blocking probability, energy and operational cost. It provides a scalable and adaptive solution for high-speed, reliable data transmission in modern communication networks.