# Optimizing Weighted Fair Queuing with Deep Reinforcement Learning for Dynamic Bandwidth Allocation
## 3 Research Contributions  
This paper contributes to the formulation of a G/G/1/K parallel queuing system by developing the WFQ continual DRL framework. The proposed approach integrates DRL with continual learning to enhance bandwidth utilization in the WFQ algorithm. The model ensures fairness by dynamically adapting allocations. Continual learning is represented by Elastic Weight Consolidation (EWC), a technique that protects the important parameters of a neural network when the model is trained on new tasks. The system continuously learns and adjusts according to network demands and objectives, thereby maximizing the cumulative reward. In addition, continual learning reduces the retraining overhead, making the model more efficient in dynamic environments compared to traditional DRL-based approaches, as demonstrated in this paper. It uses EWC to mitigate catastrophic forgetting, enabling real-time adaptation. In comparison to existing literature, this work proposes a reinforcement learning method to optimize QoS by adjusting actions based on the queue state, such as packet loss or transmission decisions. While effective, its approach is limited to static traffic environments, and the service rates chosen are such that the system stays stable, does not get congested, and lacks mechanisms for long-term adaption while network conditions grow [20]. Also, the paper [22] proposes using DRL to allocate bandwidth dynamically across WFQ queues, improving performance under varying traffic loads. Still, it does not consider the need for continual adaptation or knowledge retention when system configurations of queue length.  
This paper presents an adaptive algorithm that integrates DRL with EWC to support continual learning across varying network conditions. This approach handles adaptive bandwidth allocation under WFQ. It maintains previously obtained knowledge, providing stability and reducing catastrophic forgetting as the number of queues increases, which leads to faster convergence and makes it more suitable for real-time and complex network scenarios where traffic dynamics evolve over time. The main contribution of the proposed approach can be illustrated as described below:  
1. The proposed model improves performance by integrating the SAC algorithm with EWC, enabling continual learning within the WFQ scheduling framework. Through integration, the system can handle dynamic traffic patterns, such as bursty or periodic flows, without forgetting previously learned scheduling policies, ensuring adaptability to new scenarios without frequent retraining.
2. The approach dynamically learns optimal bandwidth allocation across multi-queue systems in WFQ in real-time, adapting to high traffic and queue length variations.  
EWC enables the agent to retain previously learned policies while learning new ones, eliminating the need to reprocess old data. This helps system stability and scalability in evolving network environments, particularly when facing unpredictable traffic patterns.
4. By integrating SAC and EWC within the WFQ scheduling algorithm, the model achieves efficient resource allocation and robustness to changing network requirements. That helps fair queuing and constant performance across varying traffic scenarios.
5. The new adaptive algorithm ensures high performance while increasing the queues, allowing the system to maximize performance.
6. A real traffic network dataset provides a more realistic and complex testbed, capturing burstiness, and variability across multiple queues.