# Optimizing Weighted Fair Queuing with Deep Reinforcement Learning for Dynamic Bandwidth Allocation
## 5 Results and Analysis
### Training Real Traffic
Despite the dynamic challenges, the error range suggests reliable convergence and robust performance. Figure 14: The Mean Square Error of twelve queues. Figure 15: The Mean Absolute Error of twelve queues. Referring to Figure 19, the MAE curve for the second real traffic dataset initially increases sharply, exhibiting poor early prediction accuracy. As training progresses, the error decreases and stabilizes within a narrow range of 0.5 to 0.6. This steady fluctuation suggests the model has adapted better to the traffic dynamics. The bounded MAE range reflects consistent and robust performance under real-world dynamics. Figure 16: The Mean Square Error (MSE) of twelve queues for first real traffic. Figure 17: The Mean Absolute Error (MAE) for twelve queues for the first real traffic. Figure 18: The Mean Square Error (MSE) of twelve queues for second real traffic. The processing with statistical analysis addresses bursty real traffic for all categories, after which the dataset is equal to 1,393,777 [33]. Regarding the Square Coefficient of Variance \(CV^{2}\) in the real traffic data, computed in equation [33]:  
\[CV^{2}=\frac{\sigma}{m} \tag{18}\]  
where \(\sigma\) represent standard deviation, and \(m\) represents the mean. As illustrated in Figure 20, a higher \(CV^{2}\) indicated that the real traffic data arrival patterns were more unpredictable and random. High variance reflects the bursty and unparable nature of real traffic conditions. To compute correlation, the copy of interarrival time will be shifted to one position, and the correlation coefficient for real traffic will be computed in the equation of correlation coefficient [34]. The results shown in Figure 21 revealed that the real traffic data appear to have a more dynamic pattern in its correlation. The decline of correlation curves indicates more influence. The statistics clearly showed patterns of bursty traffic. This paper presented knowledge that improved this algorithm by combining the continual learning technique with the DRL algorithm for the WFQ system. The development of the WFQ continual-DRL algorithm was achieved by contributing a continual learning method represented by the EWC technique, which helps to overcome catastrophic forgetting by remembering old tasks, selectively slowing down learning on the parameters important for those tasks, and improving the performance over time, which is combined with advanced deep reinforcement in the form of the SAC algorithm. In addition to developing this algorithm, the evaluation of the algorithm was expanded by increasing the number of queues to twelve. This expansion was considered to assess the scalability and ro  
Figure 19: The Mean Absolute Error (MAE) of twelve queues for second real traffic. Figure 20: Square Coefficient of Variance (CV\({}^{2}\)) in the real traffic data. bustness of the model in terms of managing higher traffic loads and more complex network conditions. The aim was to maximize the cumulative reward, and this approach achieved an improvement of approximately 76% in performance compared to the baseline reward curve in an experiment involving eight queues for WFQ-DRL. This finding demonstrates the effectiveness of our approach in regard to maintaining stable learning and adapting to new tasks while retaining previously learned knowledge. For the expansion in a second experiment involving twelve queues, the average reward curve shows an enhancement in performance by increasing reward with another fluctuation, indicating that this new approach is more effective even with an expanded environment, is more robust, and is suitable for the nature of the queueing system, as it carries out dynamic bandwidth allocation control among WFQ parallel queues. The model shows higher reward than compared to simulation of twelve queues. This suggests that the model adapts well in real world traffic, and the result highlights the model's effectiveness under realistic traffic environments.