# A BiGRU Model Based on the DBO Algorithm for Cloud-Edge Communication Networks
## 4 Simulation Experiment and Result Analysis
### Experimental Results and Analysis  
The amount of data for the experimental results in this section is the difference between the number of validation set entries (3200) and the width of the observation window. In other words, the output data of the RNN, LSTM, and TCN models are 3176 entries, and the output data of the DBO-GRU model is 3188 entries. Since the predicted value curves of each model are relatively close to one another, it would be difficult to clearly show the effect and fitting degree of each model if concentrated in one graph, so it is divided into four graphs. The curve comparison between the real values and the predicted values of the models in the validation set is shown in Figure 6a-d. In Figure 6a, the RNN model shows reasonable alignment with actual values but exhibits larger deviations during peak traffic periods, indicating that it struggles with capturing more complex temporal dependencies in the traffic data. In Figure 5(b), the LSTM model displays greater deviation from the actual data compared to the other models. The error is particularly evident during both peak and off-peak traffic periods, suggesting that the LSTM model is less effective in handling the cumulative traffic load prediction for the \(AT_{UK}\) dataset. In Figure 5(c), the TCN model achieves a closer fit to the actual data than RNN and LSTM, with fewer deviations at peak times. However, it still shows some limitations in accurately capturing sudden changes in traffic flow. In Figure 5(d), the DBO-BiGRU model demonstrates the closest fit to the actual traffic data. Its ability to capture both forward and backward temporal dependencies, combined with the Dung Beetle Optimizer's efficient hyperparameter tuning, allows it to predict traffic trends more accurately than the other models. This superior performance is evident in the minimal deviation between predicted and actual values across all traffic patterns, especially during traffic surges and drops.  
Based on the predicted and real data from all models, the values of the assessment indicators described in Section 4.3 were calculated, as shown in Table 4. It is clear that the LSTM model is far less effective than the other three models. This also confirms the conclusion drawn in the prediction graph. Therefore, only the RNN, TCN, and DBO-BiGRU models are considered during the next comparative data analysis. In terms of the MAE metrics, the values of the metrics for DBO-BiGRU decreased by 62.212% and 25.918% compared to RNN and TCN. This indicates that the overall quality of DBO-BiGRU forecast data is better. In terms of RMSE metrics, the corresponding data of DBO-BiGRU decreased by 59.106% and 25.948% compared with RNN and TCN. This indicates that the overall error level of its prediction data is lower. In terms of MAPE metrics, the values of the metrics of DBO-BiGRU decreased by 67.241% and 21.575% compared to RNN and TCN, respectively, which means that the prediction of DBO-BiGRU is more accurate. In terms of R2 metrics, the corresponding data of DBO-BiGRU increased by 1.035% and 0.18% compared to RNN and TCN, indicating that DBO-BiGRU is a better fit. The DBO-BiGRU model combines the bi-directional information capturing capability of BiGRU and the  
Figure 6: Comparison of curves for the four models.  
global optimisation capability of the DBO algorithm, which makes the model not only able to effectively capture the information in the time-series data but also able to find the best parameter combinations in the global scope. Therefore, the DBO-BiGRU model is more stable and accurate in prediction compared to other models.  
Taking the above into account, it can be concluded in this section that the DBO-BiGRU model designed in this paper outperforms all the compared models in terms of cumulative network traffic data prediction.