## 5 Result  
In this section, we first describe the settings of the experimental parameters and the simulation environment. Then, we perform a large number of simulations to evaluate the performance and feasibility of the research. All simulations were performed on servers with Core i7 CPU and 32 GB RAM. In order to evaluate the algorithm performance in a complex and real network topology, we conducted subsequent experimental work based on the optical backbone network topology in a certain place. The topology is shown in Figure 4. In the follow-up experiments, we also generated several networks of different sizes, but with the same structure using a scale-free algorithm randomly, setting the size of the network nodes to \(n\) (\(n\) = 32, 50, 68, 88, 102, 118). In this paper, a dataset provide by China Telecom is used for validation in our experiment. To show the effectiveness of the model, this paper also used the non-deep learning method and other deep learning methods, which have proved to have good performance, as a comparative test. The specific models utilized in this paper include LSTM, GRU, and 3 DCNN, all of which possess the capability of multi-step prediction. Multi-step prediction refers to the process of forecasting multiple future time steps based on the available input data. The ability to perform multi-step prediction ensures that the prediction time can be advanced, thereby guaranteeing that the allocation algorithm completes its execution before the predicted time point arrives. In this paper, we used two evaluation indicators to compare the performance of different service traffic prediction models. They are the R-Square (\(R^{2}\)) and Normalized Mean Squared Error (NMSE), and their definition are as follows in (18) and (19). \[\text{R}^{2}=1-\frac{\sum_{i=1}^{N}(\hat{y}_{i}-y_{i})^{2}}{\sum_{i=1}^{N}( \overline{y}_{i}-y_{i})^{2}} \tag{18}\]  
Figure 4: The real optical backbone network topology in specific place. \[\text{NMSE}=\frac{\sum_{i=1}^{N}{\lvert{{y_{i}}-{y_{i}}}\rvert}^{2}}{\sum_{i=1}^{N }{\lvert{{y_{i}}}\rvert}^{2}} \tag{19}\]  
Table 1 shows the indicators of the prediction results of different models. ARIMA, as a traditional linear model, is limited in capturing the nonlinear features, which leads to a subpar prediction performance, with R\({}^{2}\) being only 0.826654. In contrast, LSTM and its variant GRU, with their ability to capture nonlinear features, have significantly improved the accuracy of predictions, with R\({}^{2}\) being improved by 9% and NMSE being reduced by 48%. The 3 DCNN, by incorporating inter-service correlations, also contributes to improving R\({}^{2}\) to 0.925660 and reducing NMSE to 0.018534. Our proposed model effectively leverages features from both the inter-services correlation and temporal dimensions, leading to the best performance, where R\({}^{2}\) reaches to 0.935641 and NMSE drops to 0.016684. Figure 5 illustrates the predicted traffic volumes for two different services. It is a direct representation of our prediction results, which shows that the predicted results align with the distinct traffic patterns exhibited by each service. This indicates that the proposed model is capable of simultaneously capturing and fitting various traffic patterns associated with different types of services. A schematic diagram of the calendar resource orchestration process is shown in Figure 6, Among it, Figure 5(a) shows the calendar orchestration process without the traffic prediction strategy, and Figure 5(b) shows the process based on the multi-flow prediction strategy. In Stage 1, the services' bandwidth updates based on two strategies: one is (a) without prediction, and the other (b) uses Multi-Flow Based Prediction. In stage 2, scheme (a) will calculate the calendar allocation result of the current moment, and scheme (b) will performs both processes simultaneously, where one performs the calendar calculation process on the current moment, and the other performs the next stage process based on the PFAMD algorithm.