## 2 Related Works
Currently, most optical network resource scheduling algorithms focus on improving resource utilization based on the rules, with a limited utilization of knowledge information within the network, such as historical traffic data and historical decision data. This leads to a lack of learning tailored to the deployment environment in network decision-making. As a result, some studies are dedicated to predicting key information affecting the scheduling of optical network resources to enhance resource allocation. This paper aims to utilize traffic prediction technology to anticipate the future operational state of FlexE clients, enabling the proactive reservation of slot resources. However, network traffic exhibits highly nonlinear and bursty characteristics, posing significant challenges for accurate traffic prediction. Additionally, the rapid fluctuations in traffic, in contrast to long-term time steps, present substantial challenges for traffic prediction, particularly in the context of FlexE service calendar switching that necessitates recalculations. Traffic prediction methods can be categorized into two types: classical models and deep learning methods. Classical models exhibit good interpretability and perform well in linear prediction. Auto Regressive Integrated Moving Average (ARIMA) combines autoregressive, differencing, and moving average components to capture the linear dependencies and trends in the data [20]. However, ARIMA struggles to provide accurate predictions for the nonlinear part of the traffic data in the present network. With the increase in the data and computability, deep learning methods have been widely applied in network traffic prediction. Recurrent Neural Networks (RNN) and their variants are a class of deep models widely applied in time series forecasting due to their ability to retain information from previous sequences. [PERSON] et al. applied RNN to network traffic prediction [21]. [PERSON] et al. utilized LSTM for traffic prediction using the real-world datasets [22]. To capture longer-term dependencies, [PERSON] et al. introduced Long Short-Term Memory (LSTM) by modifying memory cells to preserve long-term information [23]. [PERSON] et al. demonstrated that LSTM outperforms the ARIMA model by approximately 30% in accurately predicting the link throughput [24]. [PERSON] et al. proposed LSTM-based Network Traffic Prediction (LNTP), a hybrid optimized model for end-to-end network traffic prediction [25]. The aforementioned deep learning methods primarily focus on utilizing the temporal features of individual flows. [PERSON] et al. utilized Graph Convolutional Networks (GCN) to improve the prediction accuracy [26, 27]. [PERSON] et al. combined MGCN (Multi-Graph Convolutional Network) with LSTM for wireless traffic prediction [28]. [PERSON] et al. utilized densely connected Convolutional Neural Networks (CNN) to capture the spatial dependencies of cell traffic [29]. [PERSON] et al. experimentally verified that CNN is suitable for extracting inter-node correlations, while RNN is effective in capturing temporal features [30]. These methods achieved a 70% to 80% accuracy in various forecasting tasks, outperforming CNN and 3 DCNN. [PERSON] et al. proposed LA-ResNet, which combines residual networks with RNN and incorporates attention mechanisms to assist in traffic prediction [31]. [PERSON] et al. [32] employed Convolutional Long Short-Term Memory (ConvLSTM) to integrate convolutional layers into the LSTM cells, enabling the combination of spatial-temporal features. However, these works mainly focus on utilizing spatial or topological information to assist in wireless traffic prediction without considering the correlation between services, which could further improve the accuracy of traffic prediction. Several studies have explored the application of Generalized Nets (GNs) in network traffic prediction, demonstrating its effectiveness in capturing the dynamics of network traffic [33]. [PERSON] et al.