In the context of advancing 6G technologies, the demands on optical communication networks have become more intricate and diverse [1]. The emergence of increasingly complex application scenarios in vertical industries, coupled with the rise of high-value dedicated line services, has led to a surge in network carrying demands [2]. This encompasses requirements for small bandwidth, high isolation, low-latency determinism, and a strong emphasis on high security and reliability. To meet the burgeoning need for fine-granularity resource allocation beyond conventional coarse-grained provisioning, a pivotal shift in the network architecture and protocols is imperative. This is particularly crucial for scenarios requiring dynamic and flexible resource management, such as ultra-reliable low-latency communications (URLLC) and the massive machine-type communications (mMTC) envisioned in the 5G paradigm.

In response to this exigency, the introduction of FlexE (Flexible Ethernet) technology emerges as a transformative solution [3]. FlexE is a pivotal advancement in optical networking that enables the subdivision of Ethernet connections into smaller, flexible sub-connections, each with an independently configurable bandwidth and characteristics [4]. This granular resource allocation empowers network operators to efficiently utilize the physical network infrastructure, thereby accommodating the diverse traffic demands expected in the 6G landscape [5].

FlexE operates at the physical layer of the network stack, enabling the dynamic partitioning of Ethernet links into sub-links [6,7]. These sub-links, referred to as “FlexE groups”, are configurable with respect to their bandwidth allocation, providing a level of flexibility unprecedented in traditional Ethernet architectures [8]. The key innovation lies in the ability to allocate and manage bandwidth in increments as small as 10 Mbps, allowing for fine-grained control over network resources.

Moreover, FlexE introduces the concept of “slots”, allowing for the precise allocation of bandwidth resources within a FlexE group. Each slot corresponds to a discrete unit of time during which a specific amount of bandwidth is available for the data transmission. This temporal granularity enhances the adaptability of the network to varying traffic patterns, ensuring the efficient utilization of resources in scenarios characterized by dynamic and bursty traffic [9].

Upon the arrival of a service within a network slicing, ensuring Quality of Service (QoS) for distinct services mandates the proper allocation of route paths contingent upon the extant network status, coupled with the provision of corresponding time slot resources along each link [10]. The transition from a granularity of 5 Gbps to 10 Mbps engenders the introduction of a pioneering fine-granularity unit (FGU) sublayer within the FlexE frame structure. This introduction, in tandem with the further segmentation and reutilization of FGUs for 5 Gbps time slots, precipitates an exponential augmentation in the number of time slots managed per link. The reduction in granularity accentuates the salience of the non-deterministic delay induced by time slot conflicts, rendering it non-negligible. Additionally, the dynamic fluctuation in service bandwidth presents a formidable challenge for expeditious and dependable time slot reallocation. The surpassing of the prescribed service bandwidth thresholds may necessitate not only the assignment of slot resources, but could also exert an influence on routing determinations, potentially instigating momentary service interruptions and fluctuations. In recent years, traffic prediction technology has garnered widespread application in improving network decision-making along with the variation in the dynamic traffic trends. Empowered via the a priori knowledge afforded by traffic prediction, networks are aptly poised to proactively extend resource computation and bandwidth reservation for the network service. Ensuring QoS for latency-sensitive services is contingent upon the imperative design of a time slot scheduling apparatus tailored for 10 Mbps fine-granularity slices, alongside a time slot reallocation mechanism underpinned by traffic prediction.

FlexE is an interface technology for the bearer network to realize service isolation and network slicing. Since ITU-T standard organizations have accelerated the FlexE standard process [11], it has developed rapidly in recent years. Eira et al. [4] provided a solution for decoupling the interface rates between routers and transport devices from the actual data flows and evaluated the trade-off between a transport box’s complexity and its ability to utilize light paths effectively, offering insights into the impact of FlexE use cases on the router port efficiency, transport box provisioning, and DWDM layer capacity in DCI contexts. D. Koulougli et al. [12] explored optimized routing in complex multi-layer, multi-domain IP-optical networks using a hierarchical PCE and FlexE technology. It formulates optimization problems that consider QoS, privacy, and FlexE constraints and introduces novel algorithms for efficient routing and client assignment. P. Zhu et al. [13] addressed the security concerns in next-gen RAN transport, focusing on eavesdropping attacks at the physical layer. A cross-layer approach using FlexE and WDM is proposed for enhanced security. Various attack levels are considered, and a trade-off between resource efficiency and security is explored. The numerical results demonstrate the effectiveness of the defense strategies. D. Koulougli et al. [14] addressed routing optimization in multi-layer multi-domain networks, emphasizing inter-layer and inter-domain coordination. It introduces a hierarchical path computation engine (PCE) that leverages FlexE technology to enhance the network performance by linking the IP and optical domains, presenting an efficient algorithm for routing and FlexE assignment and achieving a substantial performance improvement of an 87% optimal throughput. And, in [12], the authors investigate the FlexE Traffic Restoration (FTR) problem that aims to maintain high network utilization via the fast recovery of FlexE clients with the minimum cost using the spare capacity in the already deployed PHYs. H. Liang et al. [15] addressed the integration of flexible Ethernet (FlexE) and elastic optical networks (EONs) in FlexE-over-EONs scenarios, specifically focusing on the FlexE-aware architecture. It introduces mixed integer linear programming (MILP) and integer linear programming (ILP) models for single-hop and multi-hop scenarios, respectively, and presents highly time-efficient approximation algorithms that provide solutions closely approaching the optimal ones in large-scale planning. Based on [12,14], D. Koulougli et al. [16] investigated the FlexE Traffic Restoration (FTR) problem that aims to maintain high network utilization via the fast recovery of FlexE clients with the minimum cost using the spare capacity in the already deployed PHYs. And, based on [13], P. Zhu et al. [17] introduced a cross-layer security design for FlexE over WDM networks, specifically addressing eavesdropping threats at the physical layer. The approach combines universal Hashing-based FlexE data block permutation with parallel fiber transmission to enhance security. The study evaluates different attack levels, balancing resource efficiency and security, and demonstrates the effectiveness of the proposed cross-layer defense strategies. M. Wu et al. [18] investigated cross-layer restoration (CLR) in FlexE-over-EONs, specifically addressing temporary outages in FlexE switches. Extensive simulations confirm the effectiveness of these CLR strategies, highlighting the potential of FlexE and elastic optical networks for efficient restoration in scenarios involving FlexE switch outages. Recently, Gu, R., et al. [19] established a model of routing embedded timeslot scheduling for the routing of fine-granularity slices and timeslot scheduling problems in SPN-based FlexE interfaces, for which a deterministic timeslot allocation mechanism supporting end-to-end low-latency transmission is proposed.

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. Hallas et al. applied RNN to network traffic prediction [21]. Trinh et al. utilized LSTM for traffic prediction using the real-world datasets [22]. To capture longer-term dependencies, Hochreiter et al. introduced Long Short-Term Memory (LSTM) by modifying memory cells to preserve long-term information [23]. Lazaris et al. demonstrated that LSTM outperforms the ARIMA model by approximately 30% in accurately predicting the link throughput [24]. Zhang 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. Vinchoff et al. utilized Graph Convolutional Networks (GCN) to improve the prediction accuracy [26,27]. Lin et al. combined MGCN (Multi-Graph Convolutional Network) with LSTM for wireless traffic prediction [28]. Zhang et al. utilized densely connected Convolutional Neural Networks (CNN) to capture the spatial dependencies of cell traffic [29]. Huang 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 3DCNN. Li et al. proposed LA-ResNet, which combines residual networks with RNN and incorporates attention mechanisms to assist in traffic prediction [31]. Cui 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]. Smith et al. [34] proposed a GN-based network traffic prediction model, leveraging GNs to describe network elements and their interactions. The model demonstrated high accuracy and robustness in predicting network traffic fluctuations. Li and Wang [35] introduced a GN-based approach for network traffic prediction, combining time series analysis and machine learning techniques. They employed GNs to describe the dynamic changes in network traffic and applied time series analysis to the GN node and connection attributes. The integration of GNs and machine learning algorithms improved the accuracy and stability of traffic prediction. In addition, Chen et al. [36] proposed a fusion model that combines GNs with deep learning for network traffic prediction. GNs were utilized to describe network elements, and deep learning algorithms were applied to the GN node and connection attributes. The fusion model exhibited a superior performance in capturing the complex patterns and features of network traffic. In summary, GNs have shown promising applications in network traffic prediction. By describing and simulating network elements and their relationships, GNs effectively capture the dynamic changes and fluctuations in network traffic. Furthermore, the integration of GNs with other techniques, such as time series analysis and deep learning, enhances the accuracy and reliability of traffic prediction. Future research can further explore the applications of GNs in network traffic prediction and develop more efficient and robust prediction models.