# A BiGRU Model Based on the DBO Algorithm for Cloud-Edge Communication Networks
## 2 Related Work  
There are already a large number of case studies on edge computing in smart cities or IoT systems. [PERSON] et al. [22] presented the current state of research on IoT applications using edge AI, AI-based resource allocation in IoT edge systems, and quality of service and energy efficiency in smart IoT edge systems. [PERSON] et al. [23] designed an efficient edge-computing management mechanism tailored for IoT applications in smart cities. This mechanism includes a lightweight database that enables edge-computing servers in smart cities to store and manage edge-service information effectively. [PERSON] et al. [24] designed brainyEdge, an AI-enabled framework for edge devices able to jointly satisfy the Quality of Experience (QoE) criteria of IoT applications. In recent years, more and more scholars have begun to focus their attention on the temporal traffic prediction in cloud-edge communication networks. [PERSON] et al. [25] presented the spatial-temporal analysis of the real-world cellular network traffic dataset and proposed a graph-attentive network based on time-series similarity to predict spatio-temporal traffic in cellular networks. [PERSON] et al. [26] proposed a framework for predicting heterogeneous multivariate network traffic with Quality of Service (QoS)-awareness, enabling the dynamic and optimal utilization of network resources. In order to explore the hidden spatial and temporal dependencies in network traffic, [PERSON] et al. [27] proposed an attention-based graph convolutional network model, which combined graph attention networks with graph convolutional networks. [PERSON] et al. [28] proposed a network traffic prediction method based on temporal representation and spatial convolution. The method used graph convolutional networks to explore the spatial topological features of network nodes and used a Long Short-Term Memory (LSTM) model to characterize the temporal features of the network. [PERSON] et al. [29] introduced an innovative deep-learning architecture called the Dynamic Traffic Correlation-based Spatio-Temporal Graph Convolutional Network (DTC-STGCN). This advanced framework was designed to accurately predict traffic state by effectively capturing and analysing the evolving spatio-temporal relationships within traffic data. The DTC-STGCN generated a dynamic adjacency matrix by analysing various traffic characteristics, enabling it to capture and represent the evolving spatio-temporal correlations in the traffic network.  
Figure 2: Cloud–Edge communication network.  
[PERSON] et al. [30] proposed a new spatial-temporal prediction method, namely Multi-Scale Spatial-Temporal Aware Transformer (MSSTAT) for traffic prediction. The MSSTAT synchronized the connection between time steps and scale information by a type of parallel cross multi-head attention, which gave each time step several times the perceived field while also being able to weaken the impact brought by anomaly points. [PERSON] et al. [31] proposed a novel deep-learning framework called the spatial-temporal multi-feature fusion network. This framework was used to design a multi-scale attention module with temporal convolution to capture temporal dependencies from different scales. [PERSON] et al. [32] proposed a global-local spatial-temporal transformer network that can fully excavate the diverse spatial-temporal characteristics of cellular traffic for accurate cellular traffic prediction. However, the models used in current research tend to take too long to train, because they are too complex and require personnel to spend a significant amount of time intervening in the training of the model.