发表机构
School of Computing and Artificial Intelligence, Southwest Jiaotong University; SWJTU-Leeds Joint School, Southwest Jiaotong University; State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（西南交通大学计算与人工智能学院; 西南交通大学-利兹学院; 北京邮电大学网络与交换技术国家重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对边缘计算中流量预测的非平稳长程建模难题，提出时空图Transformer框架，经真实蜂窝网络数据集验证，其性能优于GCN-RNN等基线模型，可支撑主动资源管理。
AI 中文摘要
准确的交通预测对边缘计算中的主动资源管理至关重要，在边缘计算中，服务需求会随空间和时间动态变化。在实际蜂窝边缘系统中，流量在相邻服务区域间呈现强空间相关性，且受用户移动性和应用行为驱动，存在长程时间依赖关系。现有循环预测方法可捕捉短期动态，但在非平稳条件下往往难以对长时程流量演化建模。为应对这一挑战，我们提出一种时空图Transformer框架，用于联合建模边缘计算中交通预测的空间交互与时间依赖关系。该框架采用图神经网络捕捉服务区域间的空间相关性，并利用基于Transformer的自注意力机制从历史流量观测中学习长程时间模式。通过将空间表示学习与时间推理解耦，所提方法为大规模时空流量建模提供了有效机制。在真实蜂窝网络数据集上开展的大量实验表明，所提图Transformer在多个预测时程上始终优于基于循环图的基线模型，包括GCN-RNN、GCN-LSTM和GCN-GRU模型。与被动管理策略相比，生成的预测结果能实现更有效的主动资源配置，降低过载风险。这些结果凸显了图增强注意力机制在构建智能自适应边缘计算系统方面的潜力。
英文摘要
Accurate traffic forecasting is essential for proactive resource management in edge computing, where service demand evolves dynamically across both space and time. In practical cellular edge systems, traffic exhibits strong spatial correlations among neighboring service regions and long-range temporal dependencies driven by user mobility and application behavior. Existing recurrent forecasting approaches can capture short-term dynamics but often struggle to model long-horizon traffic evolution under non-stationary conditions. To address this challenge, we propose a spatiotemporal graph Transformer framework that jointly models spatial interactions and temporal dependencies for traffic forecasting in edge computing. The framework employs graph neural networks to capture spatial correlations among service regions and leverages Transformer-based self-attention to learn long-range temporal patterns from historical traffic observations. By decoupling spatial representation learning from temporal reasoning, the proposed approach provides an effective mechanism for large-scale spatiotemporal traffic modeling. Extensive experiments on a real-world cellular network dataset demonstrate that the proposed graph Transformer consistently outperforms recurrent graph-based baselines, including GCN-RNN, GCN-LSTM, and GCN-GRU models, across multiple forecasting horizons. The resulting forecasts enable more effective proactive resource provisioning and reduce overload risk compared with reactive management strategies. These results highlight the potential of graph-enhanced attention mechanisms for building intelligent and adaptive edge computing systems.
Comments12 pages, 10 figures
