Spatiotemporal graph convolutional recurrent networks for traffic matrix prediction,Transactions on Emerging Telecommunications Technologies

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Spatiotemporal graph convolutional recurrent networks for traffic matrix prediction
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2020-07-30 , DOI: 10.1002/ett.4056
Jianlong Zhao ₁ , Hua Qu _{1,

2} , Jihong Zhao _{2,

3} , Huijun Dai ₂ , Dingchao Jiang ₂

Affiliation

Through accurate network‐wide traffic prediction, network operators can agilely manage resources and improve robustness by proactively adapting to new traffic patterns, especially for traffic engineering, capacity planning and quality of service provisioning. However, due to the proliferation of backbone network traffic as well as the complexity and dynamics of network communication behavior, accurate and effective network‐wide traffic prediction is challenging. To address the challenges, this paper focuses on short‐term traffic matrix (TM) prediction in large‐scale IP backbone networks. In order to improve the prediction performance, a novel spatiotemporal graph convolutional recurrent network (SGCRN)—a deep learning framework that incorporates both spatial and temporal dependencies of traffic flows, is proposed to implement TM prediction with high accuracy and efficiency. By learning network‐wide traffic as graph‐structured TM time series, SGCRN jointly utilizes graph convolutional networks (GCN) and gated recurrent units (GRU) networks to extract comprehensive spatiotemporal correlations among traffic flows. Specifically, SGCRN employs GCNs to identify structural spatial features of traffic flows by considering topological properties, and utilizes GRUs to implement temporal features learning by considering short and long‐term dynamics of traffic flows. Extensive experimental results on the inter‐Points of Presence network traffic data from four real IP backbone networks show that SGCRN can effectively predict short‐term network‐wide TM with superior accuracy compared with other four widely used traffic prediction methods.

中文翻译：

时空图卷积递归网络用于交通矩阵预测

通过准确的全网络流量预测，网络运营商可以通过主动适应新的流量模式来灵活地管理资源并提高鲁棒性，尤其是在流量工程，容量规划和服务质量提供方面。但是，由于骨干网络流量的激增以及网络通信行为的复杂性和动态性，准确，有效的全网络流量预测具有挑战性。为了解决这些挑战，本文重点研究了大型IP骨干网中的短期流量矩阵（TM）预测。为了提高预测性能，一种新颖的时空图卷积递归网络（SGCRN）是一种深度学习框架，其中融合了流量的时空依赖性，提出了以高精度和高效率实现TM预测的方法。通过将全网流量作为图结构TM时间序列进行学习，SGCRN联合利用图卷积网络（GCN）和门控循环单元（GRU）网络来提取流量之间的综合时空相关性。具体而言，SGCRN利用GCN通过考虑拓扑属性来识别交通流的结构空间特征，并利用GRU通过考虑交通流的短期和长期动态来实现时间特征学习。来自四个真实IP骨干网的存在网络间流量数据的广泛实验结果表明，与其他四种广泛使用的流量预测方法相比，SGCRN可以有效地以更高的精度预测短期全网TM。

更新日期：2020-07-30

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