Spatiotemporal graph convolutional recurrent networks for traffic matrix prediction

Abstract

Summary 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.

Internet traffic matrix prediction with convolutional LSTM neural network

“…This years. 16,17 The third direction is to explore the use of the multimodal technique for traffic prediction, which learns both intra-and inter-modality dependencies in the traffic data and has been proven effective for the mobile encrypted traffic classification task. 18…”

Graph-based Deep Learning for Communication Networks: A Survey

“…Computer Networks [13,14] Electronics [15] IEEE Access [16,17] IEEE Communications Letters [18,19,20,21] IEEE Internet of Things Journal [22] IEEE Journal on Selected Areas in Communications [23,24,25,26] IEEE Systems Journal [27] IEEE Transactions on Industrial Informatics [28] IEEE Transactions on Information Forensics and Security [29] IEEE Transactions on Mobile Computing [30,31] IEEE Transactions on Network Science and Engineering [32] IEEE Transactions on Network and Service Management [33] IEEE Transactions on Signal Processing [34] IEEE Transactions on Vehicular Technology [35] IEEE Transactions on Wireless Communications [36,37] International Journal of Network Management [38] Performance Evaluation [39] Sensors [40] Transactions on Emerging Telecommunications Technologies [41] conducting a thorough literature search on the graph-based models. For now, we would give a short introduction for the GNNs used in the surveyed studies.…”

“…GNNs can also be used for network prediction, e.g., delay prediction [21] and traffic prediction [41,53,106]. The better prediction is the basis of proactive management.…”

“…Several studies are concerned about data-driven traffic prediction, based on the real-world network traffic data and GNN-based solutions. A framework named Spatio-temporal Graph Convolutional Recurrent Network (SGCRN) is proposed in [41], which combines GCN and GRU and is validated on the network traffic data from four real IP backbone networks. Another framework named Multi-scale Spatial-temporal Graph Neural Network (MSTNN) is proposed for Origin-Destination Traffic Prediction (ODTP) and two real-world datasets are used for evaluation [53].…”

“…GNN [82] Botnet Detection [75] GNN Approach GCN [88] Communication Delay Estimation [65] GNNs with Semisupervised Learning GCN [88] Delay Prediction [21] Message-passing Neural Networks MPNN [90] Intrusion Detection [18] Alert-GCN GCN [88] MPLS Configuration Analysis [63] DeepMPLS GNN [82] Network Calculus Analysis [47,32,58] DL-assisted Tandem Matching Analysis GNN [82] Network Configuration Feasibility [70] Ensemble GNN Model GN [91] Network Modeling [42] RouteNet MPNN [90] Network Modeling [72] Extended RouteNet MPNN [90] Network Modeling [105] Graph-based DL GN [91], GNN [82] Network Modeling [39] DeepComNet GGS-NN [93] Routing [79] Graph-Query Neural Network GNN [82] Routing and Load Balancing [81] DL-based Distributed Routing GNN [82] Traffic Prediction [41] SGCRN GCN [88] Traffic Prediction [53] MSTNN GAT [89] Traffic Prediction [106] Nonautoregressive…”