GCRINT: Network Traffic Imputation Using Graph Convolutional Recurrent Neural Network

Abstract

Missing values appear in most multivariate time series, especially in the monitored network traffic data due to high measurement cost and unavoidable loss. In the networking fields, missing data prevents advanced analysis and downgrades downstream applications such as traffic engineering and anomaly detection. Despite the great potential, existing imputation approaches based on tensor decomposition and deep learning techniques have shown limitations in addressing missing values of traffic data due to its dynamic behavior. In this paper, we propose Graph Convolutional Recurrent Neural Network for Imputing Network Traffic (GCRINT), a combination between Recurrent Neural Network (RNN) and Graph Convolutional Neural Network, for filling the missing values of network traffic data. We use a bidirectional Long Short-Term Memory network and Graph Neural Network to efficiently learn the spatial-temporal correlations in partially observed data. We conducted extensive experiments to evaluate our model by using two different datasets and various missing scenarios. The experiment results show that GCRINT achieves significantly low imputation errors and reduces the error by 35% compared to the state-of-the-art methods. GCRINT also helps to obtain a stable performance in the traffic engineering problem.

STBGRN: A Traffic Prediction Model Based on Spatiotemporal Bidirectional Gated Recurrent Units and Graph Convolutional Residual Networks

“…To address the issue of fine-grained traffic estimation, Joshi et al proposed a deep learning-based method for fine-grained vehicle flow analysis from traffic videos [28] to accurately estimate the traffic state. Le et al proposed a GCRINT [29] model combining RNN and GCN to fill in the missing values of network traffic data. He and his colleagues proposed the STNN [30] model, which consists of global spatiotemporal components and local spatiotemporal components.…”

“…The model lacks spatial dependency GCRINT [29] The GCRINT model combining RNN and GCN is used to fill in the missing values of network traffic data…”

Achieving Multi-Time-Step Segment Routing via Traffic Prediction and Compressive Sensing Techniques

“…Firstly, there is a problem of high network disturbance or a large number of rerouting flows, leading to a degradation in the overall network's Quality of Service (QoS) [3]. Most of the proposed solutions only address the routing problem in a single snapshot (which is called a "time-step" in this paper) or use a short-term traffic prediction to calculate the routing rules without considering the long time horizon [4]- [7]. Due to the dynamic behavior of the network traffic, the traffic matrix often varies over time, and the network controller may need to reroute many flows to balance the traffic loads, leading to significant network disturbance and service disruption.…”

“…In addition, applying ML/DNN into networking also needs a huge amount of monitored data for training/predicting processes. Although the quality of the measurements may have a huge impact on the performance of the TE solution [8], there are only a few studies that consider the joint problem of network monitoring and traffic engineering. Moreover, the scalability issue of applying ML/DNN techniques is often omitted in many ML-based TE studies.…”

A multi-task learning-based generative adversarial network for red tide multivariate time series imputation

“…GCNMF [28] uses Gaussian mixture distributions to represent incomplete features and derive the expected activation of the first layer neurons in GCN. Other graph-based methods focus on traffic data imputation including [29][30][31].…”