Interactive Temporal Recurrent Convolution Network for Traffic Prediction in Data Centers - Xiaofeng Cao; Yuhua Zhong; Yun Zhou; Jiang Wang; Cheng Zhu; Weiming Zhang (IEEE Access, 2018)
Accurately predicting future service traffic would be of great help for load balancing and resource allocation, which plays a key role in guaranteeing the quality of service (QoS) in cloud computing. With the rapid development of data center, the large-scale network traffic prediction requires more suitable methods to deal with the complex properties (e.g., high-dimension, long-range dependence, non-linearity, and so on). However, due to the limitations of traditional methods (e.g., strong theoretical assumptions and simple implementation), few research works could predict the large-scale network traffic efficiently and accurately. More importantly, most of the studies took only the temporal features but without the services' communications into consideration, which may weaken the QoS of applications in the data center. To this end, we applied the gated recurrent unit (GRU) model and the interactive temporal recurrent convolution network (ITRCN) to single-service traffic prediction and interactive network traffic prediction, respectively. Especially, ITRCN takes the communications between services as a whole and directly predicts the interactive traffic in large-scale network. Within the ITRCN model, the convolution neural network (CNN) part learns network traffic as images to capture the network-wide services' correlations, and the GRU part learns the temporal features to help the interactive network traffic prediction. We conducted comprehensive experiments based on the Yahoo! data sets, and the results show that the proposed novel method outperforms the conventional GRU and CNN method by an improvement of 14.3% and 13.0% in root mean square error, respectively.