Neural Network-based Traffic Matrix Prediction Incorporating Inter-Flow Correlations for Optical Network-on-Chip (ONoC)

Optical Network-on-Chip (ONoC) is a promising type of interconnection technology for high performance computing systems, with high bandwidth and low latency. In Optical Network-on-Chip (ONoC), accurate traffic prediction facilitates the path reservation and resource allocation in advance, which in turn reduces waiting delay and power overhead. In this work, neural network-based traffic prediction is proposed for ONoC, based on traffic matrix (TM). Different neural network-based traffic predictions are compared. In addition, inter-flow correlations are considered in the traffic prediction. The evaluation results show that LSTM-based traffic prediction performs more efficient. Also, the prediction results are more accurate incorporating inter-flow correlations.