Adaptive Multi-slot-ahead Prediction of Network Traffic with Gaussian Process

Abstract

Multi-slot-ahead forecasting on network traffic provides an extra degree of freedom to proactively manipulate the network resources when immediate reconfiguration of networks is expensive or infeasible. In return, it challenges the existing data-driven learning-based approaches on accuracy, especially when considering the evolving property of the traffic process. To this end, we establish an adaptive learning framework for multi-slot-ahead network traffic prediction based on Gaussian Process (GP). GP facilitates learning and comprehending the traffic process from a Bayesian perspective, where the main characteristics can be encoded into the kernel function for performance enhancement. The contributions of this paper are two-fold: 1). To track the evolving traffic characteristics, we approximate the optimal kernel adapting to the current traffic. 2). To predict in a large time horizon without significantly hurt the performance, Linear Model of Co-regionalization (LMC) is utilized to better make use of the correlation among subsequent multiple time-slots. Finally, we demonstrate the high tracking capability as well as the superiority of the proposed framework in terms of prediction accuracy through simulation.