The Learning and Prediction of Network Traffic: A Revisiting to Sparse Representation

Abstract

With accurate network traffic prediction, future communication networks can realize self-management and enjoy intelligent and efficient automation. Benefiting from discovering the sparse property of network traffic in temporal domain, it becomes possible to develop compact algorithms with high accuracy and low computational complexity. For this purpose, we establish an analytical framework for network traffic prediction by extending traditional sparse representation to predictive sparse representation, and try to take the full advantage of such sparsity. Specifically, 1). To equip sparse representation with predictive capability, we divide the historical traffic records into two sets, and jointly train the representative/predictive dictionaries, such that the query point is embedded in terms of a sparse combination of dictionary atoms, and jointly coded with its T+1 time slot behind counterpart. 2). To estimate the sparse code of the query point, we only have to decompose its counterpart into a sparse combination of the representative dictionary atoms by adopting iterative projection method, which provides extra flexibility and adaptability in determining the dependence range. After this, the prediction is performed based on the predictive dictionary. 3). To promote the capability of capturing the rapidly changing traffic, we slightly modify the sparse representation-based prediction by adopting Lyapunov optimization, and minimize the time averaged prediction error. Finally, our proposed algorithm is evaluated by simulation to show its superiority over the conventional schemes.