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37th Annual IEEE Conference on Local Computer Networks
Time series matrix factorization prediction of internet traffic matrices
Clearwater Beach, FL, USA USA
October 22-October 25
ISBN: 978-1-4673-1565-4
Min Liu, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
Shaojie Tang, Illinois Institute of Technology, Chicago, 60616-3793, USA
Xufei Mao, TNLIST, School of Software, Tsinghua University, Beijing 100190, China
Traffic matrices (TMs) are very important for traffic engineering and if they can be predicted, the network operations can be made beforehand. However, existing prediction methods are neither accurate nor efficient in practice. In this paper, we utilize the spatio-temporal property and low rank nature to directly predict the total TMs. The problem is that conventional matrix interpolation only works well when elements are missing uniformly and randomly. But in the case of TMs prediction, an entire part of the matrix is unknown. To solve this problem, we utilize some essential properties of TMs and add the time series forecasting into the matrix interpolation. We analyze our algorithm and evaluate its performance. The experiment result shows that our method can predict TMs under an NMAE of 30% in most cases, even predicting all the elements of next 3 weeks.
Index Terms:
Prediction algorithms,Forecasting,Interpolation,Time series analysis,Sensitivity,Internet,Computers,matrix interpolation,traffic matrices prediction,time series forecasting
Citation:
Min Liu, Shaojie Tang, Xufei Mao, "Time series matrix factorization prediction of internet traffic matrices," lcn, pp.284-287, 37th Annual IEEE Conference on Local Computer Networks, 2012
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