time series matrix factorization prediction of internet traffic matrices

ISCAS OpenIR

	time series matrix factorization prediction of internet traffic matrices
	Song Yunlong; Liu Min; Tang Shaojie; Mao Xufei
	2012
会议名称	37th Annual IEEE Conference on Local Computer Networks, LCN 2012
会议录名称	Proceedings - Conference on Local Computer Networks, LCN
页码	284-287
会议日期	October 22, 2012 - October 25, 2012
会议地点	Clearwater, FL, United states
收录类别	EI
ISBN	9781467315647
部门归属	(1) Institute of Computing Technology Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences Beijing 100190 China; (2) Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China; (3) Illinois Institute of Technology Chicago IL 60616-3793 United States; (4) TNLIST School of Software Tsinghua University Beijing 100190 China
摘要	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. © 2012 IEEE.; 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. © 2012 IEEE.
关键词	Interpolation Telecommunication Traffic Time Series
主办者	IEEE Computer Society; IEEE Comput. Soc. Tech. Comm. Comput. Commun. (TCCC)
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15909
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Song Yunlong,Liu Min,Tang Shaojie,et al. time series matrix factorization prediction of internet traffic matrices[C],2012:284-287.