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| time series matrix factorization prediction of internet traffic matrices | |
| Song Yunlong; Liu Min; Tang Shaojie; Mao Xufei | |
| 2012 | |
| Conference Name | 37th Annual IEEE Conference on Local Computer Networks, LCN 2012 |
| Source | Proceedings - Conference on Local Computer Networks, LCN |
| Pages | 284-287 |
| Conference Date | October 22, 2012 - October 25, 2012 |
| Conference Place | Clearwater, FL, United states |
| Indexed Type | EI |
| ISBN | 9781467315647 |
| Department | (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 |
| English Abstract | 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. |
| Keyword | Interpolation Telecommunication Traffic Time Series |
| Sponsorship | IEEE Computer Society; IEEE Comput. Soc. Tech. Comm. Comput. Commun. (TCCC) |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/15909 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Song Yunlong,Liu Min,Tang Shaojie,et al. time series matrix factorization prediction of internet traffic matrices[C],2012:284-287. |
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