Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/LCN.2012.6423629 | LCN |
Keywords | Field | DocType |
internet traffic matrix,traffic matrices prediction,spatio-temporal property,traffic matrix,interpolation,conventional matrix interpolation,experiment result,total tms,matrix algebra,entire part,time series matrix factorization,tms prediction,network operations,existing prediction method,internet,essential property,time series matrix factorization prediction,internet traffic matrices,telecommunication traffic,traffic engineering,time series forecasting,matrix interpolation,time series,low rank nature,tm prediction,prediction algorithms,time series analysis,sensitivity,forecasting | Time series,Matrix (mathematics),Computer science,Matrix decomposition,Interpolation,Algorithm,Theoretical computer science,Network operations center,Traffic engineering,Internet traffic,The Internet,Distributed computing | Conference |
ISSN | ISBN | Citations |
0742-1303 | 978-1-4673-1565-4 | 3 |
PageRank | References | Authors |
0.43 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunlong Song | 1 | 9 | 1.57 |
| Min Liu | 2 | 335 | 40.49 |
| Tang Shaojie | 3 | 2224 | 157.73 |
| XuFei Mao | 4 | 858 | 45.54 |