Research Article
Estimating current traffic matrices accurately by using long-term variations information
@INPROCEEDINGS{10.1109/BROADNETS.2008.4769116, author={Yuichi Ohsita and Takashi Miyamaura and Shin’ichi Arakawa and Eiji Oki and Kohei Shiomoto and Masayuki Murata}, title={Estimating current traffic matrices accurately by using long-term variations information}, proceedings={5th International ICST Conference on Broadband Communications, Networks, and Systems}, proceedings_a={BROADNETS}, year={2009}, month={1}, keywords={Gravity Information science Laboratories Monitoring Multiprotocol label switching Performance evaluation Routing Telecommunication traffic Tellurium Traffic control}, doi={10.1109/BROADNETS.2008.4769116} }- Yuichi Ohsita
Takashi Miyamaura
Shin’ichi Arakawa
Eiji Oki
Kohei Shiomoto
Masayuki Murata
Year: 2009
Estimating current traffic matrices accurately by using long-term variations information
BROADNETS
IEEE
DOI: 10.1109/BROADNETS.2008.4769116
Abstract
Obtaining current traffic matrices is essential to traffic engineering (TE) methods. Because it is difficult to monitor traffic matrices, several methods for estimating them from link loads have been proposed. The models used in these methods, however, are incorrect for some real networks. Thus, methods improving the accuracy of estimation by changing routes also have been proposed. However, existing methods for estimating the traffic matrix by changing routes, however, can only capture long-term variations and cannot obtain current traffic matrices accurately. In this paper, we propose a method for estimating current traffic matrices by using route changes introduced by a TE method. In this method, we first estimate the long-term variations of traffic by using the link loads monitored the last $M$ times. Then, we adjust the estimated long-term variations so as to fit the current link loads. In addition, when the traffic variation trends change and the estimated long-term variations cannot match the current traffic, our method detects mismatches. Then, so as to capture the current traffic variations, the method re-estimates the long-term variations after removing information about the end-to-end traffic causing the mismatches. For this paper, we evaluated our method through simulation. The results show that our method can estimate current traffic matrices accurately even when some end-to-end traffic changes suddenly.