The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
현재 트래픽 매트릭스를 얻는 것은 트래픽 엔지니어링(TE) 방법에 필수적입니다. 트래픽 매트릭스를 모니터링하는 것은 어렵기 때문에 링크 부하로부터 이를 추정하는 여러 가지 방법이 제안되었습니다. 그러나 이러한 방법에 사용된 모델은 일부 실제 네트워크에서는 올바르지 않습니다. 따라서 경로를 변경하여 추정의 정확도를 높이는 방법도 제안되었다. 그러나 기존의 경로 변경을 통한 교통 매트릭스 추정 방법은 장기적인 변화만을 포착할 수 있을 뿐 현재의 교통 매트릭스를 정확하게 얻을 수 없다. 본 논문에서는 TE 방법에 의해 도입된 경로 변경을 이용한 현재 트래픽 매트릭스를 추정하는 방법을 제안한다. 이 방법에서는 먼저 이전에 모니터링한 링크 부하를 사용하여 장기적인 트래픽 변화를 추정합니다. 그런 다음 현재 링크 부하에 맞게 예상된 장기 변동을 조정합니다. 또한 트래픽 변동 추세가 변하고 예상되는 장기 변동이 현재 트래픽과 일치하지 않는 경우 우리의 방법은 불일치를 감지합니다. 그런 다음 현재 트래픽 변동을 포착하기 위해 불일치를 유발하는 종단 간 트래픽에 해당하는 모니터링 데이터를 제거한 후 장기 변동을 재추정합니다. 우리는 시뮬레이션을 통해 우리의 방법을 평가합니다. 결과는 일부 종단 간 트래픽이 갑자기 변경되는 경우에도 우리 방법이 현재 트래픽 매트릭스를 정확하게 추정할 수 있음을 보여줍니다.
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Yuichi OHSITA, Takashi MIYAMURA, Shin'ichi ARAKAWA, Eiji OKI, Kohei SHIOMOTO, Masayuki MURATA, "Estimation of Current Traffic Matrices from Long-Term Traffic Variations" in IEICE TRANSACTIONS on Communications,
vol. E92-B, no. 1, pp. 171-183, January 2009, doi: 10.1587/transcom.E92.B.171.
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 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 that uses 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 at previous 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 fail to match the current traffic, our method detects mismatch. Then, so as to capture the current traffic variations, the method re-estimates the long-term variations after removing monitored data corresponding to the end-to-end traffic causing the mismatches. We evaluate 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.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E92.B.171/_p
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@ARTICLE{e92-b_1_171,
author={Yuichi OHSITA, Takashi MIYAMURA, Shin'ichi ARAKAWA, Eiji OKI, Kohei SHIOMOTO, Masayuki MURATA, },
journal={IEICE TRANSACTIONS on Communications},
title={Estimation of Current Traffic Matrices from Long-Term Traffic Variations},
year={2009},
volume={E92-B},
number={1},
pages={171-183},
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 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 that uses 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 at previous 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 fail to match the current traffic, our method detects mismatch. Then, so as to capture the current traffic variations, the method re-estimates the long-term variations after removing monitored data corresponding to the end-to-end traffic causing the mismatches. We evaluate 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.},
keywords={},
doi={10.1587/transcom.E92.B.171},
ISSN={1745-1345},
month={January},}
부
TY - JOUR
TI - Estimation of Current Traffic Matrices from Long-Term Traffic Variations
T2 - IEICE TRANSACTIONS on Communications
SP - 171
EP - 183
AU - Yuichi OHSITA
AU - Takashi MIYAMURA
AU - Shin'ichi ARAKAWA
AU - Eiji OKI
AU - Kohei SHIOMOTO
AU - Masayuki MURATA
PY - 2009
DO - 10.1587/transcom.E92.B.171
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E92-B
IS - 1
JA - IEICE TRANSACTIONS on Communications
Y1 - January 2009
AB - 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 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 that uses 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 at previous 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 fail to match the current traffic, our method detects mismatch. Then, so as to capture the current traffic variations, the method re-estimates the long-term variations after removing monitored data corresponding to the end-to-end traffic causing the mismatches. We evaluate 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.
ER -