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
IP 액세스 네트워크의 트래픽은 백본 네트워크에 비해 덜 집계되기 때문에 그 차이가 클 수 있으며 그 분포는 본질적으로 가우시안이 아닌 롱테일일 수 있습니다. 이러한 특성으로 인해 적절한 용량 계획을 위해 IP 액세스 네트워크의 트래픽 양을 예측하기가 어렵습니다. 본 논문에서는 IP 접속 네트워크에서 잔여 오류 분포를 제어하는 기능을 포함하는 트래픽 예측 방법을 제안한다. 제안된 방법의 목적은 피크 트래픽 변동의 통계적 특성을 파악하는 것인데, 기존 방법은 피크 값이 아닌 평균에 중점을 둡니다. 제안된 방법에서는 피크 주변의 잔여 오차에 가중치를 부여하면서 재귀적으로 신경망 모델을 구축합니다. 이를 통해 네트워크 운영자는 계획 정책에 따라 과소평가 오류와 과대평가 오류 간의 균형을 제어할 수 있습니다. 실제 IP 액세스 네트워크에서 측정된 총 136개의 일일 트래픽 양 데이터 시퀀스를 평가하여 제안 방법의 성능을 입증합니다.
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부
Takeshi KITAHARA, Hiroki FURUYA, Hajime NAKAMURA, "A Traffic Forecasting Method with Function to Control Residual Error Distribution for IP Access Networks" in IEICE TRANSACTIONS on Communications,
vol. E93-B, no. 1, pp. 47-55, January 2010, doi: 10.1587/transcom.E93.B.47.
Abstract: Since traffic in IP access networks is less aggregated than in backbone networks, its variance could be significant and its distribution may be long-tailed rather than Gaussian in nature. Such characteristics make it difficult to forecast traffic volume in IP access networks for appropriate capacity planning. This paper proposes a traffic forecasting method that includes a function to control residual error distribution in IP access networks. The objective of the proposed method is to grasp the statistical characteristics of peak traffic variations, while conventional methods focus on average rather than peak values. In the proposed method, a neural network model is built recursively while weighting residual errors around the peaks. This enables network operators to control the trade-off between underestimation and overestimation errors according to their planning policy. Evaluation with a total of 136 daily traffic volume data sequences measured in actual IP access networks demonstrates the performance of the proposed method.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E93.B.47/_p
부
@ARTICLE{e93-b_1_47,
author={Takeshi KITAHARA, Hiroki FURUYA, Hajime NAKAMURA, },
journal={IEICE TRANSACTIONS on Communications},
title={A Traffic Forecasting Method with Function to Control Residual Error Distribution for IP Access Networks},
year={2010},
volume={E93-B},
number={1},
pages={47-55},
abstract={Since traffic in IP access networks is less aggregated than in backbone networks, its variance could be significant and its distribution may be long-tailed rather than Gaussian in nature. Such characteristics make it difficult to forecast traffic volume in IP access networks for appropriate capacity planning. This paper proposes a traffic forecasting method that includes a function to control residual error distribution in IP access networks. The objective of the proposed method is to grasp the statistical characteristics of peak traffic variations, while conventional methods focus on average rather than peak values. In the proposed method, a neural network model is built recursively while weighting residual errors around the peaks. This enables network operators to control the trade-off between underestimation and overestimation errors according to their planning policy. Evaluation with a total of 136 daily traffic volume data sequences measured in actual IP access networks demonstrates the performance of the proposed method.},
keywords={},
doi={10.1587/transcom.E93.B.47},
ISSN={1745-1345},
month={January},}
부
TY - JOUR
TI - A Traffic Forecasting Method with Function to Control Residual Error Distribution for IP Access Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 47
EP - 55
AU - Takeshi KITAHARA
AU - Hiroki FURUYA
AU - Hajime NAKAMURA
PY - 2010
DO - 10.1587/transcom.E93.B.47
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E93-B
IS - 1
JA - IEICE TRANSACTIONS on Communications
Y1 - January 2010
AB - Since traffic in IP access networks is less aggregated than in backbone networks, its variance could be significant and its distribution may be long-tailed rather than Gaussian in nature. Such characteristics make it difficult to forecast traffic volume in IP access networks for appropriate capacity planning. This paper proposes a traffic forecasting method that includes a function to control residual error distribution in IP access networks. The objective of the proposed method is to grasp the statistical characteristics of peak traffic variations, while conventional methods focus on average rather than peak values. In the proposed method, a neural network model is built recursively while weighting residual errors around the peaks. This enables network operators to control the trade-off between underestimation and overestimation errors according to their planning policy. Evaluation with a total of 136 daily traffic volume data sequences measured in actual IP access networks demonstrates the performance of the proposed method.
ER -