Network Traffic Prediction Using Least Mean Kurtosis

Hong ZHAO; Nirwan ANSARI; Yun Q. SHI

doi:10.1093/ietcom/e89-b.5.1672

Network Traffic Prediction Using Least Mean Kurtosis

Hong ZHAO, Nirwan ANSARI, Yun Q. SHI

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Recent studies of high quality, high resolution traffic measurements have revealed that network traffic appears to be statistically self similar. Contrary to the common belief, aggregating self-similar traffic streams can actually intensify rather than diminish burstiness. Thus, traffic prediction plays an important role in network management. In this paper, Least Mean Kurtosis (LMK), which uses the negated kurtosis of the error signal as the cost function, is proposed to predict the self similar traffic. Simulation results show that the prediction performance is improved greatly over the Least Mean Square (LMS) algorithm.

Publication: IEICE TRANSACTIONS on Communications Vol.E89-B No.5 pp.1672-1674

Publication Date: 2006/05/01

Publicized

Online ISSN: 1745-1345

DOI: 10.1093/ietcom/e89-b.5.1672

Type of Manuscript: LETTER

Category: Fundamental Theories for Communications

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Hong ZHAO, Nirwan ANSARI, Yun Q. SHI, "Network Traffic Prediction Using Least Mean Kurtosis" in IEICE TRANSACTIONS on Communications, vol. E89-B, no. 5, pp. 1672-1674, May 2006, doi: 10.1093/ietcom/e89-b.5.1672.
Abstract: Recent studies of high quality, high resolution traffic measurements have revealed that network traffic appears to be statistically self similar. Contrary to the common belief, aggregating self-similar traffic streams can actually intensify rather than diminish burstiness. Thus, traffic prediction plays an important role in network management. In this paper, Least Mean Kurtosis (LMK), which uses the negated kurtosis of the error signal as the cost function, is proposed to predict the self similar traffic. Simulation results show that the prediction performance is improved greatly over the Least Mean Square (LMS) algorithm.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e89-b.5.1672/_p

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@ARTICLE{e89-b_5_1672,
author={Hong ZHAO, Nirwan ANSARI, Yun Q. SHI, },
journal={IEICE TRANSACTIONS on Communications},
title={Network Traffic Prediction Using Least Mean Kurtosis},
year={2006},
volume={E89-B},
number={5},
pages={1672-1674},
abstract={Recent studies of high quality, high resolution traffic measurements have revealed that network traffic appears to be statistically self similar. Contrary to the common belief, aggregating self-similar traffic streams can actually intensify rather than diminish burstiness. Thus, traffic prediction plays an important role in network management. In this paper, Least Mean Kurtosis (LMK), which uses the negated kurtosis of the error signal as the cost function, is proposed to predict the self similar traffic. Simulation results show that the prediction performance is improved greatly over the Least Mean Square (LMS) algorithm.},
keywords={},
doi={10.1093/ietcom/e89-b.5.1672},
ISSN={1745-1345},
month={May},}

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TY - JOUR
TI - Network Traffic Prediction Using Least Mean Kurtosis
T2 - IEICE TRANSACTIONS on Communications
SP - 1672
EP - 1674
AU - Hong ZHAO
AU - Nirwan ANSARI
AU - Yun Q. SHI
PY - 2006
DO - 10.1093/ietcom/e89-b.5.1672
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E89-B
IS - 5
JA - IEICE TRANSACTIONS on Communications
Y1 - May 2006
AB - Recent studies of high quality, high resolution traffic measurements have revealed that network traffic appears to be statistically self similar. Contrary to the common belief, aggregating self-similar traffic streams can actually intensify rather than diminish burstiness. Thus, traffic prediction plays an important role in network management. In this paper, Least Mean Kurtosis (LMK), which uses the negated kurtosis of the error signal as the cost function, is proposed to predict the self similar traffic. Simulation results show that the prediction performance is improved greatly over the Least Mean Square (LMS) algorithm.
ER -