Network traffic prediction using least mean kurtosis

Document Type

Article

Publication Date

1-1-2006

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. Copyright © 2006 The Institute of Electronics, Information and Communication Engineers.

Identifier

33646810821 (Scopus)

Publication Title

IEICE Transactions on Communications

External Full Text Location

https://doi.org/10.1093/ietcom/e89-b.5.1672

e-ISSN

17451345

ISSN

09168516

First Page

1672

Last Page

1674

Issue

5

Volume

E89-B

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