Self-similar traffic prediction using least mean kurtosis

Document Type

Conference Proceeding

Publication Date

1-1-2003

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.

Identifier

84978828149 (Scopus)

ISBN

[0769519164, 9780769519166]

Publication Title

Proceedings ITCC 2003 International Conference on Information Technology Computers and Communications

External Full Text Location

https://doi.org/10.1109/ITCC.2003.1197554

First Page

352

Last Page

355

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