Self-similar traffic prediction using least mean kurtosis

Hong Zhao, Nirwan Ansari, Yun-Qing Shi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings - ITCC 2003, International Conference on Information Technology
Subtitle of host publicationComputers and Communications
EditorsPradip K. Srimani, Emma Regentova, Ray Hashemi, Elaine Lawrence, Mario Cannataro, Amanda Spink, Wolf Bein
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages352-355
Number of pages4
ISBN (Electronic)0769519164, 9780769519166
DOIs
StatePublished - Jan 1 2003
EventInternational Conference on Information Technology: Computers and Communications, ITCC 2003 - Las Vegas, United States
Duration: Apr 28 2003Apr 30 2003

Publication series

NameProceedings ITCC 2003, International Conference on Information Technology: Computers and Communications

Other

OtherInternational Conference on Information Technology: Computers and Communications, ITCC 2003
Country/TerritoryUnited States
CityLas Vegas
Period4/28/034/30/03

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems
  • Media Technology
  • Computer Networks and Communications
  • Software

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