Network traffic prediction using least mean kurtosis

Hong Zhao, Nirwan Ansari, Yun Q. Shi

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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)
Pages (from-to)1672-1674
Number of pages3
JournalIEICE Transactions on Communications
Issue number5
StatePublished - May 2006

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


  • Internet traffic
  • LMK
  • Self-similar
  • Traffic prediction


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