Internet traffic prediction by W-Boost: Classification and regression

Hanghang Tong, Chongrong Li, Jingrui He, Yang Chen

Research output: Contribution to journalConference articlepeer-review

Abstract

Internet traffic prediction plays a fundamental role in network design, management, control, and optimization. The self-similar and non-linear nature of network traffic makes highly accurate prediction difficult In this paper, we proposed a new boosting scheme, namely W-Boost, for traffic prediction from two perspectives: classification and regression. To capture the non-linearity of the traffic while introducing low complexity into the algorithm, 'stump' and piece-wise-constant function are adopted as weak learners for classification and regression, respectively. Furthermore, a new weight update scheme is proposed to take the advantage of the correlation information within the traffic for both models. Experimental results on real network traffic which exhibits both self-similarity and non-linearity demonstrate the effectiveness of the proposed W-Boost.

Original languageEnglish (US)
Pages (from-to)397-402
Number of pages6
JournalLecture Notes in Computer Science
Volume3498
Issue numberIII
DOIs
StatePublished - Jan 1 2005
Externally publishedYes
EventSecond International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 - Chongqing, China
Duration: May 30 2005Jun 1 2005

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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