A boosting-based framework for self-similar and non-linear internet traffic prediction

Hanghang Tong, Chongrong Li, Jingrui He

Research output: Chapter in Book/Report/Conference proceedingChapter

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, a boosting-based framework is proposed for self-similar and non-linear traffic prediction by considering it as a classical regression problem. The framework is based on Ada-Boost on the whole. It adopts Principle Component Analysis as an optional step to take advantage of self-similar nature of traffic while avoiding the disadvantage of self-similarity. Feed-forward neural network is used as the basic regressor to capture the non-linear relationship within the traffic. Experimental results on real network traffic validate the effectiveness of the proposed framework.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsFuliang Yin, Chengan Guo, Jun Wang
PublisherSpringer
Pages931-936
Number of pages6
ISBN (Print)3540228438, 9783540228431
DOIs
StatePublished - Jan 1 2004
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3174
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint

Dive into the research topics of 'A boosting-based framework for self-similar and non-linear internet traffic prediction'. Together they form a unique fingerprint.

Cite this