Skip to main navigation Skip to search Skip to main content

Network traffic prediction model based on training data

  • Jinwoo Park
  • , Syed M. Raza
  • , Pankaj Thorat
  • , Dongsoo S. Kim
  • , Hyunseung Choo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Real-time audio and video services have gained much popularity in last decade, and now occupying a large portion of the total network traffic in the Internet. As the real-time services are becoming mainstream the demand for Quality of Service (QoS) is greater than ever before. To satisfy the increasing demand for QoS, it is necessary to use the network resources to the fullest. In this regards, the available bandwidth based routing is a promising solution. Unfortunately the instantaneous available bandwidth of a network is not enough as it may change the next moment in highly dynamic networks. To solve this issue, we present a prediction model for network traffic, on the basis of which network available bandwidth can be estimated. This paper utilizes the efforts done in regard to road traffic prediction to formulate a prediction model for network traffic.

Original languageEnglish
Title of host publicationComputational Science and Its Applications - ICCSA 2015 - 15th International Conference, Proceedings
EditorsMarina L. Gavrilova, Osvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Carmelo Torre, David Taniar, Bernady O. Apduhan, Ana Maria A.C. Rocha, Sanjay Misra
PublisherSpringer Verlag
Pages117-127
Number of pages11
ISBN (Print)9783319214092
DOIs
StatePublished - 2015
Event15th International Conference on Computational Science and Its Applications, ICCSA 2015 - Banff, Canada
Duration: 22 Jun 201525 Jun 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9158
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Computational Science and Its Applications, ICCSA 2015
Country/TerritoryCanada
CityBanff
Period22/06/1525/06/15

Keywords

  • K-Nearest neighbors
  • Modeling
  • Network traffic prediction

Fingerprint

Dive into the research topics of 'Network traffic prediction model based on training data'. Together they form a unique fingerprint.

Cite this