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Traffic-Predicting A Routing Algorithm Using Time Series Models

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Computational Science and Its Applications - ICCSA 2006 (ICCSA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3983))

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Abstract

A routing algorithm is proposed that analyzes network traffic conditions using time series prediction models and determines the best-effort routing path. To predict network traffic, time series models are developed under the stationary assumption, which is evaluated using the Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF). Traffic congestion is assumed when the predicted result is larger than the permitted bandwidth. Although the proposed routing algorithm requires additional processing time to predict the number of packets, the packet transmission time is reduced by 5~10% and the amount of packet loss is also reduced by about 3% in comparison to the OSPF routing algorithm. With the proposed routing algorithm, the predicted network traffic allows the routing path to be modified to avoid traffic congestion. Consequently, the traffic predicting and load balancing by modifying the paths avoids path congestion and increases the network performance.

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Author information

Authors and Affiliations

  1. School of Computer Engineering, Kyungil University, 712-701, 33 Buho-ri, Hayang-up, Gyeongsan-si, Gyeongsang buk-do, Korea

    Sangjoon Jung

  2. Dept. of Computer Engineering, Yeungnam University, 712-749, 214-1, Dae-dong, Gyeongsan-si, Gyeongsangbuk-do, Korea

    Mary Wu & Chonggun Kim

  3. School of Computer Engineering, Kyungwoon University, 730-850, 55 Indeok-ri, Sandong-myeon, Gumi-si, Gyeungsang buk-do, Korea

    Youngsuk Jung

Authors
  1. Sangjoon Jung
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  2. Mary Wu
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  3. Youngsuk Jung
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  4. Chonggun Kim
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  2. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

    Osvaldo Gervasi

  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

    Vipin Kumar

  4. OptimaNumerics Ltd.,, Cathedral House 23-31 Waring Street, BT1 2DX, Belfast, UK

    C. J. Kenneth Tan

  5. Clayton School of IT, Monash University, 3800, Clayton, Australia

    David Taniar

  6. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, I-06123, Perugia, Italy

    Antonio Laganá

  7. School of Computing, Soongsil University, Seoul, Korea

    Youngsong Mun

  8. School of Information and Communication Engineering, Sungkyunkwan University, Korea

    Hyunseung Choo

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© 2006 Springer-Verlag Berlin Heidelberg

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Jung, S., Wu, M., Jung, Y., Kim, C. (2006). Traffic-Predicting A Routing Algorithm Using Time Series Models. In: Gavrilova, M.L., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751632_110

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  • DOI: https://doi.org/10.1007/11751632_110

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34077-5

  • Online ISBN: 978-3-540-34078-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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