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International Conference on Testbeds and Research Infrastructures

TridentCom 2019: Testbeds and Research Infrastructures for the Development of Networks and Communications pp 62–77Cite as

Genetic Algorithm Based Solution for Large-Scale Topology Mapping

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Abstract

Simulating large-scale network experiments requires powerful physical resources. However, partitioning could be used to reduce the required power of the resources and to reduce the simulation time. Topology mapping is a partitioning technique that maps the simulated nodes to different physical nodes based on a set of conditions. In this paper, genetic algorithm-based mapping is proposed to solve the topology mapping problem. The obtained results prove a high reduction in simulation time, in addition to high utilization of the used resources (The number of used resources is minimum).

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Author information

Authors and Affiliations

  1. Department of Computer and Systems Engineering, Alexandria University, Alexandria, Egypt

    Nada Osman, Mustafa ElNainay & Moustafa Youssef

  2. Islamic University of Madinah, Medina, Saudi Arabia

    Mustafa ElNainay

Authors
  1. Nada Osman
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  2. Mustafa ElNainay
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  3. Moustafa Youssef
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Corresponding author

Correspondence to Nada Osman .

Editor information

Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. School of Software, Central South University, Changsha, China

    Kuang Li

  3. Shanghai Polytechnic University, Shanghai, China

    Xiaoxian Yang

  4. Hangzhou Dianzi University, Hangzhou Shi, Zhejiang, China

    Yuyu Yin

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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Osman, N., ElNainay, M., Youssef, M. (2020). Genetic Algorithm Based Solution for Large-Scale Topology Mapping. In: Gao, H., Li, K., Yang, X., Yin, Y. (eds) Testbeds and Research Infrastructures for the Development of Networks and Communications. TridentCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 309. Springer, Cham. https://doi.org/10.1007/978-3-030-43215-7_5

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  • DOI: https://doi.org/10.1007/978-3-030-43215-7_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43214-0

  • Online ISBN: 978-3-030-43215-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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