Genetic Algorithm Based Solution for Large-Scale Topology Mapping

Nada Osman; Mustafa ElNainay; Moustafa Youssef

Testbeds and Research Infrastructures for the Development of Networks and Communications. 14th EAI International Conference, TridentCom 2019, Changsha, China, December 7-8, 2019, Proceedings

Research Article

Genetic Algorithm Based Solution for Large-Scale Topology Mapping

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@INPROCEEDINGS{10.1007/978-3-030-43215-7_5,
    author={Nada Osman and Mustafa ElNainay and Moustafa Youssef},
    title={Genetic Algorithm Based Solution for Large-Scale Topology Mapping},
    proceedings={Testbeds and Research Infrastructures for the Development of Networks and Communications. 14th EAI International Conference, TridentCom 2019, Changsha, China, December 7-8, 2019, Proceedings},
    proceedings_a={TRIDENTCOM},
    year={2020},
    month={3},
    keywords={Network simulation Topology mapping Testbeds Genetic algorithm},
    doi={10.1007/978-3-030-43215-7_5}
}

Nada Osman
Mustafa ElNainay
Moustafa Youssef
Year: 2020
Genetic Algorithm Based Solution for Large-Scale Topology Mapping
TRIDENTCOM
Springer
DOI: 10.1007/978-3-030-43215-7_5

Nada Osman¹^,*, Mustafa ElNainay^,*, Moustafa Youssef¹^,*

1: Alexandria University

*Contact email: nada_s_osman@alexu.edu.eg, ymustafa@alexu.edu.eg, moustafa@alexu.edu.eg

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).

Keywords: Network simulation Topology mapping Testbeds Genetic algorithm

Published: 2020-03-05
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-030-43215-7_5