Mobile Networks and Management. 5th International Conference, MONAMI 2013, Cork, Ireland, September 23-25, 2013, Revised Selected Papers

Research Article

Enhancing Path Selection in Multihomed Nodes

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  • @INPROCEEDINGS{10.1007/978-3-319-04277-0_6,
        author={Bruno Sousa and Kostas Pentikousis and Marilia Curado},
        title={Enhancing Path Selection in Multihomed Nodes},
        proceedings={Mobile Networks and Management. 5th International Conference, MONAMI 2013, Cork, Ireland, September 23-25, 2013, Revised Selected Papers},
        proceedings_a={MONAMI},
        year={2014},
        month={6},
        keywords={MADM DoE TOPSIS path selection multihoming evaluation},
        doi={10.1007/978-3-319-04277-0_6}
    }
    
  • Bruno Sousa
    Kostas Pentikousis
    Marilia Curado
    Year: 2014
    Enhancing Path Selection in Multihomed Nodes
    MONAMI
    Springer
    DOI: 10.1007/978-3-319-04277-0_6
Bruno Sousa1,*, Kostas Pentikousis2,*, Marilia Curado1,*
  • 1: University of Coimbra
  • 2: Huawei Technologies
*Contact email: bmsousa@dei.uc.pt, k.pentikousis@huawei.com, marilia@dei.uc.pt

Abstract

Path selection in multihomed nodes can be enhanced by optimization techniques that consider multiple criteria. With NP-Hard problems, MADM techniques have the flexibility of including any number of benefits or costs criteria and are open regarding the functions that can be employed to normalize data or to determine distances. TOPSIS uses the Euclidean distance (straight line) while DiA employs the Manhattan distance (grid-based) to determine the distance of each path to ideal values. MADM techniques have been employed in distinct areas, as well. Such openness and flexibility may lead to sub-optimal path selection, as their optimality is associated with functions that determine distance as a straight line or as grid path, and not inside an ideal range determined by the type of criteria. In this paper we propose the MeTH distance which considers the type of criteria, whether benefits or costs. In addition, we establish a MADM evaluation methodology based on statistical analysis that enables an objective comparison between MADM mechanisms and respective functions for path selection. With the proposed MADM evaluation methodology, we demonstrate that our MeTH distance is more efficient for the path selection problem than Euclidean and Manhattan distances.