4th International IEEE Conference on Broadband Communications, Networks, Systems

Research Article

Cost-Effective Heuristics for Planning GMPLS Transport Networks

  • @INPROCEEDINGS{10.1109/BROADNETS.2007.4550457,
        author={Nabil Naas and H. T. Mouftah},
        title={Cost-Effective Heuristics for Planning GMPLS Transport Networks},
        proceedings={4th International IEEE Conference on Broadband Communications, Networks, Systems},
        publisher={IEEE},
        proceedings_a={BROADNETS},
        year={2010},
        month={5},
        keywords={GMPLS; WDM;Transport network planning; Multigranular crossconnect; RMGPA problem; Heuristic optimization},
        doi={10.1109/BROADNETS.2007.4550457}
    }
    
  • Nabil Naas
    H. T. Mouftah
    Year: 2010
    Cost-Effective Heuristics for Planning GMPLS Transport Networks
    BROADNETS
    IEEE
    DOI: 10.1109/BROADNETS.2007.4550457
Nabil Naas1,*, H. T. Mouftah1,*
  • 1: School of Information Technology and Engineering University of Ottawa, Ottawa, Canada
*Contact email: nnaas@site.uottawa.ca, mouftah@site.uottawa.ca

Abstract

With the explosive traffic growth of WDM-based transport networks, the development of GMPLS (or multigranularity)- based transport networks becomes essential to cope with the network scalability problems. Much work has been devoted to the development of Multi-Granular Crossconnect (MG-XC) architectures and planning (or dimensioning) methods. Extending these efforts here, we are defining a novel problem of planning GMPLS-based transport networks by (1) considering the whole traffic hierarchy defined in GMPLS; (2) allowing bifurcation of multi-granularity traffic demands among different physical routes. We will call such a problem the Routing and Multi-Granular Paths Assignment (RMGPA). The objective of the problem is to minimize the total weighted node port count. Due to the computational complexity of the problem, only verysmall- sized problems can be solved exactly through Mixed Integer Linear Programming (MILP) optimization. In this paper, we propose novel heuristics that are capable of solving large-sized problems in a reasonable amount of time.