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Parallel Constraint-Based Local Search: An Application to Designing Resilient Long-Reach Passive Optical Networks

  • Alejandro Arbelaez
  • Deepak Mehta
  • Barry O’Sullivan
  • Luis Quesad
Chapter

Abstract

Many network design problems arising in areas as diverse as VLSI circuit design, QoS routing, traffic engineering, and computational sustainability require clients to be connected to a facility under path-length constraints and budget limits. These problems can be seen as instances of the Rooted Distance-Constrained Minimum Spanning-Tree problem (RDCMST), which is NP-hard. An inherent feature of these networks is that they are vulnerable to a failure. Therefore, it is often important to ensure that all clients are connected to two or more facilities via edge-disjoint paths.We call this problem the Edge-disjoint RDCMST (ERDCMST). Previous work on RDCMST has focused on dedicated algorithms and, therefore, it is difficult to use these algorithms to tackle ERDCMST. We present a constraint-based parallel local search algorithm for solving ERDCMST. Traditional ways of extending a sequential algorithm to run in parallel perform either portfolio-based search in parallel or parallel neighbourhood search. Instead, we exploit the semantics of the constraints of the problem to perform multiple moves in parallel by ensuring that they are mutually independent. The ideas presented in this chapter are general and can be adapted to other problems as well. The effectiveness of our approach is demonstrated by experimenting with a set of problem instances taken from real-world passive optical network deployments in Ireland, Italy, and the UK. Our results show that performing moves in parallel can significantly reduce the elapsed time and improve the quality of the solutions of our local search approach.

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Notes

Acknowledgments

This work was supported by DISCUS (FP7 Grant Agreement 318137), and Science Foundation Ireland (SF) Grant No. 10/CE/I1853. The Insight Centre for Data Analytics is also supported by SFI under Grant Number SFI/12/RC/2289.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Alejandro Arbelaez
    • 1
  • Deepak Mehta
    • 1
  • Barry O’Sullivan
    • 1
  • Luis Quesad
    • 1
  1. 1.Insight Centre for Data AnalyticsUniversity College CorkCorkIreland

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