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A Fast, Scalable Meta-Heuristic for Network Slicing Under Traffic Uncertainty

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Applications of Evolutionary Computation (EvoApplications 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12104))

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Abstract

Perceived to be one of the cornerstones of the emerging next generation (5G) networks, Network slicing enables the accommodation of multiple logical networks with diverse performance requirements on a common substrate platform. Of particular interest among different facets of network slicing is the problem of designing an individual network slice tailored specifically to match the requirements of the big-bandwidth next generation network services. In this work, we present an exact formulation for the network slice design problem under traffic uncertainty. As the considered mathematical formulation is known to pose a high degree of computational difficulty to state-of-the-art commercial mixed integer programming solvers owing to the inclusion of robust constraints, we propose a meta-heuristic based on ant colony optimisation algorithms for the robust network slice design problem. Experimental evaluation conducted on realistic network topologies from SNDlib reveals that the proposed meta-heuristic can indeed be an efficient alternative to the commercial mixed integer programming solvers.

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Notes

  1. 1.

    As large coefficients are known to pose some problems at various stages of the solution process in CPLEX, the capacities and demands were scaled down by a factor of 1 Gbps, and the costs by EUR 1000.

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Authors and Affiliations

  1. Chair of Communication Networks, Technische Universität Chemnitz, 09126, Chemnitz, Germany

    Thomas Bauschert & Varun S. Reddy

Authors
  1. Thomas Bauschert
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  2. Varun S. Reddy
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Correspondence to Thomas Bauschert .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Pedro A. Castillo

  2. Université Le Havre Normandie, Le Havre, France

    Juan Luis Jiménez Laredo

  3. Universidad de Extremadura, Mérida, Spain

    Francisco Fernández de Vega

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Bauschert, T., Reddy, V.S. (2020). A Fast, Scalable Meta-Heuristic for Network Slicing Under Traffic Uncertainty. In: Castillo, P.A., Jiménez Laredo, J.L., Fernández de Vega, F. (eds) Applications of Evolutionary Computation. EvoApplications 2020. Lecture Notes in Computer Science(), vol 12104. Springer, Cham. https://doi.org/10.1007/978-3-030-43722-0_16

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  • DOI: https://doi.org/10.1007/978-3-030-43722-0_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43721-3

  • Online ISBN: 978-3-030-43722-0

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