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Genetic algorithms for the network slice design problem under uncertainty

Published: 13 July 2019 Publication History

Abstract

Robust optimisation, in recent years, has surfaced as an essential technique to handle data uncertainty in mathematical programming models. However, the resulting robust counterparts are often hard to solve even for modern state-of-the-art Mixed Integer Programming solvers, underlining the need for approximate algorithms. Based on the works of Gonçalves and Resende [3], we propose genetic algorithms for the network slice design problem (NSDP) under uncertainty. We investigate the performance of the proposed algorithms using realistic datasets from SNDlib [4].

References

[1]
A. Baumgartner, T. Bauschert, A. M. C. A. Koster, and V. S. Reddy. 2017. Optimisation Models for Robust and Survivable Network Slice Design: A Comparative Analysis. In GLOBECOM 2017 - 2017 IEEE Global Communications Conference. 1--7.
[2]
Christina Büsing and Fabio D'Andreagiovanni. 2012. New Results about Multi-band Uncertainty in Robust Optimization. In Experimental Algorithms, Ralf Klasing (Ed.). Springer Berlin Heidelberg, Berlin, Heidelberg, 63--74.
[3]
José Fernando Gonçalves and Mauricio G. C. Resende. 2011. Biased random-key genetic algorithms forÂăcombinatorial optimization. Journal of Heuristics 17, 5 (01 Oct 2011), 487--525.
[4]
S. Orlowski, R. Wessäly, M. Pióro, and A. Tomaszewski. 2010. SNDlib 1.0-Survivable Network Design Library. Networks 55, 3 (2010), 276--286.
[5]
Matthias Rost and Stefan Schmid. 2016. Service Chain and Virtual Network Embeddings: Approximations using Randomized Rounding. CoRR abs/1604.02180 (2016).

Cited By

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  • (2020)A Fast, Scalable Meta-Heuristic for Network Slicing Under Traffic UncertaintyApplications of Evolutionary Computation10.1007/978-3-030-43722-0_16(244-259)Online publication date: 9-Apr-2020

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cover image ACM Conferences
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2019
2161 pages
ISBN:9781450367486
DOI:10.1145/3319619
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2019

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Author Tags

  1. combinatorial optimisation
  2. genetic algorithms
  3. network optimisation
  4. network slicing
  5. robust optimisation

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  • Research-article

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GECCO '19
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GECCO '19: Genetic and Evolutionary Computation Conference
July 13 - 17, 2019
Prague, Czech Republic

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2020)A Fast, Scalable Meta-Heuristic for Network Slicing Under Traffic UncertaintyApplications of Evolutionary Computation10.1007/978-3-030-43722-0_16(244-259)Online publication date: 9-Apr-2020

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