ICC 2020 - 2020 IEEE International Conference on Communications (ICC) 2020
DOI: 10.1109/icc40277.2020.9148914
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A metaheuristic approach for minimizing service creation time in slice-enabled networks

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Cited by 5 publications
(3 citation statements)
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“…9, it is clear that our proposed algorithm outperforms other competitors on all test cases. Although the binary solution representation has been successfully applied to solve VNFPP on small data centers (e.g., [81,82,98]), it does not scale well in the larger-scale problems considered in our experiments. Likewise, the direct solution representation is only able to obtain feasible solutions to small data centers, as shown in Fig.…”
Section: Resultsmentioning
confidence: 97%
“…9, it is clear that our proposed algorithm outperforms other competitors on all test cases. Although the binary solution representation has been successfully applied to solve VNFPP on small data centers (e.g., [81,82,98]), it does not scale well in the larger-scale problems considered in our experiments. Likewise, the direct solution representation is only able to obtain feasible solutions to small data centers, as shown in Fig.…”
Section: Resultsmentioning
confidence: 97%
“…A factor we had not included in our previous work [15], was whether the characteristics of the problem play some role in choosing the best parameters in each algorithm. To fill that blank, we run GA and ACO for a 20 × 5 JSS problem.…”
Section: A Numerical Resultsmentioning
confidence: 99%
“…More precisely, we consider a system that receives requests for setting up network slices and provides scheduling decisions based on nature-inspired metaheuristics. Expanding our previous work on metaheuristics [15], the evolution-based and the swarm-based metaheuristic categories are targeted through two key representatives, namely: The genetic algorithm (GA) and the ant colony optimisation (ACO), respectively. For each one of the approaches, we study thoroughly the impact of configuration parameters on their performance in order to select the best-fitting solution for the VNF/CNF scheduling problem.…”
Section: Introductionmentioning
confidence: 99%