A metaheuristic approach for minimizing service creation time in slice-enabled networks

Charismiadis, Anastasios-Stavros; Tsolkas, Dimitris; Passas, Nikos; Merakos, Lazaros

doi:10.1109/icc40277.2020.9148914

Cited by 5 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

Evolutionary Multi-Objective Virtual Network Function Placement: A Formal Model and Effective Algorithms

Billingsley¹,

Li²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

Data centers are critical to the commercial and social activities of modern society but are also major electricity consumers. To minimize their environmental impact, it is imperative to make data centers more energy efficient while maintaining a high quality of service (QoS). Bearing this consideration in mind, we develop an analytical model using queueing theory for evaluating the QoS of a data center. Furthermore, based on this model, we develop a domain-specific evolutionary optimization framework featuring a tailored solution representation and a constraint-aware initialization operator for finding the optimal placement of virtual network functions in a data center that optimizes multiple conflicting objectives with regard to energy consumption and QoS. In particular, our framework is applicable to any existing evolutionary multi-objective optimization algorithm in a plug-in manner. Extensive experiments validate the efficiency and accuracy of our QoS model as well as the effectiveness of our tailored algorithms for virtual network function placement problems at various scales.

show abstract

Section: Resultsmentioning

confidence: 97%

Evolutionary Multi-Objective Virtual Network Function Placement: A Formal Model and Effective Algorithms

Billingsley¹,

Li²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…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%