D-ViNE: Dynamic Virtual Network Embedding in Non-Terrestrial Networks

Maity, Ilora; Vu, Thang X.; Chatzinotas, Symeon; Minardi, Mario

doi:10.1109/wcnc51071.2022.9771560

Cited by 20 publications

(1 citation statement)

References 13 publications

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“…Much of the existing studies on SGIN concentrate on the deployment and reassignment of SFCs [5][6] [7], However, simple remapping will lead to frequent redeployment of SFCs on the network, which will consume a lot of resources [8]. Obviously, VNF migration is better suited for this situation, and based on our understanding, there is no research on VNF migration on SGIN networks.…”

Section: Introductionmentioning

confidence: 99%

Cost-aware service function chain migration in satellite-ground integrated networks

Fu,

Ran,

2024

Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024)

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Section: Introductionmentioning

confidence: 99%

Cost-aware service function chain migration in satellite-ground integrated networks

Fu,

Ran,

2024

Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024)

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Novel Initialization Functions for Metaheuristic-Based Online Virtual Network Embedding

Rubio-Loyola,

Aguilar-Fuster

2024

J Netw Syst Manage

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Virtual network embedding (VNE) is the process of allocating resources in a substrate (i.e. physical) network to support virtual networks optimally. The VNE problem is an NP-hard problem that has been studied for more than a decade in the continuous seek to maximize the revenue of physical infrastructures with more efficient VNE solutions. Metaheuristics have been widely used in online VNE as they incorporate mechanisms to avoid local optimum solutions, explore larger search spaces, and keep acceptable execution times. All metaheuristic optimization algorithms require initialization for which the vast majority of online VNE solutions implement random initialization. This paper proposes three novel initialization functions namely, Initialization Based on Node Selection (IFNS), Initialization Function Based on Community Detection (IFCD), and Initialization Function Based on Previous Solutions (IFPS), intending to enhance the performance of the online VNE process. Through simulation, our initialization functions have been proven to enhance the acceptance rate, revenue, and revenue-to-cost metrics of the VNE process. The enhancements achieved by our initialization functions are statistically significant and their implementation does not add computational overhead to the classic VNE approaches.

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