Skip to Main content Skip to Navigation
Journal articles

Virtual network function-forwarding graph embedding: A genetic algorithm approach

Abstract : Network Function Virtualization (NFV) provides a simple and effective mean to deploy and manage network and telecommu-nications' services. A typical service can be expressed in the form of a Virtual Network Function-Forwarding Graph (VNF-FG). Allocating a VNF-FG is equivalent to place VNFs and virtual links onto a given substrate network considering resources and quality of service (QoS) constraints. The deployment of VNF-FGs in large-scale networks, such that QoS measures and deployment cost are optimized, is an emerging challenge. Single-objective VNF-FGs allocation has been addressed in existing literature; however, there is still a lack of studies considering multi-objective VNF-FGs allocation. In addition, it is not trivial to obtain optimal VNF-FGs allocation due to its high computational complexity even in case of single-objective VNF-FGs allocation. Genetic algorithms (GAs) have been proved its ability in coping with multi-objective optimization problems, thus we propose a GA-based scheme to solve multi-objective VNF-FGs allocation problem in this paper. The numerical results confirm that the proposed scheme can provide near Pareto-optimal solutions within a short execution time.
Document type :
Journal articles
Complete list of metadata

Cited literature [32 references]  Display  Hide  Download
Contributor : Yassine Hadjadj Aoul Connect in order to contact the contributor
Submitted on : Saturday, January 4, 2020 - 5:35:23 PM
Last modification on : Monday, April 4, 2022 - 9:28:24 AM
Long-term archiving on: : Monday, April 6, 2020 - 5:27:41 PM


Files produced by the author(s)



Quang Tran Anh Pham, Jean-Michel Sanner, Cédric Morin, yassine Hadjadj-Aoul. Virtual network function-forwarding graph embedding: A genetic algorithm approach. International Journal of Communication Systems, Wiley, 2019, pp.e4098. ⟨10.1002/dac.4098⟩. ⟨hal-02427993⟩



Record views


Files downloads