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

International audienceNetwork 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