A Knapsack-based Optimization Algorithm for VNF Placement and Chaining Problem

During the last decade, we are witnessing the emergence of NFV and SDN to reduce CAPEX and OPEX. Under the SDN paradigm and thanks to NFV, a service can be swiftly deployed by the chaining of several VNFs forming an SFC running on a virtualized infrastructure. Nowadays, there are still quite a number of issues related to SFCs, among them, the optimal placement of SFC components. In this paper, we focused on the variant of the resource allocation cost optimization problem of VNF placement and chaining for limited resources on the servers. After proving that the problem of VNF placement is NP-Hard and equivalent to the multiple knapsack problem, we proposed a genetic algorithm-based meta-heuristic to solve large instance of our VNF placement and chaining problem variant. Simulation results show that our genetic algorithms are efficient since they reduce the SFC mean cost and improve the accepted requests ratio.