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Assigning virtual network resources to physical network components, calledVirtual Network Embedding, is a major challenge in cloud computing platforms.In this paper, we propose a memetic elitist pareto evolutionary algorithm forvirtual network embedding problem, which is called MEPE-VNE. MEPE-VNE applies anon-dominated sorting-based multi-objective evolutionary algorithm, calledNSGA-II, to reduce computational complexity of constructing a hierarchy ofnon-dominated Pareto fronts and assign a rank value to each virtual networkembedding solution based on its dominance level and crowding distance value.Local search is applied to enhance virtual network embedding solutions andspeed up convergence of the proposed algorithm. To reduce loss of goodsolutions, MEPE-VNE ensures elitism by passing virtual network embeddingsolutions with best fitness values to next generation. Performance of theproposed algorithm is evaluated and compared with existing algorithms usingextensive simulations, which show that the proposed algorithm improves virtualnetwork embedding by increasing acceptance ratio and revenue while decreasingthe cost incurred by substrate network.

Memetic Elitist Pareto Evolutionary Algorithm for Virtual Network Embedding