Abstract

Assigning virtual network resources to physical network components, called Virtual Network Embedding, is a majorchallenge in cloud computing platforms. In this paper, we propose a memetic elitist pareto evolutionary algorithmfor virtual network embedding problem, which is called MEPE-VNE. MEPE-VNE applies a non-dominated sortingbasedmulti-objective evolutionary algorithm, called NSGA-II, to reduce computational complexity of constructinga hierarchy of non-dominated Pareto fronts and assign a rank value to each virtual network embedding solutionbased on its dominance level and crowding distance value. Local search is applied to enhance virtual networkembedding solutions and speed up convergence of the proposed algorithm. To reduce loss of good solutions, MEPEVNEensures elitism by passing virtual network embedding solutions with best fitness values to next generation.Performance of the proposed algorithm is evaluated and compared with existing algorithms using extensivesimulations, which show that the proposed algorithm improves virtual network embedding by increasing acceptanceratio and revenue while decreasing the cost incurred by substrate network.

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