  Assigning virtual network resources to physical network components, called
Virtual Network Embedding, is a major challenge in cloud computing platforms.
In this paper, we propose a memetic elitist pareto evolutionary algorithm for
virtual network embedding problem, which is called MEPE-VNE. MEPE-VNE applies a
non-dominated sorting-based multi-objective evolutionary algorithm, called
NSGA-II, to reduce computational complexity of constructing a hierarchy of
non-dominated Pareto fronts and assign a rank value to each virtual network
embedding solution based on its dominance level and crowding distance value.
Local search is applied to enhance virtual network embedding solutions and
speed up convergence of the proposed algorithm. To reduce loss of good
solutions, MEPE-VNE ensures 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
extensive simulations, which show that the proposed algorithm improves virtual
network embedding by increasing acceptance ratio and revenue while decreasing
the cost incurred by substrate network.
