Virtual Network Embedding Algorithm Based on Multi-objective Particle Swarm Optimization of Pareto Entropy

Virtual network embedding/mapping refers to the reasonable allocation of substrate network resources for users' virtual network requests, which is a key issue for virtual resource leasing in Cloud computing. Most of the existing researches only aim to maximize the revenue. As the scale of hardware network expands, the energy consumption of substrate network also needs to be paid more attention. In this paper, a multi-objective virtual network mapping algorithm based on particle swarm optimization with Pareto entropy (VNE-MOPSO) is proposed. It combines energy consumption and revenue. The algorithm controls the energy consumption of the substrate network as much as possible to achieve the goal of energy saving on the premise of ensuring a small resource cost. By introducing the Pareto entropy based multi-objective optimization model, it can calculate the difference of entropy and evaluate the evolutionary state. With this as feedback information, a dynamic adaptive particle velocity updating strategy is designed to achieve the goal of solving the approximate optimal multi-objective optimization mapping scheme. Simulation results show that the proposed algorithm has certain advantages over the typical single target mapping algorithm in cost, energy consumption and average return.