Research Article
Virtual Network Embedding Algorithm Based on Multi-objective Particle Swarm Optimization of Pareto Entropy
@INPROCEEDINGS{10.1007/978-3-030-36442-7_5, author={Ying Liu and Cong Wang and Ying Yuan and Guo-jia Jiang and Ke-zhen Liu and Cui-rong Wang}, title={Virtual Network Embedding Algorithm Based on Multi-objective Particle Swarm Optimization of Pareto Entropy}, proceedings={Broadband Communications, Networks, and Systems. 10th EAI International Conference, Broadnets 2019, Xi’an, China, October 27-28, 2019, Proceedings}, proceedings_a={BROADNETS}, year={2019}, month={12}, keywords={Virtual network embedding Multi-objective optimization Discrete particle swarm optimization Pareto entropy}, doi={10.1007/978-3-030-36442-7_5} }- Ying Liu
Cong Wang
Ying Yuan
Guo-jia Jiang
Ke-zhen Liu
Cui-rong Wang
Year: 2019
Virtual Network Embedding Algorithm Based on Multi-objective Particle Swarm Optimization of Pareto Entropy
BROADNETS
Springer
DOI: 10.1007/978-3-030-36442-7_5
Abstract
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.
