基于混合群智能优化的虚拟网络映射算法

网络虚拟化是突破网络发展僵局的一项重要技术，而虚拟网络映射(VNE)是网络虚拟化的一个主要问题。提高底层网络资源的利用率和收益是虚拟网络映射的主要目标。针对底层网络支持路径分裂的情况，建立了整数线性规划(ILP)模型，并提出基于混合群智能优化的虚拟网络映射算法。该算法在兼顾映射开销和映射均衡性的基础上利用粒子群优化算法(PSO)和遗传算法(GA)迭代优化映射方案。仿真实验结果表明，与现有的主流研究成果相比，该算法显著地提高了底层网络长期平均运营收益与虚拟网络请求接受率。

Virtual network embedding algorithm based on a hybrid swarm intelligence optimization

Network virtualization is recognized as a significant technology to solve the ossification of current Internet. Virtual Network Embedding (VNE) is a major challenge in network virtualization. The main object of VNE is to increase the acceptance ratio of Virtual Network (VN) and the revenue of infrastructure providers. Regarding VNE as an Integer Linear Programming (ILP) model with an assumption that substrate network needs to support path splitting, a new VNE algorithm based on hybrid swarm intelligence optimization was proposed. The proposed algorithm took advantage of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to optimize the mapping scheme in view of the balance of the mapping overhead and the mapping proportionality. Compared with the existing mainstream approaches, the simulation results demonstrate that the proposed algorithm can increase the long-term average revenue and acceptance ratio.