基于DPSO负载可控的虚拟网络映射算法  

Load Controllable Virtual Network Embedding Algorithm Based on Discrete Particle Swarm Optimization

作　　者：苑迎[1,2] 王翠荣[2] 王聪[2] 史闻博[2] 

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110819 [2]东北大学秦皇岛分校计算机与通信工程学院,河北秦皇岛066004

出　　处：《东北大学学报（自然科学版）》2014年第1期10-14,共5页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(61202447;61300195);中央高校基本科研业务费专项资金资助项目(N110323009);辽宁省教育厅科学研究项目(L2013099)

摘　　要：针对多租赁模式下的虚拟网络映射问题,以降低底层链路负载、加快映射速度、提高底层物理资源利用率为目标,将离散粒子群算法与虚拟节点映射规则相结合,提出了物理节点可复用、负载可控制的MLB-VNE-SDPSO算法.该算法在兼顾CPU等主机资源利用率的前提下节约了物理链路的带宽资源,缩短了虚拟链路的映射过程.仿真实验表明,在保证网络负载的前提下,获得了较好的物理节点利用率,提高了虚拟网络的收益成本比.According to the multi-rental pattern over virtual network embedding problem in cloud computing, a resource allocation algorithm named MLB-VNE-SDPSO was proposed to reduce the substrate link load, speed up the mapping efficiency and increase the substrate physical resource utilization. The leveraging discrete binary particle swarm optimization algorithm was combined with the virtual network embedding rules in the proposed algorithm. Both CPU and host resources utilization ratio was taken into account, so the physical link bandwidth resource was saved and the time for virtual link mapping process was reduced. The major characteristic of the mapping algorithm was that repeatable mapping for each virtual network could be supported and the load of substrate node could be controlled. Simulation results showed that in the premise of guaranteeing substrate network load the algorithm can achieve better utilization ratio of substrate network and higher revenue-cost ratio of virtual networks.

关 键 词：网络虚拟化 映射算法 虚拟网络 整数线性规划 离散粒子群算法 

分 类 号：TP393.02[自动化与计算机技术—计算机应用技术]