东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2014, Vol. 35 ›› Issue (1): 10-13.DOI: 10.12068/j.issn.1005-3026.2014.01.003

• 信息与控制 • 上一篇    下一篇

基于DPSO负载可控的虚拟网络映射算法

苑迎1,2，王翠荣2，王聪2，史闻博2   

  1. (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)
  • 收稿日期:2013-02-06 修回日期:2013-02-06 出版日期:2014-01-15 发布日期:2013-07-09
  • 通讯作者: 苑迎
  • 作者简介:苑迎(1981-)，女，河北秦皇岛人，东北大学讲师，博士研究生；王翠荣(1963-)，女，河北迁安人，东北大学教授.
  • 基金资助:
    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).

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

YUAN Ying1,2， WANG Cuirong2， WANG Cong2， SHI Wenbo2   

  1. 1. School of Information Science & Engineering， Northeastern University， Shenyang 110819， China; 2. School of Computer and Communication Engineering， Northeastern University at Qinhuangdao， Qinhuangdao 066004， China.
  • Received:2013-02-06 Revised:2013-02-06 Online:2014-01-15 Published:2013-07-09
  • Contact: YUAN Ying
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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.

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

Abstract: 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.

Key words: network virtualization, embedding algorithm, virtual network, integerlinear programming, discrete particle swarm optimization

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