通信学报 ›› 2012, Vol. 33 ›› Issue (Z1): 170-177.doi: 10.3969/j.issn.1000-436x.2012.z1.022

• 学术论文 • 上一篇    下一篇

VNE-AFS:基于人工鱼群的网络虚拟化映射算法

朱强,王慧强,吕宏武,王振东   

  1. 哈尔滨工程大学 计算机科学与技术学院,黑龙江 哈尔滨 150001
  • 出版日期:2012-09-25 发布日期:2017-08-03
  • 基金资助:
    国家自然科学基金资助项目;教育部高等学校博士点基金资助项目;中央高校基本科研业务费专项资金项目;中央高校基本科研业务费专项资金项目;黑龙江省自然科学基金资助项目;黑龙江省自然科学基金资助项目

VNE-AFS:virtual network embedding based on artificial fish swarm

Qiang ZHU,Hui-qiang WANG,Hong-wu LV,Zhen-dong WANG   

  1. College of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China
  • Online:2012-09-25 Published:2017-08-03
  • Supported by:
    The National Natural Science Foundation of China;The Research Foundation for the Doctoral Program of Higher Education of China;The Fundamental Research Funds for the Central Universities;The Fundamental Research Funds for the Central Universities;The Natural Science Foundation of Heilongjiang Province;The Natural Science Foundation of Heilongjiang Province

摘要:

虚拟网络资源映射是云计算研究领域的一个难点问题。以降低底层网络映射开销为目标,提出一种基于人工鱼群的网络虚拟化映射算法VNE-AFS。根据虚拟网络请求对底层网络节点和链路的约束关系建立二进制组合优化模型,并利用人工鱼群算法实现虚拟网络资源向底层网络资源的近似最优映射。实验结果表明,与现有的虚拟网络映射算法相比,该算法有效地降低了底层网络的开销和求解时间,提高了虚拟网络映射的成功率、平均收益和资源利用率。

关键词: 云计算, 网络虚拟化, 网络虚拟化映射, 二进制组合优化, 人工鱼群

Abstract:

Recently virtual network embedding problem had been proposed as a research challenge in the cloud computing environment.In order to reduce the costs,a virtual network embedding algorithms based on artificial fish swarm(VNE-AFS)was proposed.A binary combinatorial optimization model was built according to the constraints on nodes and links between virtual network and substrate network,and the artificial fish swarm algorithm was used to achieve the approximate optimal mapping.The simulation results indicate that the costs of substrate network and computation time are reduced and the success rate,average revenue of embedding and average usage of links are increased compared with the existing virtual network embedding algorithms.

Key words: cloud computing, network virtualization, network virtualization embedding, binary combinatorial optimization, artificial fish swarm

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