系统仿真学报 ›› 2020, Vol. 32 ›› Issue (8): 1436-1445.doi: 10.16182/j.issn1004731x.joss.18-0843

• 仿真建模理论与方法 • 上一篇    下一篇

面向网络仿真拓扑的多目标优化映射方法

王晓锋, 陈阳, 张光杰, 陈建宇   

  1. 江南大学 物联网工程学院,江苏 无锡 214122
  • 收稿日期:2018-12-18 修回日期:2019-03-04 出版日期:2020-08-18 发布日期:2020-08-13
  • 作者简介:王晓锋(1978-), 男, 江苏无锡, 博士, 副教授, 研究方向为网络安全, 网络建模与模拟; 陈阳(1995-), 女, 吉林松原, 硕士生, 研究方向为网络仿真。
  • 基金资助:
    国家自然科学基金(61672264),国家重点研发计划(2016YFB0800801)

Multi-objective Topology Mapping Method for Network Emulation

Wang Xiaofeng, Chen Yang, Zhang Guangjie, Chen Jianyu   

  1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
  • Received:2018-12-18 Revised:2019-03-04 Online:2020-08-18 Published:2020-08-13

摘要: 网络仿真是新型网络技术验证的重要支撑,针对给定的网络仿真拓扑,实现有效映射是其关键。综合考虑多种资源需求,提出了多目标优化映射方法MOTM (Multi-Objective Topology Mapping Method),实现物理资源的有效利用。该方法分析网络中节点、链路资源需求,赋予相应权值;将映射问题转化为图划分问题,采用多级图划分方法进行划分,并通过远程吞吐量阈值优化调整;最后,基于映射策略实现了仿真拓扑的自动部署。实验表明,MOTM相对于Openstack映射方法、随机映射方法,负载不均衡指数平均降低66.5%,95.5%,远程通信开销指数平均降低69.1%,65.2%。

关键词: 网络仿真, 拓扑映射, Openstack, 多目标优化

Abstract: Network emulation is an important support for the verification of new network technology, and effective mapping is the key to the emulation network topology. Considering the multiple resource requirements, a multi-objective topology mapping method (MOTM) for network emulation topology is proposed to realize the effective physical resources utilization. The method analyzes the resource requirements of nodes and links, assigns corresponding weights, converts the mapping problem into the graph partitioning problem, divides the graph by multi-level graph partitioning method, and forms a mapping strategy through remote throughput threshold optimization adjustment. The automatic deployment is implemented based on the mapping strategy. Experiments show that, compared with the Openstack mapping method and the random mapping method, MOTM reduces the load imbalance index by 66.5% and 95.5%, and the telecommunication overhead index by 69.1% and 65.2%.

Key words: network emulation, topology mapping, Openstack, multi-objective optimization

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