计算机应用 ›› 2021, Vol. 41 ›› Issue (4): 1160-1164.DOI: 10.11772/j.issn.1001-9081.2020060845

所属专题: 网络与通信

• 网络与通信 • 上一篇    下一篇

基于软件定义网络的数据中心自适应多路径负载均衡算法

许红亮, 杨桂芹, 蒋占军   

  1. 兰州交通大学 电子与信息工程学院, 兰州 730070
  • 收稿日期:2020-06-18 修回日期:2020-11-09 出版日期:2021-04-10 发布日期:2021-01-05
  • 通讯作者: 杨桂芹
  • 作者简介:许红亮(1996—),女,甘肃定西人,硕士研究生,主要研究方向:软件定义网络、负载均衡;杨桂芹(1970—),女,河南许昌人,教授,硕士,主要研究方向:现代信号处理理论、现代通信理论;蒋占军(1975—),男,宁夏中卫人,教授,博士,主要研究方向:移动通信。
  • 基金资助:
    甘肃省高原交通信息工程及控制重点实验室开放课题(20161106);兰州交通大学“百名青年优秀人才培养计划”资助项目(150220232)。

Data center adaptive multi-path load balancing algorithm based on software defined network

XU Hongliang, YANG Guiqin, JIANG Zhanjun   

  1. School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou Gansu 730070, China
  • Received:2020-06-18 Revised:2020-11-09 Online:2021-04-10 Published:2021-01-05
  • Supported by:
    This work is partially supported by the Gansu Provincial Open Project of the Key Laboratory of Plateau Traffic Information Engineering and Control (20161106), the Foundation of Hundred Excellent Young Talents Training Program of Lanzhou Jiaotong University (150220232).

摘要: 针对传统多路径负载均衡算法无法有效地感知网络的运行状态、不能综合考虑链路的实时传输状态以及大多数算法缺少自适应性的问题,基于软件定义网络(SDN)的集中控制和全网管控思想,提出一种基于蜘蛛猴优化的SDN自适应多路径负载均衡算法(SMO-LBA)。首先,利用数据中心网络的感知能力来获取多路径的实时链路状态信息;然后,利用蜘蛛猴算法的全局探索和局部开采能力将链路空闲率作为每条路径的适应度值,并引入自适应权重对路径进行动态评估及更新;最后,寻找数据中心网络中链路占用率最小的路径,确定其为最优转发路径。选用胖树拓扑在Mininet平台上进行仿真实验,实验结果表明SMO-LBA可提高数据中心网络的吞吐量和平均链路利用率,实现网络自适应负载均衡。

关键词: 软件定义网络, 多路径负载均衡, 蜘蛛猴优化算法, 胖树, Mininet

Abstract: The traditional multi-path load balancing algorithms cannot effectively perceive the running state of the network, cannot comprehensively consider the real-time transmission states of the links and most of them lack adaptability. In order to solve these problems, a Software Defined Network(SDN) adaptive multi-path Load Balancing Algorithm based on Spider Monkey Optimization(SMO-LBA) was proposed based on the idea of centralized control and whole network control of SDN. Firstly, the perceptul ability of data center network was used to obtain the multi-path real-time link state information. Then, based on the global exploration and local exploitation ability of spider monkey optimization algorithm, the link idle rate was used as the adaptability value of each path, and the paths were dynamically evaluated and updated by introducing the adaptive weight. Finally, the path with the lowest link occupancy rate in data center network was determined as the optimal forwarding path. The fat tree topology was selected to carry out the simulation experiment on Mininet platform. Experimental results show that SMO-LBA can improve the throughput and average link utilization of data center network, and realize the adaptive load balancing of the network.

Key words: Software Defined Network (SDN), multi-path load balancing, Spider Monkey Optimization (SMO) algorithm, fat tree, Mininet

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