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

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