基于差分进化融合蚁群算法的数据中心流量调度机制

针对数据中心网络的传统流量调度方法容易引起网络拥塞及链路负载不均衡等问题，提出了一种差分进化（DE）融合蚁群（ACO）算法（DE-ACO）的动态流量调度机制，对数据中心网络中的大象流调度进行优化。首先，利用软件定义网络（SDN）技术捕获实时网络状态信息并设定流量调度的优化目标；然后，通过优化目标重定义DE算法，计算出多条可用候选路径，作为ACO算法的初始化全局信息素；最后，结合全局网络状态以求得全局最优路径，并重新路由拥堵链路上的大象流。实验结果表明，以在随机通信模式下为例，与等价多路径路由（ECMP）算法和基于蚁群算法的SDN数据中心网络流量调度（ACO-SDN）算法相比，所提算法的平均对分带宽分别提高了29.42%～36.26%和5%～11.51%，降低了网络的最大链路利用率（MLU），较好地实现了网络负载均衡。

Data center flow scheduling mechanism based on differential evolution and ant colony optimization algorithm

As the traditional flow scheduling method for data center network is easy to cause network congestion and link load imbalance， a dynamic flow scheduling mechanism based on Differential Evolution （DE） and Ant Colony Optimization （ACO） algorithm （DE-ACO） was proposed to optimize elephant flow scheduling in data center networks. Firstly， Software Defined Network （SDN） technology was used to capture the real-time network status information and set the optimization objectives of flow scheduling. Then， DE algorithm was redefined by the optimization objectives， several available candidate paths were calculated and used as the initialized global pheromone of the ACO algorithm. Finally， the global optimal path was obtained by combining with the global network status， and the elephant flow on the congested link was rerouted. Experimental results show that compared with Equal-Cost Multi-Path routing （ECMP） algorithm and network flow scheduling algorithm of SDN data center based on ACO algorithm （ACO-SDN）， the proposed algorithm increases the average bisection bandwidth by 29.42% to 36.26% and 5% to 11.51% respectively in random communication mode， reducing the Maximum Link Utilization （MLU） of the network， and achieving better load balancing of the network.