基于软件定义网络的对等网传输调度优化

针对对等网（P2P）系统中的应用层组播（ALM）流量优化问题，设计了一个基于软件定义网络（SDN）的实时流调度系统。首先使用网络测量技术获取网络的流量矩阵，然后将它抽象成一张带权重的网络状态图提供给终端优先组播树（TFST）生成算法。TFST生成算法分两阶段进行：第一阶段计算组播树时通过修改终端节点的距离为0来巧妙地引导生成算法优先考虑终端节点；第二阶段是根据设定的权衡因子对分支节点数量进行调整，这样计算出的组播树能同时兼顾流量代价和实施代价。最后为避免组播树部署到网络中时频繁的流表更新带来的网络性能下降问题，还设计了一个基于循环神经网络的模块来根据网络性能自动调整更新周期。仿真结果表明采用了ALM实时流调度系统的网络拥塞指标与原始网络相比下降了47%，在中等负载情况下，利用神经网络模块自动调整更新周期方式与立即更新和固定5 s间隔更新方式相比，拥塞指标的均值分别降低了17.6%和25%，在将机器学习引入SDN实现智能化网络方面具有较大的应用价值。

P2P transmission scheduling optimization based on software defined network

To solve the traffic optimization problem of Application Layer Multicast (ALM) in Peer-to-Peer (P2P) system, a real-time flow scheduling system based on Software Defined Network (SDN) was designed. Firstly, some network measurement technologies were used to obtain the traffic matrix of the network, which was then abstracted into a weighted network state diagram and provided to the Terminal First Steiner Tree (TFST) generation algorithm. The TFST generation algorithm was divided into two stages. When generating multicast tree in the first stage, the algorithm was dexterously made to give higher priority to the terminal nodes by modifying the distance of terminal nodes to zero. In the second stage, the amount of branch nodes was adjusted according to the preset weighting factor so that the calculated multicast tree was able to give consideration to both traffic cost and implementation cost. Finally, to prevent the degradation of network performance caused by the too frequent updates of flow table when deploying the multicast tree into the network, a recurrent neural network-based module was designed to automatically adjust the update cycle according to the network performance. The simulation results indicate that the congestion index of the network using ALM real-time flow scheduling system is reduced by 47% compared with that of the original network. In addition, under medium load, the average value of the congestion index of the method with neural network module used to automatically adjust the update cycle is reduced by 17.6% and 25% respectively compared with those of the immediate update and fixed 5-second interval update methods. It is seen that the design has great practical application value in introducing machine learning into SDN to realize intelligent network.