Software-defined network provides a greater solution to many of the complex network management functionalities in a data center network (DCN). In a software-defined data center network, it is essential to manage heavy traffic, to avoid data loss, and to avoid server unavailability during heavy flows. One of the major tasks in a network is the load balancing in the available links. Due to the dynamic data traffic in the network, it is necessary to perform a deep learning of the long-term and the short-term data. Hence, this paper proposes an intelligent load balancing scenario that includes link load balancing, server load balancing, and traffic classification. An artificial neural network is needed because the data are uncertain all the time. Thus, our method provides improved throughput, minimized the flow completion time, and minimizes data loss.

软件定义的网络为数据中心网络（DCN）中的许多复杂的网络管理功能提供了更好的解决方案。在软件定义的数据中心网络中，至关重要的是管理大流量，避免数据丢失以及避免大流量期间服务器不可用。网络中的主要任务之一是可用链路中的负载平衡。由于网络中动态的数据流量，有必要对长期和短期数据进行深度学习。因此，本文提出了一种智能的负载均衡方案，该方案包括链路负载均衡，服务器负载均衡和流量分类。由于数据一直都是不确定的，因此需要一个人工神经网络。因此，我们的方法可提高吞吐量，最大程度地减少流程完成时间，并最大程度地减少数据丢失。