Network intelligence is a discipline that builds on the capabilities of network systems to act intelligently by the usage of network resources for delivering high-quality services in a changing environment. Wide area network intelligence is a class of network intelligence in wide area network which covers the core and the edge of Internet. In this paper, we propose a system based on machine learning for wide area network intelligence. The whole system consists of a core machine for pre-training and many terminal machines to accomplish faster responses. Each machine is one of dual-hemisphere models which are made of left and right hemispheres. The left hemisphere is used to improve latency by terminal response and the right hemisphere is used to improve communication by data generation. In an application on multimedia service, the proposed model is superior to the latest deep feed forward neural network in the data center with respect to the accuracy, latency and communication. Evaluation shows scalable improvement with regard to the number of terminal machines. Evaluation also shows the cost of improvement is longer learning time.

翻译：广域网情报是覆盖互联网核心和边缘的广域网网网络情报的一类网络情报。在本文中,我们提议一个基于广域网情报的机器学习的系统。整个系统由训练前的核心机和许多终端机组成,以作出更快的反应。每台机器都是由左半球和右半球制作的双声波模型之一。左半球用于通过终端反应改善延缓度,右半球用于通过数据生成改进通信。在多媒体服务应用中,提议的模型优于数据中心最新的深传前神经网络,其准确性、延缓性和通信性。评价显示,终端机数量有可扩展的改进。评价还显示,改进的代价是更长的学习时间。