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.

本文提出了一种基于机器学习的针对广域网的智能系统，系统由用于预训练的核心机器和众多用于快速响应的终端机器构成，每个终端机器都是由左右半球构成的双半球模型，其中左半球用于通过终端响应来提高延迟，右半球则通过数据生成来提高通信，该模型在多媒体服务应用中具有较高的准确性、低延迟和高通信性能，与最新的前馈深度神经网络相比更优秀，同时随着终端机器数量的增加，系统性能也可扩展，并且成本是较长的学习时间。

应用于多媒体服务的广域网络智能