论文标题
广泛的区域网络智能，并应用多媒体服务
Wide Area Network Intelligence with Application to Multimedia Service
论文作者
论文摘要
网络智能是一门学科，它基于网络系统的能力，可通过在不断变化的环境中提供高质量服务的网络资源来智能行动。广域网络智能是范围内网络中的一类网络智能，涵盖了互联网的核心和边缘。在本文中，我们提出了一个基于机器学习的系统，用于广泛的网络智能。整个系统由用于预训练的核心机器和许多终端机器，以实现更快的响应。每台机器都是由左和右半球制成的双半球模型之一。左半球用于通过终端响应来改善潜伏期，右半球用于通过数据生成来改善通信。在多媒体服务的应用程序中，就准确性，延迟和通信而言，所提出的模型优于数据中心中最新的深层饲料前向神经网络。评估显示有关终端机数量的可扩展改善。评估还表明，改善成本是更长的学习时间。
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.
