arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2201.07216cs.NIcs.AIcs.NE
收藏

广域网智能及其在多媒体业务中的应用

Wide Area Network Intelligence with Application to Multimedia Service

Chinese Academy of Sciences(中国科学院);University of Chinese Academy of Sciences(中国科学院大学);Osaka University(大阪大学);Tokyo Institute of Technology(东京工业大学)

Satoshi Kamo, Yiqiang Sheng

摘要:

网络智能是一门学科,它建立在网络系统的能力之上,通过使用网络资源在不断变化的环境中提供高质量的服务来智能地行动。广域网智能是广域网中的一类网络智能,它覆盖了互联网的核心和边缘。本文提出了一种基于机器学习的广域网智能系统。整个系统由一个用于预训练的核心机和多个用于快速响应的终端机组成。每台机器都是由左右半球组成的双半球模型。左半球用于通过终端响应来改善延迟,而右半球用于通过数据生成来改善通信。在一个多媒体业务的应用中,该模型在准确性、延迟和通信方面优于数据中心最新的深度前馈神经网络。评估表明,可扩展的改善方面的终端机的数量。评估还表明,改进的成本是更长的学习时间。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑