强化学习
计算机科学
软件定义的网络
多媒体
体验质量
软件
控制(管理)
交通整形
人工智能
计算机网络
网络流量控制
服务质量
网络数据包
程序设计语言
作者
Xiaotao Huang,Tingting Yuan,Guanhua Qiao,Yi Ren
出处
期刊:IEEE Network
[Institute of Electrical and Electronics Engineers]
日期:2018-11-01
卷期号:32 (6): 35-41
被引量:58
标识
DOI:10.1109/mnet.2018.1800097
摘要
Software Defined Networking (SDN) is a promising paradigm to provide centralized traffic control. Multimedia traffic control based on SDN is crucial but challenging for Quality of Experience (QoE) optimization. It is very difficult to model and control multimedia traffic because solutions mainly depend on an understanding of the network environment, which is complicated and dynamic. Inspired by the recent advances in artificial intelligence (AI) technologies, we study the adaptive multimedia traffic control mechanism leveraging Deep Reinforcement Learning (DRL). This paradigm combines deep learning with reinforcement learning, which learns solely from rewards by trial-and-error. Results demonstrate that the proposed mechanism is able to control multimedia traffic directly from experience without referring to a mathematical model.
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