Automatic Quality of Experience Management for WLAN Networks using Multi-Armed Bandit

被引:0
作者
Moura, Henrique D. [1 ]
Macedo, Daniel Fernandes [1 ]
Vieira, Marcos A. M. [1 ]
机构
[1] Univ Fed Minas Gerais, Dept Ciencia Comp, Belo Horizonte, MG, Brazil
来源
2019 IFIP/IEEE SYMPOSIUM ON INTEGRATED NETWORK AND SERVICE MANAGEMENT (IM) | 2019年
关键词
Wireless Networks; Software Defined Networks; Reinforcement Learning; Multi-Armed Bandit; Quality of Experience; WIRELESS; FRAMEWORK;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Providing acceptable Quality of Experience (QoE) in Wireless Local Area Network (WLAN) is very difficult: home networks are managed by non-technical people, and the proprietary management solutions of enterprise networks usually do not incorporate QoE mechanisms. Due to these difficulties, automatic QoE management mechanisms are welcome. This paper presents control loops capable of changing the power and transmission channels in the WLAN, based on Software Defined Wireless Networks and reinforcement learning, in order to improve user satisfaction for Web applications. A prototype evaluates our proposal in three case studies with a web browsing application, in which several access points are controlled by a central controller or by independent controllers. Our results show that the control loop can improve the Mean Opinion Score (MOS) by at least 4 % in the worst case, and 167 % in the best case, thus benefiting the user. Further, the control loop also reduced in page load time by 25 % in the worst case, and 233 % in the best case.
引用
收藏
页码:279 / 288
页数:10
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