摘要 : The massive influx of mobile devices and their increasing use in recent years have resulted in the overprovision of access points (APs) in networks. Unlike in residential environments, network administrators in enterprises and uni... 展开 The massive influx of mobile devices and their increasing use in recent years have resulted in the overprovision of access points (APs) in networks. Unlike in residential environments, network administrators in enterprises and universities make every endeavor to enhance the user experience (UX) of WiFi networks where the network dynamics (e.g., traffic load and user mobility) are usually unexpected. To this end, an existing mechanism for WiFi association is client driven, i.e., users associate themselves to the AP with higher signal strength. However, they still incur dissatisfaction due to the insufficient available bandwidth. To cope with this in a centralized manner, we propose DARCAS, a software-defined network (SDN)-enabled WiFi framework for association regulation. DARCAS adopts a notion of bandwidth satisfaction ratio (BSR), which is closely related to UX. It maximizes the aggregated network throughput while satisfying the BSR of each user with sufficient airtime (i.e., channel occupancy time) provision. We use this idea in a metaheuristic genetic algorithm called DARCAS-GA, which effectively finds the suboptimal association distribution of the maximum BSR in polynomial time. We implement the DARCAS system on off-the-shelf wireless routers and an SDN controller. We report real-life experimental results in the considered scenarios and conduct extensive simulations on the NS-3 simulator to examine its performance with scalability. With fine-tuned settings, DARCAS exhibits up to 80% of the BSR gain compared to existing solutions. 收起