Research Article
Research on Load Balancing for Software Defined Cloud-Fog Network in Real-Time Mobile Face Recognition
@INPROCEEDINGS{10.1007/978-3-319-66628-0_12, author={Chenhua Shi and Zhiyuan Ren and Xiuli He}, title={Research on Load Balancing for Software Defined Cloud-Fog Network in Real-Time Mobile Face Recognition}, proceedings={Communications and Networking. 11th EAI international Conference, ChinaCom 2016 Chongqing, China, September 24-26, 2016, Proceedings, Part II}, proceedings_a={CHINACOM}, year={2017}, month={10}, keywords={Mobile face recognition Cloud computing Fog computing Cloud-fog network Software Defined Network Load balancing}, doi={10.1007/978-3-319-66628-0_12} }- Chenhua Shi
Zhiyuan Ren
Xiuli He
Year: 2017
Research on Load Balancing for Software Defined Cloud-Fog Network in Real-Time Mobile Face Recognition
CHINACOM
Springer
DOI: 10.1007/978-3-319-66628-0_12
Abstract
The real-time camera-equipped mobile devices have been widely researched recently. And cloud computing has been used to support those applications. However, the high communication latency and unstable connections between cloud and users influence the Quality of Service (QoS). To address the problem, we integrate fog computing and Software Defined Network (SDN) to the current architecture. Fog computing pushes the computation and storage resources to the network edge, which can efficiently reduce the latency and enable mobility support. While SDN offers flexible centralized control and global knowledge to the network. For applying the software defined cloud-fog network (SDC-FN) architecture in the real-time mobile face recognition scenario effectively, we propose leveraging the SDN centralized control and fireworks algorithm (FWA) to solve the load balancing problem in the SDC-FN. The simulation results demonstrate that the SDN-based FWA could decrease the latency remarkably and improve the QoS in the SDC-FN architecture.