A cloud and fog network architecture for medical big data and its distributed computing scheme

作者：He Xiuli; Ren Zhiyuan; Shi Chenhua; Cong Li

来源：Journal of Xi'an Jiaotong University, 2016, 50(10): 71-77.

DOI：10.7652/xjtuxb201610011

• Cache analysis
• Computing devices
• Computing power
• Constrained particle swarm optimization
• Hybrid clouds
• Latency performance
• Medical measurement
• Multi-devices

A novel hybrid cloud and fog network architecture based on edge computing is proposed to solve the problem of high processing latency of the medical big data in cloud computing network. The architecture adds a fog computing layer between cloud servers and medical measurement devices by using edge devices such as routers or switches in a hospital. Fog computing devices proactively cache analysis results of medical images and other medical big data from cloud servers, and compare these data with the data from medical measurement devices to get the diagnostic results and reduce processing latency. Meanwhile, a multi device distributed computing scheme is proposed by considering the weak computing power of edge devices and a constrained particle swarm optimization load balancing(CPSO-LB) algorithm is applied to minimize the latency. Simulation results indicate that the novel network architecture with CPSO-LB algorithm decreases the latency effectively. A comparison with a cloud computing shows that it's latency performance increases by 50.95%-37.37% when 10 fog devices and processing 6-10 Gb medical data are used.

• 出版日期2016
• 单位西安电子科技大学