Cloud/fog hybrid network architecture based on SDN

Abstract

The invention belongs to the technical field of a fog computing network, and particularly relates to a cloud/fog hybrid network architecture based on an SDN. The cloud/fog hybrid network architecture comprises an infrastructure layer, a fog computing layer, an SDN control layer and a cloud computing layer, wherein the infrastructure layer is used for sending a request of a user terminal and detection data of a medical device to the fog computing layer; the fog computing layer is connected with the infrastructure layer via an unshielded category 5e twisted pair, and is used for receiving and storing medical data uploaded by the user terminal, downloading an analysis result on a cloud server in an active caching manner, processing the medical data uploaded by a client according to the downloaded analysis result, and uploading the medical data to the cloud computing layer; the SDN control layer is connected with the fog computing layer via the unshielded category 5e twisted pair, and is used for controlling data forwarding of the fog computing layer; and the cloud computing layer is connected with the SDN control layer via an optical fiber, and is used for storing the collected medical data, carrying out analysis on the medical data, and processing the medical data received by the fog computing layer cooperatively with the fog computing layer.

Description

technical field [0001] The invention belongs to the field of fog computing network technology, and in particular relates to an SDN-based cloud / fog hybrid network architecture. Background technique [0002] In medical scenarios, patients want to know accurate diagnosis results and the best treatment plan as soon as possible, which requires medical workers to have high work efficiency, which requires medical workers to have high personal quality, but medical workers Cultivation requires a long and lengthy process. [0003] With its powerful computing and storage capabilities, cloud computing can compare the patient medical testing data transmitted to the cloud with the results of big data analysis and mining, and obtain diagnostic results with important reference value for doctors, which can improve the work efficiency of doctors and reduce Patient waiting time. However, since the cloud server is far away from the medical detection terminal and user terminal, the time delay ...

AI Technical Summary

Problems solved by technology

However, since the cloud server is far away from the medical detection terminal and user terminal, the time delay and communication overhead caused by data transmission are relatively large; at the same time, a large number of medical terminals and user terminals access the cloud server, which makes the cloud server overloaded and the overall The network robustness is poor, and the network is prone to failure, which may lead to the inability to guarantee the work efficiency of doctors and increase the waiting time of patients.

Images