本文關鍵詞： 醫(yī)療大數據 云計算 云/霧混合網絡 負載均衡　出處：《西安交通大學學報》2016年10期 　論文類型：期刊論文

【摘要】：針對云計算應用于醫(yī)療大數據場景時存在業(yè)務處理時延較高的問題,提出了一種基于邊緣計算的新型云/霧混合網絡架構,該架構利用醫(yī)院中的路由器或交換機等邊緣設備,在云服務器與醫(yī)療檢測設備之間構建一個霧計算層,通過將云服務器中的醫(yī)學影像等醫(yī)療大數據分析結果主動緩存至霧計算設備,并與霧設備上來自醫(yī)療檢測終端的數據進行對比計算,得出診斷結果,達到降低業(yè)務處理時延的目的�？紤]到邊緣設備的計算能力較弱,進一步提出了一種多設備分布式計算方案,利用帶約束的粒子群優(yōu)化負載均衡(CPSO-LB)算法,達到任務處理時延最小的目標。仿真結果表明:基于CPSO-LB算法的云/霧混合網絡能有效地降低醫(yī)療數據處理時延;當采用10個霧計算設備,處理的醫(yī)療數據量在6~10Gb時,與云計算網絡相比時延性能提升了50.95%~37.37%。
[Abstract]:A new cloud / fog hybrid network architecture based on edge computing is proposed to solve the problem of high service delay when cloud computing is applied to the medical big data scenario. The architecture uses edge devices such as routers or switches in a hospital to build a fog computing layer between a cloud server and a medical detection device. The results of medical big data analysis such as medical images in the cloud server are actively cached to the fog computing equipment, and compared with the data from the medical detection terminal on the fog equipment, the diagnosis results are obtained. In order to reduce the delay of service processing, a multi-device distributed computing scheme is proposed considering the weak computing power of edge devices. The constrained particle swarm optimization (PSO) load balancing algorithm (CPSO-LB) is used. The simulation results show that the cloud / fog hybrid network based on CPSO-LB algorithm can effectively reduce the delay of medical data processing. When 10 fog computing devices are used and the amount of medical data processed is 610Gb, the delay performance of cloud computing network is improved by 50.95g / 37.37g / h compared with cloud computing network.
【作者單位】： 西安電子科技大學ISN國家重點實驗室;國網吉林省電力有限公司信息通信公司;
【基金】：國家自然科學基金資助項目(61401331) 港澳臺科技合作專項資金資助項目(2015DFT10160) 中央高校基本科研業(yè)務費專項資金資助項目(20101155739)
【分類號】：TP311.13;TP338.8
【正文快照】： 在醫(yī)患比例嚴重失調的當今社會,挖掘分析醫(yī)療大數據,利用獲得的有價值的信息輔助醫(yī)生診斷治療疾病,提高醫(yī)生診斷效率,成為了迫切需要解決的問題。云計算作為目前醫(yī)療大數據分析處理的支撐平臺,為醫(yī)院信息化建設提供了強大的技術支持[1]。近年來,專家學者們基于云計算提出了醫(yī)，

本文編號：1478730