A Cloud and Fog Network Architecture for Medical Big Data and Its Distributed Computing Scheme

doi:10.7652/xjtuxb201610011

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A Cloud and Fog Network Architecture for Medical Big Data and Its Distributed Computing Scheme

更新时间：2025-07-09

- A Cloud and Fog Network Architecture for Medical Big Data and Its Distributed Computing Scheme
- Vol. 50, Issue 10, Pages: 71-77(2016)
- 作者机构：
  
  西安电子科技大学ISN国家重点实验室,西安,710071
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201610011
  CLC： TP393.1
- Online First：10 October 2016，
  
  Published：2016
- 稿件说明：
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A Cloud and Fog Network Architecture for Medical Big Data and Its Distributed Computing Scheme[J]. 2016, 50(10): 71-77.
DOI：

A Cloud and Fog Network Architecture for Medical Big Data and Its Distributed Computing Scheme[J]. 2016, 50(10): 71-77. DOI： 10.7652/xjtuxb201610011.

摘要

针对云计算应用于医疗大数据场景时存在业务处理时延较高的问题

提出了一种基于边缘计算的新型云/雾混合网络架构

该架构利用医院中的路由器或交换机等边缘设备

在云服务器与医疗检测设备之间构建一个雾计算层

通过将云服务器中的医学影像等医疗大数据分析结果主动缓存至雾计算设备

并与雾设备上来自医疗检测终端的数据进行对比计算

得出诊断结果

达到降低业务处理时延的目的。考虑到边缘设备的计算能力较弱

进一步提出了一种多设备分布式计算方案

利用带约束的粒子群优化负载均衡(CPSO-LB)算法

达到任务处理时延最小的目标。仿真结果表明:基于CPSO-LB算法的云/雾混合网络能有效地降低医疗数据处理时延; 当采用10个雾计算设备

处理的医疗数据量在6～10 Gb时

与云计算网络相比时延性能提升了50.95%～37.37%。

Abstract

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.

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references

孙瑛, 朱刘松. 浅析云计算在医院信息化建设中的应用 [J]. 解放军医院管理杂志, 2014, 21(7): 660-662.

SUN Ying, ZHU Liusong. Brief analysis on the application of cloud computing in the construction of hospital information technology [J]. Hospital Administration Journal of Chinese People's Liberation Army, 2014, 21(7): 660-662.

高汉松, 肖凌, 许德玮, 等. 基于云计算的医疗大数据挖掘平台 [J]. 医学信息学杂志, 2013, 34(5): 7-12.

GAO Hansong, XIAO Ling, XU Dewei, et al. Medical data mining platform based on cloud computing [J]. Journal of Medical Informatics, 2013, 34(5): 7-12.

王达明. 基于云计算与医疗大数据的Apriori算法的优化研究 [D]. 北京: 北京邮电大学, 2015: 33-42.

ZHANG Haolan, ZHAO Yali, PANG Chaoyi, et al. Splitting large medical data sets based on normal distribution in cloud environment [EB/OL].(2015-07-29)[2016-01-25]. http:∥dx.doi.org/10.1109/TCC. 2015.2462361.

BONOMI F, MILITO R, ZHU J, et al. Fog computing and its role in the internet of things [C]∥Proceedings of the 1st ACM Mobile Cloud Computing Workshop. New York, USA: ACM, 2012: 13-15.

SARKAR S, MISRA S. Theoretical modelling of fog computing: a green computing paradigm to support IoT applications [J]. IET Networks, 2016, 5(2): 23-29.

OUEIS J, STRINATI E C, BARBAROSSA S. The fog balancing: load distribution for small cell cloud computing [C]∥IEEE Vehicular Technology Conference. Piscataway, NJ, USA: IEEE, 2015: 1-6.

HASSAN M A, XIAO M, WEI Q, et al. Help your mobile applications with fog computing [C]∥2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking-Workshops. Piscataway, NJ, USA: IEEE, 2015: 49-54.

YI Shanhe, HAO Zijiang, QIN Zhengrui, et al. Fog computing: platform and applications [C]∥Proceedings of 3rd Workshop on Hot Topics in Web Systems and Technologies. Piscataway, NJ, USA: IEEE, 2015: 73-78.

GU L, ZENG D, GUO S, et al. Cost-efficient resource management in fog computing supported medical CPS [EB/OL].(2015-12-17)[2016-01-25]. http: ∥dx.doi.org/10.1109/TETC.2015.2508382.

DUBEY H, YANG J, CONSTANT N, et al. Fog data: enhancing telehealth big data through fog computing [C]∥ Proceedings of the ASE Big Data and Social Informatics 2015. New York, USA: ACM, 2015: a14.

SHI Y, DING G, WANG H, et al. The fog computing service for healthcare [C]∥2015 2nd International Symposium on Future Information and Communication Technologies for Ubiquitous Healthcare. Piscataway, NJ, USA: IEEE, 2015: 1-5.

ISMAIL B I, GOORTANI E M, KARIM M B A, et al. Evaluation of docker as edge computing platform [C]∥2015 IEEE Conference on Open Systems. Piscataway, NJ, USA: IEEE, 2015: 130-135.

KENNEDY J, EBERHART R C. Particle swarm optimization [C]∥Proceedings of the 1995 IEEE International Conference on Neural Networks. Piscataway, NJ, USA: IEEE, 1995: 1942-1948.

李相勇, 田澎, 孔民. 解约束优化问题的新粒子群算法 [J]. 系统管理学报, 2007, 16(2): 120-129.

LI Xiangyong, TIAN Peng, KONG Min. A new particle swarm optimization for solving constrained optimization problems [J]. Journal of Systems Management, 2007, 16(2): 120-129.

赵建华, 张陵, 孙清. 利用粒子群算法的传感器优化布置及结构损伤识别研究 [J]. 西安交通大学学报, 2015, 49(1): 79-85.

ZHAO Jianhua, ZHANG Ling, SUN Qing. Optimal placement of sensors for structural damage identification using improved particle swarm optimization [J]. Journal of Xi'an Jiaotong University, 2015, 49(1): 79-85.

RADOJEVIC B, ZAGAR M. Analysis of issues with load balancing algorithms in hosted(cloud)environments [C]∥Proceedings of the 34th International Convention on Information and Communication Technology, Electronics and Microelectronics. Piscataway, NJ, USA: IEEE, 2011: 416-420.

王霜, 修保新, 肖卫东. Web服务器集群的负载均衡算法研究 [J]. 计算机工程与应用, 2004, 40(25): 78-80.

WANG Shuang, XIU Baoxin, XIAO Weidong. Research on dynamic load-balancing algorithm for web-service cluster system [J]. Computer Engineering and Applications, 2004, 40(25): 78-80.

SAHOO B, KUMAR D, JENA S K. Performance analysis of greedy load balancing algorithms in heterogeneous distributed computing system [C]∥2014 International Conference on High Performance Computing and Applications. Piscataway, NJ, USA: IEEE, 2014: 1-7.

李谦,伍卫国,曹莹方,等.面向能耗优化的云渲染系统任务调度策略.2016,50(2):1-6.[doi:10.7652/xjtuxb201602001]

周墨颂,朱正东,董小社,等.采用资源划分的云环境下Hadoop资源许可调度方法.2015,49(8):69-74.[doi:10.7652/xjtuxb201508012]

郑鹏飞,尤佳莉,王劲林,等.一种多租户云的内部网络共享策略.2014,48(8):54-59.[doi:10.7652/xjtuxb201408010]

马莉,唐善成,王静,等.云计算环境下的动态反馈作业调度算法.2014,48(7):77-82.[doi:10.7652/xjtuxb201407014]

熊金波,姚志强,马建峰,等.云计算环境中的组合文档模型及其访问控制方案.2014,48(2):25-31.[doi:10.7652/xjtuxb201402005]

庄威,桂小林,林建材,等.云环境下基于多属性层次分析的虚拟机部署与调度策略.2013,47(2):28-32.[doi:10.7652/xjtuxb201302005]

李健,黄庆佳,刘一阳,等.云计算环境下的大规模图状数据处理任务调度算法.2012,46(12):116-122.[doi:10.7652/xjtuxb201212020]

傅欢,梁力,王飞,等.采用局部凸性和八叉树的点云分割算法.2012,46(10):60-65.[doi:10.7652/xjtuxb201210011]

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