雾计算的概念、相关研究与应用

doi:10.11959/j.issn.1000-436x.2018086

摘要/Abstract

摘要：

首先，系统地分析和总结了雾计算的研究现状，重点介绍了雾计算出现的背景及其相对于云计算的优势，对雾计算及其他相似的计算模式进行比较，指出了雾计算相比于传统计算模式的优点。然后，总结了雾计算的体系结构与各层功能。同时，对于雾计算在网络管理和资源调度这2个方面的研究问题展开讨论，总结了前人提出的解决方法并指出了现有方法的不足。最后，对于雾计算的一些相关应用进行了阐述，并以智能驾驶、工业物联网这2个示范应用为例指出了当前雾计算在实际应用上仍需攻关的重要课题。

关键词: 雾计算, 时延, 地理分布, 移动性

Abstract:

Firstly,the previous work of fog computing was systematically analyzed and summarized.The background of fog computing and the comparison with cloud computing were introduced.Besides,based on the comparison with other computing style,the advantages and characteristics of fog computing were explained.In addition,the architecture of fog computing was described.Moreover,network management and resource scheduling of fog computing were discussed,where the related previous work were summarized and analyzed.At last,the applications of fog computing were described.Taking the intelligent driving and industrial Internet of things applications as examples,the key research issues of fog computing were proposed.

Key words: fog computing, delay, geo-distributed, mobility

中图分类号:

TP393

贾维嘉,周小杰. 雾计算的概念、相关研究与应用[J]. 通信学报, 2018, 39(5): 153-165.

Weijia JIA,Xiaojie ZHOU. Concepts,issues,and applications of fog computing[J]. Journal on Communications, 2018, 39(5): 153-165.

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参考文献 66

[1]	ARMBRUST M , FOX A , GRIFFITH R ,et al. A view of cloud computing[J]. Communications of the ACM, 2010,53(4): 50-58.
[2]	BONOMI F , MILITO R , ZHU J ,et al. Fog computing and its role in the internet of things[C]// Edition of the Mcc Workshop on Mobile Cloud Computing. 2012: 13-16.
[3]	HASHIZUME K , ROSADO D G , FERNáNDEZ-MEDINA E ,et al. An analysis of security issues for cloud computing[J]. Journal of Internet Services and Applications, 2013,4(1): 5.
[4]	STOJMENOVIC I , WEN S . The fog computing paradigm:scenarios and security issues[C]// Federated Conference on Computer Science and Information Systems. 2014: 1-8.
[5]	HOU X , LI Y , CHEN M ,et al. Vehicular fog computing:a viewpoint of vehicles as the infrastructures[J]. IEEE Transactions on Vehicular Technology, 2016,65(6): 3860-3873.
[6]	PENG M , YAN S , ZHANG K ,et al. Fog-computing-based radio access networks:issues and challenges[J]. IEEE Network, 2015,30(4): 46-53.
[7]	LU R , ZHU H , LIU X ,et al. Toward efficient and privacy-preserving computing in big data era[J]. IEEE Network, 2014,28(4): 46-50.
[8]	BONOMI F , . Connected vehicles,the internet of things,and fog computing[C]// The Eighth ACM International Workshop on Vehicular Inter-Networking (VANET). 2011: 13-15.
[9]	VAQUERO L M , RODERO-MERINO L . Finding your way in the fog[J]. ACM SIGCOMM Computer Communication Review, 2014,44(5): 27-32.
[10]	FERNANDO N , SENG W L , RAHAYU W . Mobile cloud computing:a survey[J]. Future Generation Computer Systems, 2013,29(1): 84-106.
[11]	DINH H T , LEE C , NIYATO D ,et al. A survey of mobile cloud computing:architecture,applications,and approaches[J]. Wireless Communications ＆ Mobile Computing, 2013,13(18): 1587-1611.
[12]	DAVIS A , PARIKH J , WEIHL W E . Edge computing:extending enterprise applications to the edge of the internet[C]// International Conference on World Wide Web-Alternate Track Papers ＆ Posters. 2004: 180-187.
[13]	DENG R , LU R , LAI C ,et al. Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption[J]. IEEE Internet of Things Journal, 2017,3(6): 1171-1181.
[14]	YI S , HAO Z , QIN Z ,et al. Fog computing:platform and applications[C]// Hot Topics in Web Systems and Technologies. 2015: 73-78.
[15]	FIRDHOUS M , GHAZALI O , HASSAN S ,et al. Fog computing:will it be the future of cloud computing?[C]// International Conference on Informatics ＆ Applications. 2014: 1-8.
[16]	LUAN T H , GAO L , LI Z ,et al. Fog computing:focusing on mobile users at the edge[J]. arXiv Preprint,arXiv:1502.01815, 2015.
[17]	LI J , JIN J , YUAN D ,et al. EHOPES:data-centered fog platform for smart living[C]// Telecommunication Networks and Applications Conference. 2015: 308-313.
[18]	HAJIBABA M , GORGIN S . A review on modern distributed computing paradigms:cloud computing,jungle computing and fog computing[J]. Journal of Computing ＆ Information Technology, 2014,22(2): 69.
[19]	SATYANARAYANAN M . Fundamental challenges in mobile computing[J]. ACM Symposium on Principles of Distributed Computing, 1996: 1-7.
[20]	LI W , ZHAO Y , LU S ,et al. Mechanisms and challenges on mobility-augmented service provisioning for mobile cloud computing[J]. IEEE Communications Magazine, 2015,53(3): 89-97.
[21]	TULI A , HASTEER N , SHARMA M ,et al. Exploring challenges in mobile cloud computing:an overview[C]// Confluence 2013:the Next Generation Information Technology Summit. 2013: 496-501.
[22]	NISHIO T , SHINKUMA R , TAKAHASHI T ,et al. Service-oriented heterogeneous resource sharing for optimizing service latency in mobile cloud[C]// International Workshop on Mobile Cloud Computing ＆NETWORKING. 2013: 19-26.
[23]	BITTENCOURT L F , LOPES M M , PETRI I ,et al. Towards virtual machine migration in fog computing[C]// International Conference on P2P,Parallel,Grid,Cloud and Internet Computing. 2015: 1-8.
[24]	SHI W , CAO J , ZHANG Q ,et al. Edge computing:vision and challenges[J]. IEEE Internet of Things Journal, 2016,3(5): 637-646.
[25]	CHURCH K , GREENBERG A , HAMILTON J . On delivering embarrassingly distributed cloud services[C]// Hotnets. 2008: 55-60.
[26]	GU C , LIU C , ZHANG J ,et al. Green scheduling for cloud data centers using renewable resources[J]. Proceedings-IEEE INFOCOM, 2015,2015: 354-359.
[27]	SATYANARAYANAN M , BAHL P , CACERES R ,et al. The case for VM-based cloudlets in mobile computing[J]. IEEE Pervasive Computing, 2009,8(4): 14-23.
[28]	YANNUZZI M , MILITO R , SERRAL-GRACIA R ,et al. Key ingredients in an IoT recipe:fog computing,cloud computing,and more fog computing[C]// IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks. 2014: 325-329.
[29]	LUAN T H , CAI L X , CHEN J ,et al. VTube:towards the media rich city life with autonomous vehicular content distribution[C]// Sensor,Mesh and Ad Hoc Communications and Networks. 2011: 359-367.
[30]	GARCIA L P , MONTRESOR A , EPEMA D ,et al. Edge-centric computing:vision and challenges[J]. ACM Sigcomm Computer Communication Review, 2015,45(5): 37-42.
[31]	JUTILA M . An adaptive edge router enabling Internet of things[J]. IEEE Internet of Things Journal, 2016,3(6): 1061-1069.
[32]	XU Y , MAHENDRAN V , RADHAKRISHNAN S . Towards SDN-based fog computing:MQTT broker virtualization for effective and reliable delivery[C]// International Conference on Communication Systems and Networks. 2016: 1-6.
[33]	KRISHNAN Y N , BHAGWAT C N , UTPAT A P . Fog computing—network based cloud computing[C]// International Conference on Electronics and Communication Systems. 2015: 250-251.
[34]	BRUNEO D , DISTEFANO S , LONGO F ,et al. Stack4Things as a fog computing platform for smart city applications[C]// IEEE INFOCOM 2016-IEEE Conference on Computer Communications Workshops. 2016: 848-853.
[35]	GUPTA H , VAHIDDASTJERDI A , GHOSH S K ,et al. iFogSim:a toolkit for modeling and simulation of resource management techniques in the Internet of things,edge and fog computing environments[J]. Software:Practice and Experience, 2017,47(9): 1275-1296.
[36]	YAN S , PENG M , WANG W . User access mode selection in fog computing based radio access networks[J]. arXiv present,arXiv:1602.00766, 2016.
[37]	YI S , LI C , LI Q . A survey of fog computing:concepts,applications and issues[C]// The Workshop on Mobile Big Data. 2015: 37-42.
[38]	HELLER B , SHERWOOD R , MCKEOWN N . The controller placement problem[C]// Workshop on Hot Topics in Software Defined Networks. 2012: 7-12.
[39]	DSOUZA C , AHN G J , TAGUINOD M . Policy-driven security management for fog computing:preliminary framework and a case study[C]// IEEE International Conference on Information Reuse and Integration. 2015: 16-23.
[40]	MODI C , PATEL D , BORISANIYA B ,et al. Review:a survey of intrusion detection techniques in cloud[J]. Journal of Network ＆Computer Applications, 2013,36(1): 42-57.
[41]	KULKARNI S , SAHA S , HOCKENBURY R . Preserving privacy in sensor-fog networks[C]// Internet Technology and Secured Transactions. 2015: 96-99.
[42]	STOLFO S J , SALEM M B , KEROMYTIS A D . Fog computing:mitigating insider data theft attacks in the cloud[C]// IEEE Symposium on Security and Privacy Workshops. 2012: 125-128.
[43]	VALENZUELA J , WANG J , BISSINGER N . Real-time intrusion detection in power system operations[J]. IEEE Transactions on Power Systems, 2013,28(2): 1052-1062.
[44]	STONE M . Cross-validatory choice and assessment of statistical predictions[M]// Introduction to chaos:Institute of Physics Pub. 1999: 111-147.
[45]	FARAHNAKIAN F , LILJEBERG P , PLOSILA J . Energy-efficient virtual machines consolidation in cloud data centers using reinforcement learning[C]// Euromicro International Conference on Parallel,Distributed,and Network-Based Processing. 2014: 500-507.
[46]	HAN Z , TAN H , CHEN G ,et al. Dynamic virtual machine management via approximate Markov decision process[C]// IEEE Conference on Computer Communications. 2016: 1-9.
[47]	BEATE OTTENW?LDER , KOLDEHOFE B , ROTHERMEL K ,et al. MigCEP:operator migration for mobility driven distributed complex event processing[C]// ACM International Conference on Distributed Event-Based Systems. 2013: 183-194.
[48]	DO C T , TRAN N H , PHAM C ,et al. A proximal algorithm for joint resource allocation and minimizing carbon footprint in geo-distributed fog computing[C]// International Conference on Information NETWORKING. 2015: 324-329.
[49]	ZHANG H , XIAO Y , BU S ,et al. Fog computing in multi-tier data center networks:a hierarchical game approach[C]// IEEE International Conference on Communications. 2016: 1-6.
[50]	ZENG D , GU L , GUO S ,et al. Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system[J]. IEEE Transactions on Computers, 2016,65(12): 3702-3712.
[51]	HUANG C Y , XU K . Reliable realtime streaming in vehicular cloud-fog computing networks[C]// IEEE/CIC International Conference on Communications in China. 2016: 1-6.
[52]	ZHANG H , ZHANG Q , DU X . Toward vehicle-assisted cloud computing for smartphones[J]. IEEE Transactions on Vehicular Technology, 2015,64(12): 5610-5618.
[53]	OUEIS J , STRINATI E C , BARBAROSSA S . The fog balancing:load distribution for small cell cloud computing[C]// Vehicular Technology Conference. 2015: 1-6.
[54]	HONG H J , TSAI P H , HSU C H . Dynamic module deployment in a fog computing platform[C]// Network Operations and Management Symposium. 2016: 1-6.
[55]	PHAM X Q , HUH E N . Towards task scheduling in a cloud-fog computing system[C]// Network Operations and Management Symposium (APNOMS). 2016: 1-4.
[56]	YANG J , ZHANG S , WU X ,et al. Online learning-based server provisioning for electricity cost reduction in data center[J]. IEEE Transactions on Control Systems Technology, 2016,PP(99): 1-8.
[57]	MAO H , ALIZADEH M , MENACHE I ,et al. Resource management with deep reinforcement learning[C]// ACM Workshop on Hot Topics in Networks. 2016: 50-56.
[58]	LIU N , LI Z , XU J ,et al. A hierarchical framework of cloud resource allocation and power management using deep reinforcement learning[C]// 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). 2017: 372-382.
[59]	ZHU X , CHEN H , YANG L T ,et al. Energy-aware rolling-horizon scheduling for real-time tasks in virtualized cloud data centers[C]// High Performance Computing and Communications ＆ 2013 IEEE International Conference on Embedded and Ubiquitous Computing (HPCC_EUC). 2013: 1119-1126.
[60]	OpenFog Consortium Architecture Working Group. OpenFog reference architecture for fog computing[J]. OPFRA001, 2017,20817: 162
[61]	CAO Y , HOU P , BROWN D ,et al. Distributed analytics and edge intelligence:pervasive health monitoring at the era of fog computing[C]// The Workshop on Mobile Big Data. 2015: 43-48.
[62]	ROY S , BOSE R , SARDDAR D . A fog-based dss model for driving rule violation monitoring framework on the Internet of things[J]. International Journal of Advanced Science ＆ Technology, 2015,82: 23-32.
[63]	FADLULLAH Z M , KATO N . On optimally reducing power loss in micro-grids with power storage devices[M]. Evolution of Smart Grids.Springer International Publishing. 2015: 1361-1370.
[64]	KOPETZ H , POLEDNA S . In-vehicle real-time fog computing[C]// IEEE/IFIP International Conference on Dependable Systems and Networks Workshop. 2016: 162-167.
[65]	KHAN S , PARKINSON S , QIN Y . Fog computing security:a review of current applications and security solutions[J]. Journal of Cloud Computing, 2017,6(1): 19.
[66]	LOM M , PRIBYL O , SVITEK M.Industry 4 . 0 as a part of smart cities[C]// Smart Cities Symposium Prague. 2016: 1-6.

方面	具体指标	云计算	雾计算
	节点的访问方式	通过互联网（主要是有线网）	通过本地网络设施（主要是无线网）
	服务的访问方式	通过核心网	通过边缘设备
通信访问方式	终端用户到节点距离	多跳	一跳
	对于移动前传和集中式的基带单元的负担	重	轻
	缓存与无线信号处理	集中式	兼具集中式与分布式
	无线资源管理	集中式	兼具集中式与分布式
	控制方式	完全控制	部分控制
控制管理	软件虚拟的基础设施	数据中心服务器	边缘设备
	地理分布	否	是
	控制端数目	少	多
	时延	较高（分钟级~月级）	较低（毫秒级~秒级）
	时延抖动	高	很低
	可用性	99.99%	不定
	数据传输过程受攻击可能性	可能性高	可能性很低
	安全策略	难以定义	易被定义
服务质量	移动性支持	有限支持	支持程度高
	服务来源	全球	本地
	使用成本	高	低
	网络要求	高	低
	对实时应用的支持	差	好
	任务的传输功耗	大	小
	终端用户数	数十万或数百万	数十万
	节点数目	少	多
	硬件设备平均成本/美元	1 500~3 000	50~200
采用设备	设备部署位置	远端数据中心	靠近网络边缘
	硬件	存储容量大、计算能力强	存储容量与计算能力有限
	设备部署环境	带制冷设备的大型仓库	小型仓库或室外
	服务器拥有者与管理者	大公司（如Google）	小公司或个人
	部署速度（代价）	慢	快
	内容产生者	主要是人	主要是传感器设备
目标用户	内容丰富程度	丰富	单一
	目标用户	互联网用户	移动用户

层次	主要设备	主要功能
终端用户层	用户的手机、便携式电脑等终端设备与传感器节点	内容的生产者和内容的消费者
接入网络层	无线网络设备为主，有线网络设备为辅	按照预定的规则将终端用户的任务发送到对应的雾节点
雾层	雾边缘节点、微雾、雾服务器	提供一定程度的计算、存储和通信
核心网络层	核心网络设备	将超出雾层计算或存储能力的任务发送到云数据中心
云层	云数据中心服务器	数据备份、大计算量任务处理

文献来源	存储容量	时延	功耗	效用	网络资源占用与迁移时间
文献[13]	×	√（计算时延、传输时延）	√（功耗）	×	√（给定带宽下的网络资源占用）
文献[47]	×	√（往返时延）	×	×	√（给定带宽下的网络资源占用）
文献[48]	×	×	√（平均二氧化碳排放）	√（平均传输数据量）	×
文献[49]	×	√（计算时延、传输时延）	√（功耗）	√（基础设施成本）	√（迁移时间）
文献[50]	√（服务器的存储容量）	√（计算时延、I/O时延、传输时延）	×	×	×
文献[51]	×	×	×	√（平均传输数据量）	√（给定带宽下的网络资源占用）
文献[52]	×	√（往返时延）	√（功耗）	×	×
文献[53]	×	√（往返时延）	×	×	√（给定通信功率）
文献[54]	×	√（资源短缺程度）	×	√（平均传输数据量）	×
文献[55]	×	√（往返时延）	×	×	×