Journal Home > Volume 22 , Issue 6

With the rapid development of pervasive intelligent devices and ubiquitous network technologies, new network applications are emerging, such as the Internet of Things, smart cities, smart grids, virtual/augmented reality, and unmanned vehicles. Cloud computing, which is characterized by centralized computation and storage, is having difficulty meeting the needs of these developing technologies and applications. In recent years, a variety of network computing paradigms, such as fog computing, mobile edge computing, and dew computing, have been proposed by the industrial and academic communities. Although they employ different terminologies, their basic concept is to extend cloud computing and move the computing infrastructure from remote data centers to edge routers, base stations, and local servers located closer to users, thereby overcoming the bottlenecks experienced by cloud computing and providing better performance and user experience. In this paper, we systematically summarize and analyze the post-cloud computing paradigms that have been proposed in recent years. First, we summarize the main bottlenecks of technology and application that cloud computing encounters. Next, we analyze and summarize several post-cloud computing paradigms, including fog computing, mobile edge computing, and dew computing. Then, we discuss the development opportunities of post-cloud computing via several examples. Finally, we note the future development prospects of post-cloud computing.


menu
Abstract
Full text
Outline
About this article

Post-Cloud Computing Paradigms: A Survey and Comparison

Show Author's information Yuezhi Zhou( )Di Zhang( )Naixue Xiong
Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China.
Department Mathematics and Computer Science, Northeastern State University, Tahlequah, OK 74464, USA.

Abstract

With the rapid development of pervasive intelligent devices and ubiquitous network technologies, new network applications are emerging, such as the Internet of Things, smart cities, smart grids, virtual/augmented reality, and unmanned vehicles. Cloud computing, which is characterized by centralized computation and storage, is having difficulty meeting the needs of these developing technologies and applications. In recent years, a variety of network computing paradigms, such as fog computing, mobile edge computing, and dew computing, have been proposed by the industrial and academic communities. Although they employ different terminologies, their basic concept is to extend cloud computing and move the computing infrastructure from remote data centers to edge routers, base stations, and local servers located closer to users, thereby overcoming the bottlenecks experienced by cloud computing and providing better performance and user experience. In this paper, we systematically summarize and analyze the post-cloud computing paradigms that have been proposed in recent years. First, we summarize the main bottlenecks of technology and application that cloud computing encounters. Next, we analyze and summarize several post-cloud computing paradigms, including fog computing, mobile edge computing, and dew computing. Then, we discuss the development opportunities of post-cloud computing via several examples. Finally, we note the future development prospects of post-cloud computing.

Keywords:

cloud computing, fog computing, edge computing, mobile edge computing, dew computing
Received: 26 September 2017 Accepted: 29 September 2017 Published: 14 December 2017 Issue date: December 2017
References(65)
[1]
IBM, IBM introduces ready-to-use cloud computing, , 2007.
[2]
Bort J., Amazon’s massive cloud business hit over $12 billion in revenue and $3 billion in profit in 2016, , 2017.
[3]
Rosoff M., Microsoft vows to have $20 billion in cloud revenue by 2018, , 2015.
[4]
ITU, ICT facts and figures 2016, , 2016.
[5]
Ministry of Industry and Information Technology of the People’s Republic of China, Economic operation of Telecom industry November 2016, (in Chinese), , 2016.
[6]
Bonomi F., Connected vehicles, the internet of things, and fog computing, presented at the 8th ACM Int. Workshop on Vehicular Inter-networking, Las Vegas, NV, USA, 2011.
[7]
Bonomi F., Milito R., Zhu J., and Addepalli S., Fog computing and its role in the internet of things, in Proc. 1st Edition of the MCC Workshop on Mobile Cloud Computing, Helsinki, Finland, 2012, pp. 13-15.
[8]
ETSI, Mobile-edge computing—Introductory technical white paper, , 20014.
[9]
Wang Y. W., Cloud-dew architecture, Int. J. Cloud Comput., vol. 4, no. 3, pp. 199-210, 2015.
[10]
Berman F., Fox G., and Hey A. J. G., Grid Computing: Making the Global Infrastructure A Reality. New York, NY, USA: John Wiley, 2003.
DOI
[11]
Papazoglou M. P., Service-oriented computing: Concepts, characteristics and directions, in Proc. 4th Int. Conf. Web Information Systems Engineering, Roma, Italy, 2003, pp. 3-12.
[12]
Kephart J. O. and Chess D. M., The vision of autonomic computing, Computer, vol. 36, no. 1, pp. 41-50, 2003.
[13]
Zhang Y. X. and Zhou Y. Z., Transparent computing: A new paradigm for pervasive computing, in Proc. 3rd Int. Conf. Ubiquitous Intelligence and Computing, Wuhan, China, 2006, pp. 1-11.
[14]
Hu Y. C., Patel M., Sabella D., Sprecher N., and Young V., Mobile edge computing—A key technology towards 5G, , 2015.
[15]
Grigorik I., High Performance Browser Networking. O’Reilly, 2013.
[16]
Tolia N., Andersen D. G., and Satyanarayanan M., Quantifying interactive user experience on thin clients, Computer, vol. 39, no. 3, pp. 46-52, 2006.
[17]
OpenFog, OpenFog Consortium, , 2015.
[18]
Vaquero L. M. and Rodero-Merino L., Finding your way in the fog: Towards a comprehensive definition of fog computing, ACM SIGCOMM Comput. Commun. Rev., vol. 44, no. 5, pp. 27-32, 2014.
[19]
Yi S. H., Hao Z. J., Qin Z. R., and Li Q., Fog computing: Platform and applications, in Proc. 3rd IEEE Workshop on Hot Topics in Web Systems and Technologies, Washington, DC, USA, 2015, pp. 73-78.
[20]
Yi S. H., Li C., and Li Q., A survey of fog computing: Concepts, applications and issues, in Proc. 2015 Workshop on Mobile Big Data, Hangzhou, China, 2015, pp. 37-42.
[21]
ETSI, Multi-access edge computing, , 2016.
[22]
Morris I., ETSI drops ‘Mobile’ from MEC, , 2016.
[23]
Brown G., Mobile edge computing use cases & deployment options, , 2016.
[24]
ETSI, Mobile edge computing, , 2017.
[25]
Wang Y. W., The relationships among cloud computing, fog computing, and dew computing, , 2015.
[26]
Wang Y. W., The initial definition of dew computing, , 2015.
[27]
Wang Y. W., Definition and categorization of dew computing, Open J. Cloud Comput., vol. 3, no. 1, pp. 1-7, 2016.
[28]
Skala K., Davidovic D., Afgan E., Sovic I., and Šojat Z., Scalable distributed computing hierarchy: Cloud, fog and dew computing, Open J. Cloud Comput., vol. 2, no. 1, pp. 16-24, 2015.
[29]
Open Edge Computing, Open edge computing initiative, , 2017.
[30]
Shi W. S., Cao J., Zhang Q., Li Y., and Xu L. Y., Edge computing: Vision and challenges, IEEE Internet Things J., vol. 3, no. 5, pp. 637-646, 2016.
[31]
Zhou Y. Z., Tang W. J., Zhang D., and Zhang Y. X., Software-defined streaming-based code scheduling for transparent computing, in Proc. 4th Int. Conf. Advanced Cloud and Big Data, Chengdu, China, 2016, pp. 296-303.
[32]
Zhang Y. X., Guo K. H., Ren J., Zhou Y. Z., Wang J. X., and Chen J. E., Transparent computing: A promising network computing paradigm, Comput. Sci. Eng., vol. 19, no. 1, pp. 7-20, 2017.
[33]
Zhang Y. X. and Zhou Y. Z., Transparent computing: Spatio-temporal extension on von Neumann architecture for cloud services, Tsinghua Sci. Technol., vol. 18, no. 1, pp. 10-21, 2013.
[34]
Stojmenovic I. and Wen S., The fog computing paradigm: Scenarios and security issues, in Proc. 2014 Federated Conf. Computer Science and Information Systems, Warsaw, Poland, 2014, pp. 1-8.
[35]
Luan T. H., Gao L. X., Li Z., Xiang Y., Wei G. Y., and Sun L. M., Fog computing: Focusing on mobile users at the edge, arXiv preprint arXiv: 1502.01815, 2016.
[36]
Saharan K. P. and Kumar A., Fog in comparison to cloud: A survey, Int. J. Comput. Appl.. vol. 122, no. 3, pp. 10-12, 2015.
[37]
Klas G. I., Fog computing and mobile edge cloud gain momentum open fog consortium, ETSI MEC and Cloudlets, , 2015.
[38]
Dolui K. and Datta S. K., Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing, in Proc. Global Internet of Things Summit, Geneva, Switzerland, 2017, pp. 1-6.
[39]
Sun Q., Liu J., Li S., Fan C., and Sun J., Internet of things: Summarize on concepts, architecture, and key technology problem, (in Chinese), J. Beijing Univ. Posts Telecomm., vol. 33, no. 3, pp. 1-9, 2010.
[40]
ITU, The internet of things, , 2005.
[41]
Gartner, Gartner says 8.4 billion connected “Things” will be in use in 2017, Up 31 percent from 2016, , 2017.
[42]
Cisco, Fog computing and the internet of things: Extend the cloud to where the things are, , 2015.
[43]
Triboan D., Chen L. M., Chen F., and Wang Z. M., Towards a service-oriented architecture for a mobile assistive system with real-time environmental sensing, Tsinghua Sci. Technol., vol. 21, no. 6, 581-597, 2016.
[44]
Perera C., Qin Y. R., Estrella J. C., Reiff-Marganiec S., and Vasilakos A. V., Fog computing for sustainable smart cities: A survey, ACM Comput. Surv., vol. 50, no. 3, p. 32, 2017.
[45]
Lu R. X., Heung K., Lashkari A. H., and Ghorbani A. A., A lightweight privacy-preserving data aggregation scheme for fog computing-enhanced IoT, IEEE Access, vol. 5, pp. 3302-3312, 2017.
[46]
3GPP Technical Specification Group Services and System Aspects, System Architecture for the 5G System, Stage 2 (release 15), 3GPP TS 23.501, V0.5.0, June 1, 2017.
DOI
[47]
Anjum A., Abdullah T., Tariq M., Baltaci Y., and Antonopoulos N., Video stream analysis in clouds: An object detection and classification framework for high performance video analytics, IEEE Trans. Cloud Comput., .
[48]
Wang X. F., Chen M., Taleb T., Ksentini A., and Leung V., Cache in the air: Exploiting content caching and delivery techniques for 5G systems, IEEE Commun. Mag., vol. 52, no. 2, pp. 131-139, 2014.
[49]
Satyanarayanan M., Bahl P., Caceres R., and Davies N., The case for VM-based cloudlets in mobile computing, IEEE Pervasive Comput., vol. 8, no. 4, pp. 14-23, 2009.
[50]
Hu W. L., Gao Y., Ha K., Wang J. J., Amos B., Chen Z., Pillai P., and Satyanarayanan M., Quantifying the impact of edge computing on mobile applications, in Proc. 7th ACM SIGOPS Asia-Pacific Workshop on Systems, Hong Kong, China, 2016, pp. 1-8.
[51]
Chen S. Y., Song S. F., Li L. X., and Shen J., Survey on smart grid technology, (in Chinese), Power Syst. Technol., vol. 33, no. 8, pp. 1-7, 2009.
[52]
Yan Y., Qian Y., Sharif H., and Tipper D., A survey on smart grid communication infrastructures: Motivations, requirements and challenges, IEEE Commun. Surv. Tut., vol. 15, no. 1, pp. 5-20, 2013.
[53]
Stojmenovic I., Fog computing: A cloud to the ground support for smart things and machine-to-machine networks, in Proc. Australasian Telecommunication Networks and Applications Conf., Southbank, Australia, 2014, pp. 117-122.
[54]
Simmhan Y., Aman S., Kumbhare A., Liu R. Y., Stevens S., Zhou Q. Z., and Prasanna V., Cloud-based software platform for big data analytics in smart grids, Comput. Sci. Eng., vol. 15, no. 4, pp. 38-47, 2013.
[55]
Su J., Wang D., and Zhang F. F., Review of vehicle networking technology application, (in Chinese), Int. Things Technol., vol. 4, no. 6, pp. 69-72, 2014.
[56]
Kaiwartya O., Abdullah A. H., Cao Y., Altameem A., Prasad M., Lin C. T., and Liu X. L., Internet of vehicles: Motivation, layered architecture, network model, challenges, and future aspects, IEEE Access, vol. 4, pp. 5356-5373, 2016.
[57]
Salahuddin M. A., Al-Fuqaha A., and Guizani M., Software-defined networking for RSU clouds in support of the Internet of vehicles, IEEE Internet Things J., vol. 2, no. 2, pp. 133-144, 2015.
[58]
Lu N., Cheng N., Zhang N., Shen X. M., and Mark J. W., Connected vehicles: Solutions and challenges, IEEE Internet Things J., vol. 1, no. 4, pp. 289-299, 2014.
[59]
Li Z. Y., Shahidehpour M., Bahramirad S., and Khodaei A., Optimizing traffic signal settings in smart cities, IEEE Trans. Smart Grid, vol. 8, no. 5, pp. 2382-2393, 2017.
[60]
Kotevska O., Lbath A., and Bouzefrane S., Toward a real-time framework in cloudlet-based architecture, Tsinghua Sci. Technol., vol. 21, no. 1, pp. 80-88, 2016.
[61]
Liu J. Q., Wan J. F., Zeng B., Wang Q. R., Song H. B., and Qiu M. K., A scalable and quick-response software defined vehicular network assisted by mobile edge computing, IEEE Commun. Mag., vol. 55, no. 7, pp. 94-100, 2017.
[62]
Valavanis K. P. and Vachtsevanos G. J., Handbook of Unmanned Aerial Vehicles. Cham, Switzerland: Springer, 2014.
DOI
[63]
Lin Y. C. and Saripalli S., Sampling-based path planning for UAV collision avoidance, IEEE Trans. Intell. Transp. Syst., .
[64]
Stöcker C., Bennett R., Nex F., Gerke M., and Zevenbergen J., Review of the current state of UAV regulations, Remote Sens., vol. 9, no. 5, p. 459, 2017.
[65]
Motlagh N. H., Bagaa M., and Taleb T., UAV-based IoT platform: A crowd surveillance use case, IEEE Commun. Mag., vol. 55, no. 2, pp. 128-134, 2017.
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 26 September 2017
Accepted: 29 September 2017
Published: 14 December 2017
Issue date: December 2017

Copyright

© The author(s) 2017

Acknowledgements

This work was supported by Tsinghua University Initiative Scientific Research Program (No. 20161080066).

Rights and permissions

Return