A Survey on Emerging Computing Paradigms for Big Data

ZHANG Yaoxue; REN Ju; LIU Jiagang; XU Chugui; GUO Hui; LIU Yaping

doi:10.1049/cje.2016.11.016

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ZHANG Yaoxue, REN Ju, LIU Jiagang, XU Chugui, GUO Hui, LIU Yaping. A Survey on Emerging Computing Paradigms for Big Data[J]. Chinese Journal of Electronics, 2017, 26(1): 1-12. doi: 10.1049/cje.2016.11.016

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ZHANG Yaoxue, REN Ju, LIU Jiagang, XU Chugui, GUO Hui, LIU Yaping. A Survey on Emerging Computing Paradigms for Big Data[J]. Chinese Journal of Electronics, 2017, 26(1): 1-12. doi: 10.1049/cje.2016.11.016

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A Survey on Emerging Computing Paradigms for Big Data

doi: 10.1049/cje.2016.11.016

1.
School of Information Science and Engineering, Central South University, Changsha 410083, China;
2.
Research Institute of Information Security and Big Data, Central South University, Changsha 410083, China

Funds: This work is supported by the Project of Innovation-driven Plan of Central South University, the China Hunan Provincial Science & Technology Program (No.2012GK4106), and International Science & Technology Cooperation Program of China (No.2013DFB10070).

More Information

Corresponding author: REN Ju (corresponding author) received the B.S. (2009), M.S. (2012), and Ph.D. (2016) degrees, all in computer science from Central South University (CSU), China. From 2013 to 2015, he was a visiting Ph.D. student at the Department of Electrical and Computer Engineering, University of Waterloo, Canada. Since 2016, he has been a distinguished professor with the School of Information Science and Engineering at CSU. His research interests include Internet-of-things, wireless communication, transparent computing and cloud computing. (Email:renju@csu.edu.cn)
Received Date: 2016-10-18
Rev Recd Date: 2016-10-28
Publish Date: 2017-01-10

Abstract

Abstract

The explosive growth of data volume and the ever-increasing demands of data value extraction have driven us into the era of big data. The "5V" (Variety, Velocity, Volume, Value, and Veracity) characteristics of big data pose great challenges to traditional computing paradigms and motivate the emergence of new solutions. Cloud computing is one of the representative technologies that can perform massive-scale and complex data computing by taking advantages of virtualized resources, parallel processing and data service integration with scalable data storage. However, as we are also experiencing the revolution of Internet-of-things (IoT), the limitations of cloud computing on supporting lightweight end devices significantly impede the flourish of cloud computing at the intersection of big data and IoT era. It also promotes the urgency of proposing new computing paradigms. We provide an overview on the topic of big data, and a comprehensive survey on how cloud computing as well as its related technologies can address the challenges arisen by big data. Then, we analyze the disadvantages of cloud computing when big data encounters IoT, and introduce two promising computing paradigms, including fog computing and transparent computing, to support the big data services of IoT. Finally, some open challenges and future directions are summarized to foster continued research efforts into this evolving field of study.
- Computing paradigm,
- Big data,
- Cloud computing,
- Transparent computing,
- Fog computing,
- Int-ernet-of-things (IoT)

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References(68)

References

R. Villars, C. Olofson and M. Eastwood, "Big data:What it is and why you should care", White Paper, IDC, MA, USA, 2011.

S. Kaisler, F. Armour, J.A. Espinosa, et al., "Big data:Issues and challenges moving forward", Proc. of the 46th Hawaii International Conference on System Sciences (HICSS), IEEE, pp.995-1004, 2013.

O. Leary and E. Daniel, "Artificial intelligence and big data", IEEE Intelligent Systems, Vol.2, No.28, pp.96-99, 2013.

L. Chih-Wei, H. Chih-Ming, C. Chih-Hung, et al., "An improvement to data service in cloud computing with content sensitive transaction analysis and adaptation", Proc. of IEEE 37th Annual Conference Workshops Computer Software and Applications, pp.463-468, 2013.

L. Chang, R. Ranjan, Z. Xuyun, et al., "Public auditing for big data storage in cloud computing:A survey", Proc. of IEEE 16th International Conference on Computational Science and Engineering (CSE), pp.1128-1135, 2013.

T. Ewaryst and A. Kapczynski, Internet-Technical Development and Applications, Springer Science and Business Media, 2009.

C. Stephanie and H. Quentin, "Attention Shoppers:Store is Tracking Your Cell", New York Times, July 14, 2013.

"Unlocking game-changing wireless capabilities:Cisco and SITA help copenhagen airport develop new services for cransforming the passenger experience customer case study CISCO", available at http://www.cisco.com/en/US/prod/collateral/wireless, 2012-12-01.

M. Chen, S. Mao and Y. Liu, "Big data:A survey", Mobile Networks and Applications, Vol.19, No.2, pp.171-209, 2014.

A. McAfee, E. Brynjolfsson, T. Davenport, et al., "Big data:The management revolution", Harvard Bus. Rev., Vol.90, No.10, pp.61-67, 2012.

B. Franks, Taming the Big Data Tidal Wave:Finding Opportunities in Huge Data Streams with Advanced Analytics, John Wiley and Sons, 2012.

M.A. Beyer and D. Laney, "The importance of big data:A definition", Gartner, 2012-06-21, Retrieved from https://www.gartner.com/id=2057415, 2013-02-19.

Z. Paul, K. Parasuraman, T. Deutsch, et al., Harness the Power of Big Bata the IBM Big Data Platform, McGraw Hill Professional, 2012.

J. Berman, Principles of Big Data:Preparing, Sharing, and Analyzing Complex Information, Newnes, 2013.

M. Hilbert, "Big data for development:A review of promises and challenges", Development Policy Review, Vol.34, No.1, 135-174, 2016.

D.E. OLeary, "Artificial intelligence and big data", IEEE Intell. Syst, Vol.2, No.28, pp.96-99, 2013.

K. Avita, W. Mohammad and RH. Goudar, "Big data:Issues, challenges, tools and good practices", Proc. of Sixth International Conference on Contemporary Computing (IC3), Noida, India, pp.404-409, 2013.

C. Chen and C. Zhang, "Data-intensive applications, challenges, techniques and technologies:A survey on big data", Information Sciences, Vol.275, No.8, pp.314-347, 2014.

P. Russom, "Big data analytics", TDWI Best Practices Report, pp.1-35, 2011.

S. Chaudhuri, "What next?:A half-dozen data management research goals for big data and the cloud", Proc. of the 31st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, Scottsdale, AZ, USA, pp.1-4, 2012.

S. Meng and L. Liu, "Enhanced monitoring-as-a-service for effective cloud management", IEEE Transactions on Computers, Vol.62, No.9, pp.1705-1720, 2013.

A Rabkin and R.H. Katz, "Chukwa:A system for reliable large-scale log collection", Proc. of International Conference on Large Installation System Administration, USENIX Association, Vol.10, pp.1-15, 2010.

A.N. Toosi, R.N. Calheiros and R. Buyya, "Interconnected cloud computing environments:Challenges, taxonomy, and survey", ACM Computing Surveys (CSUR), Vol.47, No.1, page.7, 2014.

C.W. Lu, C.M. Hsieh, C.H. Chang, et al., "An improvement to data service in cloud computing with content sensitive transaction analysis and adaptation", Proc. of IEEE 37th Annual Conference Workshops Computer Software and Applications (COMPSACW), pp.463-468, 2013.

M. Dong, H. Li, K. Ota, et al., "Multicloud-based evacuation services for emergency management". IEEE Cloud Computing, Vol.1, No.4, pp.50-59, 2014.

I. Foster, Y. Zhao, I. Raicu, et al., "Cloud computing and grid computing 360-degree compared", Proc. of Workshop on Grid Computing Environments, pp.1-10, 2008.

W.T. TSAI, X.Y. BAI and Y. HUANG, "Software-as-a-service (SaaS):Perspectives and challenges", Science China Information Sciences, Vol.57, No.5, pp.1-15, 2014.

D. Talia, "Clouds for scalable big data analytics",Computer, Vol.46, No.5, pp.98-101, 2013.

A. Fernandez, S.D. Rio, V. Lopez, et al., "Big Data with cloud computing:An insight on the computing environment, MapReduce, and programming frameworks", Wiley Interdisciplinary Reviews:Data Mining and Knowledge Discovery, Vol.4, No.5, pp.380-409, 2014.

S. Ghemawat, H. Gobioff and S.T. Leung, "The Google file system", Proc. of ACM SIGOPS Operating Systems Review, New York, USA, Vol.37, No.5, pp.29-43, 2003.

T. White, Hadoop:The Definitive Guide, O'Reilly Media, Sebastapol, CA, 2009.

J. Han, E. Hailong, G. Le, et al., "Survey on NoSQL database", Proc. of 6th International Conference on Pervasive Computing and Applications (ICPCA), Port Elizabeth, South Africa, pp.363-366, 2011.

F. Chang, J. Dean, S. Ghemawat, et al., "Bigtable:A distributed storage system for structured data", ACM Transactions on Computer Systems (TOCS), Vol.26, No.2, pp.1-26, 2008.

G. DeCandia, D. Hastorun, M Jampani, et al., "Dynamo:Amazon's highly available key-value store", ACM SIGOPS Operating Systems Review, Vol.41, No.6, pp.205-220, 2007.

D. Karger, E. Lehman, T. Leighton, et al., "Consistent hashing and random trees:Distributed caching protocols for relieving hot spots on the World Wide Web", Proc. of the Twenty-ninth Annual ACM Symposium on Theory of Computing, New York, pp.654-663, 1997.

N. Dimiduk, A. Khurana, M. Ryan, et al., HBase in Action, Manning Shelter Island, 2012.

A. Lakshman and P. Malik, "Cassandra:A decentralized structured storage system", ACM SIGOPS Operating Systems Review, Vol.44, No.2, pp.35-40, 2010.

J Russell and R. Cohn, Hypertable, 1st ed., Key Biscayne, FL, (USA):Bookvika Publishing, 2012.

K Chodorow and M Dirolf, MongoDB:The Definitive Guide Powerful and Scalable Data Storage. 1st ed., Sebastopol, CA:O-Reilly, 2010.

R.M. Chang, R.J. Kauffman and Y.O. Kwon, "Understanding the paradigm shift to computational social science in the presence of big data", Decision Support Systems, Vol.63, No.7, pp.67-80, 2014.

J. Chen, Y. Chen, X. Du, et al., "Big data challenge:A data management perspective", Frontiers of Computer Science, Vol.7, No.2, pp.157-164, 2013.

H. Li, M. Dong, K. Ota, et al., "Pricing and repurchasing for big data processing in multi-clouds", IEEE Transactions on Emerging Topics in Computing, Vol.4, No.2, pp.266-277, 2016.

X. Meng and X. Ci, "Big data management:Concepts, techniques and challenges", Journal of Computer Research and Development, Vol.50, No.1, pp.146-169, 2013.

J. Dean and S. Ghemawat, "MapReduce:Simplified data processing on large clusters", Communications of the ACM, Vol.51, No.1, pp.107-113, 2008.

M. Zaharia, M. Chowdhury, M.J. Franklin, et al., "Spark:Cluster computing with working sets", Proc.of HotCloud, Boston, MA, pp.1-7, 2010.

M. Zaharia, T. Das, H. Li, et al., "Discretized streams:Faulttolerant streaming computation at scale", Proc. of 24th ACM Symposium on Operating Systems Principles (SOSP), Farmington, PA, pp. 423-438, 2013.

A. Thusoo, J.S. Sarma, N. Jain, et al., "Hive:A warehousing solution over a map-reduce framework", Proc. of the VLDB Endowment, Vol.2, No.2, pp.1626-1629, 2009.

C. Olston, B. Reed, U. Srivastava, et al., "Pig latin:A not-soforeign language for data processing", Proc. of ACM SIGMOD International Conference on Management of Data, Vancouver, Canada, pp.1099-1110, 2008.

K.S. Beyer, V. Ercegovac, R. Gemulla, et al., "Jaql:A scripting language for large scale semistructured data analysis", Proc. of Conference on Very Large Database, pp.1272-1283, 2011.

M. Chiang and T. Zhang, "Fog and IoT:An overview of research opportunities ", IEEE Internet of Things Journal, 2016.

M. Weiner, M. Jorgovanovic, A. Sahai, et al., " Design of a low-Latency, high-reliability wireless communication system for control applications ", Proc. of IEEE International Conference on Communications (ICC), pp.3829-3835, 2014.

R. Kelly, "Internet of things data to top 1.6 Zettabytes by 2022", available at https://campustechnology.com/articles/2015/04/15/internet-of-things-data-to-top-1-6-zettabytes-by-2020.aspx, 2016-4-7.

F. Bonomi, R. Milito, J. Zhu, et al., " Fog computing and its role in the Internet of things ", Proc. of Workshop on Mobile Cloud Computing, pp.13-16, 2012.

Y. Shanhe, C. Li, Q Li, "A survey of fog computing:Concepts, applications and issues", Proc. of the 2015 Workshop on Mobile Big Data, ACM, pp.37-42, 2015.

Cisco, "Iox overview", available at http://goo.gl/n2mfiw, 2015-6-18.

H.T. Dinh, C.L. Lee, D. Niyato, et al. "A survey of mobile cloud computing:Architecture, applications, and approaches", Proc. of Wireless Communications and Mobile Computing, pp.1587-1611, 2013.

T. Beck, S. Feld, L. Claudia, et al., "Mobile-edge computing", Informatik-Spektrum, Vol.39, No.2, pp.108-114, 2016.

M. Satyanarayanan, P. Bahl, R. Caceres, et al.," The case for vm-based cloudlets in mobile computing", IEEE Pervasive Computing, Vol.8, No.4, pp.14-23, 2009.

D. F. Willis, A. Dasgupta and S. Banerjee, " Paradrop:A multitenant platform for dynamically installed third party services on home gateways", Proc. of ACM SIGCOMM Workshop on Distributed Cloud Computing, pp.43-44, 2014.

Y. Zhang, "Transparence computing:Concept, architecture and example", Chinese Journal of Electronics, Vol.32, No.12, pp.169-173, 2004.

Y. Zhang and Y. Zhou, "4VP:A novel meta OS approach for streaming programs in ubiquitous computing", Proc. of IEEE 21st International Conference on Advanced Information Networking and Applications (AINA 2007), Niagara Falls, Canada, pp.394-403, 2007.

Y. Zhou, Y. Zhang and Y. Xie, "TransCom:A virtual diskbased cloud computing platform for heterogeneous services", IEEE Transactions on Network and Service Management, Vol.11, No.1, pp.46-59, 2014

Y. Zhang and Y. Zhou, "TransOS:A transparent computingbased operating system for the cloud", Int. J. Cloud Computing, Vol.1, No.4, pp.287-301, 2012.

J. Liu, Y. Zhou and D. Zhang, "TranSim:A simulation framework for cache-enabled transparent computing systems", IEEE Transactions on Computers, Vol.65, No.10, pp.3171-3183, 2016.

J. Ren, Y. Zhang and J. Chen, "Analysis on the scheduling problem in transparent computing", Proc. of IEEE HPCC, Zhangjiajie, China, pp.13-15, 2013.

M. Nick, A. Tom, B. Hari, et al., "OpenFlow:Enabling innovation in campus networks", ACM SIGCOMM Computer Communication Review, Vol.38, No.2, pp.69-77, 2008.

J. Ren, Y. Zhang, N. Zhang, et al., "Dynamic channel access to improve energy efficiency in cognitive radio sensor networks", IEEE Transactions on Wireless Communications, Vol.15, No.5, pp.3143-3156, 2016.

J. Ren, Y. Zhang, K. Zhang, et al., "Lifetime and energy hole evolution analysis in data-gathering wireless sensor networks", IEEE Transactions on Industrial Informatics, Vol.12, No.2, pp.788-800, 2016.

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