Study on Distributed Cloud Computing Environment with Composition Model and Graph Model
Article No.: 96, Pages 1 - 8
Abstract
The conventional centralized cloud has some issues related to timeliness and network congestion. To solve correspondent issues, the conventional cloud is extended in close proximity to a cloud service customer. The researches regarding network edge side are widely studied and still on-going toward the next-generation cloud. Accordingly, this paper illustrates a geographically distributed cloud environment namely the distributed cloud. First, an overview of the distributed cloud is described conceptually and respectfully. Second, composition model and graph model are used to illustrate the distributed cloud. Third, numerical evaluations are performed to verify necessities of the distributed cloud. Lastly, this paper will show possibilities of this research on the conclusion and future works.
References
[1]
ITU-T SG13, Y.3500 (information technology - cloud computing - overview and vocabulary), ITU-T, 2014.
[2]
ITU-T SG13, Y.3502 (information technology - cloud computing - reference architecture), ITU-T, 2014.
[3]
S. Srivastava and S. P. Singh, A survey on latency reduction approaches for performance optimization in cloud computing, in Computational Intelligence & Communication Technology (CICT), 2016 Second Inter- national Conference on. IEEE, 2016, pp. 111--115.
[4]
X. Chen, L. Jiao, W. Li, and X. Fu, Efficient multi-user computation offloading for mobile-edge cloud computing, IEEE/ACM Transactions on Networking, vol. 24, no. 5, pp. 2795--2808, 2016.
[5]
P. Corcoran and S. K. Datta, Mobile-edge computing and the internet of things for consumers: Extending cloud computing and services to the edge of the network, IEEE Consumer Electronics Magazine, vol. 5, no. 4, pp. 73--74, 2016.
[6]
Pathak, A. Wang, C. Huang, A. G. Greenberg, Y. C. Hu, R. Kern, J. Li, and K. W. Ross, Measuring and evaluating tcp splitting for cloud services. in PAM, vol. 10. Springer, 2010, pp. 41--50.
[7]
M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, The case for vm-based cloudlets in mobile computing, IEEE pervasive Computing, vol. 8, no. 4, 2009.
[8]
Y. Cai, F. R. Yu, and S. Bu, Dynamic operations of cloud radio access networks (c-ran) for mobile cloud computing systems, IEEE Transactions on Vehicular Technology, vol. 65, no. 3, pp. 1536--1548, 2016.
[9]
M Díaz, C. Martín, and B. Rubio, State-of-the-art, challenges, and open issues in the integration of internet of things and cloud computing, Journal of Network and Computer Applications, vol. 67, pp. 99--117, 2016.
[10]
D. Wu, X. Liu, S. Hebert, W. Gentzsch, and J. Terpenny, Democra- tizing digital design and manufacturing using high performance cloud computing: Performance evaluation and benchmarking, Journal of Manufacturing Systems, vol. 43, pp. 316--326, 2017.
[11]
P. Garcia Lopez, A. Montresor, D. Epema, A. Datta, T. Higashino, A. Iamnitchi, M. Barcellos, P. Felber, and E. Riviere, Edge-centric computing: Vision and challenges, ACM SIGCOMM Computer Com- munication Review, vol. 45, no. 5, pp. 37--42, 2015.
[12]
B. Varghese, N. Wang, S. Barbhuiya, P. Kilpatrick, and D. S. Nikolopou- los, Challenges and opportunities in edge computing, pp. 20--26, 2016.
[13]
W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, Edge computing: Vision and challenges, IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637--646, 2016.
[14]
S. Yi, Z. Hao, Z. Qin, and Q. Li, Fog computing: Platform and applications, in Hot Topics in Web Systems and Technologies (HotWeb), 2015 Third IEEE Workshop on. IEEE, 2015, pp. 73--78.
[15]
T. H. Luan, L. Gao, Z. Li, Y. Xiang, G. Wei, and L. Sun, Fog computing: Focusing on mobile users at the edge, arXiv preprint arXiv: 1502.01815, 2015.
[16]
V. Dastjerdi, H. Gupta, R. N. Calheiros, S. K. Ghosh, and R. Buyya, Fog computing: Principles, architectures, and applications, arXiv preprint arXiv:1601.02752, 2016.
[17]
F. Bonomi, R. Milito, P. Natarajan, and J. Zhu, Fog computing: A platform for internet of things and analytics, in Big Data and Internet of Things: A Roadmap for Smart Environments. Springer, 2014, pp. 169--186.
[18]
B. Tang, Z. Chen, G. Hefferman, T. Wei, H. He, and Q. Yang, A hierarchical distributed fog computing architecture for big data analysis in smart cities, in Proceedings of the ASE BigData & SocialInformatics 2015. ACM, 2015, p. 28.
[19]
N. Panwar, S. Sharma, and A. K. Singh, A survey on 5g: The next generation of mobile communication, Physical Communication, vol. 18, pp. 64--84, 2016.
[20]
M. T. Beck and M. Maier, Mobile edge computing: Challenges for future virtual network embedding algorithms, 2014.
[21]
Y. C. Hu, M. Patel, D. Sabella, N Sprecher, and V. Young, Mobile edge computing a key technology towards 5g, ETSI White Paper, vol. 11, no. 11, pp. 1--16, 2015.
[22]
P. Khethavath, J. P. Thomas, E. Chan-tin, Towards an efficient distributed cloud computing architecture, Peer-to-Peer Networking and Applications, vol. 10, no. 5, pp. 1152--1168, 2017.
[23]
M, Korling, Distributed cloud infrastructure, Ericsson, 2017.
[24]
J. Hodges, The Distributed Cloud: Automating, Scaling, Securing & Orchestrating the Edge, Heavy Reading White Paper, 2015.
[25]
5G PPP Architecture Working Group, View on 5G Architecture, 2016.
[26]
ITU-T FG IMT-2020, IMT-O-40 (Draft Terms and definitions for IMT- 2020), ITU-T, 2016.
[27]
ITU-T FG IMT-2020, IMT-O-041 (Draft Technical Report: Report on application of network softwarization to IMT-2020), ITU-T, 2016.
[28]
ITU-T FG IMT-2020, IMT-O-043 (Draft Recommendation: Framework of IMT-2020 network architecture), ITU-T, 2016.
[29]
ITU-T FG IMT-2020, IMT-O-045 (Draft Technical Report: FMC architecture based on Unified Network Integrated Cloud), ITU-T, 2016.
[30]
H. Mekky, F. Hao, S. Mukherjee, Z.-L. Zhang, and T. Lakshman, Application-aware data plane processing in sdn, pp. 13--18, 2014.
[31]
S. Brief, Openflow-enabled sdn and network functions virtualization, Open Netw. Found, 2014.
[32]
M.-C. Chuang and J.-F. Lee, Fh-pmipv6: a fast handoff scheme in proxy mobile ipv6 networks, in Consumer Electronics, Communications and Networks (CECNet), 2011 International Conference on. IEEE, 2011, pp. 1297--1300.
[33]
P. R. Egli, PMIPv6, 2010, URL: https://www.slideshare.net/PeterREgli/p-6098167 {accessed: 2017-07-19}
[34]
Alsaffar, M. Aazam, C. S. Hong, and E.-N. Huh, An architecture of iptv service based on pvr-micro data center and pmipv6 in cloud computing, Multimedia Tools and Applications, pp. 1--34, 2016.
[35]
Y. Bi, H. Zhou, W. Xu, X. S. Shen, and H. Zhao, An efficient pmipv6- based handoff scheme for urban vehicular networks, IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 12, pp. 3613--3628, 2016.
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- Study on Distributed Cloud Computing Environment with Composition Model and Graph Model
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Published In
January 2018
628 pages
ISBN:9781450363853
DOI:10.1145/3164541
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- SKKU: SUNGKYUNKWAN UNIVERSITY
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Published: 05 January 2018
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IMCOM '18
IMCOM '18: The 12th International Conference on Ubiquitous Information Management and Communication
January 5 - 7, 2018
Langkawi, Malaysia
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IMCOM '18 Paper Acceptance Rate 100 of 255 submissions, 39%;
Overall Acceptance Rate 213 of 621 submissions, 34%
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