Cloud reliability enhancement mechanisms: A Survey

Vikas Mangotra¹ , Richa Dogra²

Section:Survey Paper, Product Type: Isroset-Journal
Vol.6 , Issue.3 , pp.31-34, Jun-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i3.3134

Online published on Jun 30, 2018

Copyright © Vikas Mangotra, Richa Dogra . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

502 Views    355 Downloads    121 Downloads

Abstract :
In today’s era cloud computing becomes the hottest topic due to its ability to reduce the cost associated with computing. Cloud computing provides the on demand services like storage, servers, resources etc. to the users without physically acquiring them and the payment is according to pay per use. Since cloud provides the storage, reduces the managing cost and time for organization to the user but security and confidentiality becomes the one of the biggest obstacle in front of us. The major problem with cloud environment is, the number of user is uploading their data on cloud storage so sometimes due to lack of security there may be chances of loss of confidentiality. To overcome these obstacles a third party is required to prevent data, data encryption, and integrity and control unauthorized access for data storage to the cloud. To optimize better results we will review some paper and find the better results to remove the security barriers.

Key-Words / Index Term :
Cloud Computing, security, and confidentiality

References :
[1] GX. Yu, “Intelligent Urban Traffic Management System Based on Cloud Computing and Internet of Things,” pp. 2169–2172, 2012.
[2] B. Mills, T. Znati, and R. Melhem, “Shadow Computing: An energy-aware fault tolerant computing model,” 2014 Int. Conf. Comput. Netw. Commun., pp. 73–77, 2014.
[3] V. M. Sivagami, “Survey on Fault Tolerance Techniques in Cloud Computing Environment,” no. 9, pp. 419–425, 2015.
[4] R. Jhawar, V. Piuri, and M. Santambrogio, “A Comprehensive Conceptual System-Level Approach to Fault Tolerance in Cloud Computing,” pp. 0–4, 2012.
[5] C. A. Chen, M. Won, R. Stoleru, and G. G. Xie, “Energy-efficient fault-tolerant data storage and processing in mobile cloud,” IEEE Trans. Cloud Comput., vol. 3, no. 1, pp. 28–41, 2015.
[6] S. S. Lakshmi, “Fault Tolerance in Cloud Computing,” vol. 04, no. 01, pp. 1285–1288, 2013.
[7] R. Buyya, C. S. Yeo, and S. Venugopal, “Market-oriented cloud computing: Vision, hype, and reality for delivering IT services as computing utilities,” Proc. - 10th IEEE Int. Conf. High Perform. Comput. Commun. HPCC 2008, pp. 5–13, 2008.
[8] Z. Xiao, W. Song, and Q. Chen, “Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment,” IEEE Trans. Parallel Distrib. Syst., vol. 24, no. 6, pp. 1107–1117, Jun. 2013.
[9] U. Wajid, C. Cappiello, P. Plebani, B. Pernici, N. Mehandjiev, M. Vitali, M. Gienger, K. Kavoussanakis, D. Margery, D. G. Perez, and P. Sampaio, “On Achieving Energy Efficiency and Reducing CO 2 Footprint in Cloud Computing,” vol. 7161, no. c, 2015.
[10] Y. Xie, H. Wen, B. Wu, Y. Jiang, and J. Meng, “Transactions on Cloud Computing,” vol. 13, no. 9, 2015.
[11] D. Ardagna, G. Casale, M. Ciavotta, J. F. PĂ©rez, and W. Wang, “Quality-of-service in cloud computing : modeling techniques and their applications,” pp. 1–17, 2014.
[12] M. Armbrust, I. Stoica, M. Zaharia, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, and A. Rabkin, “A view of cloud computing,” Commun. ACM, vol. 53, no. 4, p. 50, 2010.
[13] S. Saha, S. Pal, and P. K. Pattnaik, “A Novel Scheduling Algorithm for Cloud Computing Environment,” vol. 1, 2016.
[14] A. Farahzadi, P. Shams, J. Rezazadeh, and R. Farahbakhsh, “Middleware technologies for cloud of things-a survey ☆,” Digit. Commun. Networks, no. April, pp. 1–13, 2017.
[15] J. P. D. Comput, B. Javadi, J. Abawajy, and R. Buyya, “Failure-aware resource provisioning for hybrid Cloud infrastructure,” J. Parallel Distrib. Comput., vol. 72, no. 10, pp. 1318–1331, 2012.
[16] J. Mohammed, C.-H. Lung, A. Ocneanu, A. Thakral, C. Jones, and A. Adler, “Internet of Things: Remote Patient Monitoring Using Web Services and Cloud Computing,” in 2014 IEEE International Conference on Internet of Things(iThings), and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom), 2014, pp. 256–263.
[17] X. Cui, B. Mills, T. Znati, and R. Melhem, “Shadow replication: An energy-aware, fault-tolerant computational model for green cloud computing,” Energies, vol. 7, no. 8, pp. 5151–5176, 2014.
[18] D. Boru, D. Kliazovich, F. Granelli, P. Bouvry, and A. Y. Zomaya, “Energy-efficient data replication in cloud computing datacenters,” Globecom Work. (GC Wkshps), 2013 IEEE, pp. 446–451, 2013.
[19] C. Wang, N. Cao, J. Li, K. Ren, and W. Lou, “Secure Ranked Keyword Search over Encrypted Cloud Data,” in 2010 IEEE 30th International Conference on Distributed Computing Systems, 2010, pp. 253–262.
[20] H. Wang, Z. Kang, and L. Wang, “Performance-Aware Cloud Resource Allocation via Fitness-Enabled Auction,” IEEE Trans. Parallel Distrib. Syst., vol. 27, no. 4, pp. 1160–1173, Apr. 2016.
[21] [21] Z. Amin, “Review on Fault Tolerance Techniques in Cloud Computing,” vol. 116, no. 18, pp. 11–17, 2015.
[22] P. Zhang, S. Hu, J. He, Y. Zhang, G. Huang, and J. Zhang, “Building cloud-based healthcare data mining services,” Proc. - 2016 IEEE Int. Conf. Serv. Comput. SCC 2016, pp. 459–466, 2016.
[23] J. Abawajy, S. Member, M. Chowdhury, and A. Kelarev, “Hybrid Consensus Pruning of Ensemble Classifiers for Big Data Malware Detection,” vol. 3, no. 2, pp. 1–11, 2015.