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Optimized Replication Management with Reputation for Detecting Collusion in Large Scale Cloud Systems

by Hanane Bennasar, Mohammad Essaaidi, Ahmed Bendahmane, Jalel Ben-othman
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 30
Year of Publication: 2019
Authors: Hanane Bennasar, Mohammad Essaaidi, Ahmed Bendahmane, Jalel Ben-othman
10.5120/ijca2019919186

Hanane Bennasar, Mohammad Essaaidi, Ahmed Bendahmane, Jalel Ben-othman . Optimized Replication Management with Reputation for Detecting Collusion in Large Scale Cloud Systems. International Journal of Computer Applications. 178, 30 ( Jul 2019), 28-35. DOI=10.5120/ijca2019919186

@article{ 10.5120/ijca2019919186,
author = { Hanane Bennasar, Mohammad Essaaidi, Ahmed Bendahmane, Jalel Ben-othman },
title = { Optimized Replication Management with Reputation for Detecting Collusion in Large Scale Cloud Systems },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2019 },
volume = { 178 },
number = { 30 },
month = { Jul },
year = { 2019 },
issn = { 0975-8887 },
pages = { 28-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number30/30729-2019919186/ },
doi = { 10.5120/ijca2019919186 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:51:52.814826+05:30
%A Hanane Bennasar
%A Mohammad Essaaidi
%A Ahmed Bendahmane
%A Jalel Ben-othman
%T Optimized Replication Management with Reputation for Detecting Collusion in Large Scale Cloud Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 30
%P 28-35
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Several cloud computing systems used voting techniques to deal with sabotage issues. However, these techniques become inefficient, and present some new security vulnerabilities when malicious resources collude and return the same wrong result. Usually, this kind of security threats are handled using several techniques and approaches such as voting techniques. In this paper, a very efficient approach to overcome sabotage issues is proposed, especially in the case of very complex attacks. The performances of this approach are evaluated in a cloud system model and it is compared against other voting techniques, like reputation-based voting, using simulations which allowed to investigate the effect of collusive cloud resources on the correctness of the results. The obtained results show that the proposed approach achieves lower error rates and enhanced performances in terms of overhead and slowdown.

References
  1. Z. Lei, C. Zuo-Ning, “MapReduce Implementation Based on vStarCloud”, Journal of Advances in Computer Network, Vol. 1, No. 3, September 2013,pp 162-167.
  2. S.Zhao , V.Lo, “Result Verification and Trust-based Scheduling”, Fifth IEEE International Conference on Peer-to peer Computing, IEEE Computer Society, Washington, pp. 31–38, 2005.
  3. C. Kumar, “Optimization of Data Storage and Fault Tolerance Strategies in Cloud Computing”, International Journal of Science and Research (IJSR), 6.391 Volume 6, January 2017, pp 1320- 1324.
  4. A. Bendahmane, M. Essaaidi. “The Effectiveness of Reputation-based Voting for Collusion Tolerance in Large-Scale Grids”, IEEE Transactions on Dependable and Secure omputing, Computing, 12, 6, (665), (2015).
  5. W.Jiang, H.Wan, S.Zhao, Reputation Concerns of Independent Directors: Evidence from Individual Director Voting, The Review of Financial Studies 29 (3), 655-696 , 2015.
  6. S.Choi, H.Kim, A Taxonomy of Desktop Grid Systems Focusing on Scheduling,Columbia Business school Reaserch 2015.
  7. K. Watanabe, M. Fukushi, and S. Horiguchi, “Collusion-Resistant Sabotage-Tolerance Mechanisms for Volunteer Computing Systems,” IEEE International Conference on e-Business Engineering, Macau, pp. 213 –218, 2009.
  8. K. Watanabe, M.Fukushi, Optimal Spot-checking for Computation Time Minimization in Volunteer Computing,Springer, Graduate School of Information SciencesTohoku UniversityAoba-ku SendaiJapan, Grid Computing 7: 575.2009.
  9. L. F. G. Sarmenta, “Sabotage-tolerance mechanisms for volunteer computing systems,”Future Generation Computer Systems, 18(4 Future Gener. Comput. Syst. 18(4), 561–572 (2002) .
  10. G.Levitin, L.Xing,”Optimal Spot-Checking for Collusion Tolerance in Computer Grids”, IEEE Transactions on Dependable and Secure Computing, doi:10.1109/TDSC.2017.2690293, 2017.
  11. Z.Zhu,R. Jiang, “A Secure Anti Collusion Data Sharing Scheme for Dynamic Groups in the Cloud”, IEEE Transactions on Parallel and Distributed Systems, VOL. 27, NO. 1, JANUARY 2016.
  12. M. Mortazavi, B. Ladani A MapReduce-based algorithm for parallelizing collusion detection in Hadoop, IEEE International Conference, 145-154, 2015.
  13. T.Samuel, A.Nizar,”Credibility-Based Result Verification for Map-Reduce”, India Conference (INDICON), 2014 Annual IEEE, pp. 1–6,2014.
  14. K. JIJI, A.Nizar, “An Efficient Approach for MapReduce Result Verification”, Springer Science+Business Media Singapore, volume 412 2016.
  15. M.Haberkorn, K.Trivedi, “Availability Monitor for a Software Based System”,IEEEXplore Conference: High Assurance Systems Engineering Symposium, 2007. HASE '07. 10th IEEE, 2007.
  16. M.Durrani ,A. Shamsi,” Volunteer computing: requirements, challenges, and solutions”, Journal of Network and Computer Applications archive Volume 39, March, 2014.
  17. M.Siebenhaar, O.Wenge, “Verifying the Availability of Cloud Applications”, pp. 489-494, 2013.
  18. Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, Cesar A. F. De Rose, and Rajkumar Buyya, CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms, Software: Practice and Experience (SPE), Volume 41, Number 1, Pages: 23-50, ISSN: 0038-0644, Wiley Press, New York, USA, January, 2011.
  19. K.Watanabe, M.Fukushi,” Adaptive group-based job scheduling for high performance and reliable volunteer computing”. Journal of Information Processing, Volume 19, 2011.
  20. B. Ismail, D. Jagadisan, “Determining Overhead, Variance & Isolation Metrics in Virtualization for IaaS Cloud”, Springer Science+Business Media, LLC , Pages 315-330, 2011.
  21. Kukanov, Alexey (2008-03-04). "Why a simple test can get parallel slowdown". Retrieved 2015-02-15
  22. D. Kondo, F. Araujo, P. Malecot, P. Domingues, L. M. Silva, G. Fedak, and F. Cappello, “Characterizing result errors in internet desktop grids,“ In Euro-Par 2007, Parallel Processing, 13th International Euro- Par Conference, France, Proceedings, volume 4641 of LNCS, pp. 361 371.Springer, 2007.
  23. M. Kuhn, S. Schmid, and R. Wattenhofer, “Distributed asymmetric verification in computational grids,” in IEEE International Symposium on Parallel and Distributed Processing, p.1-10, 2008.
  24. A.Quamar, A.Deshpande, J.Lin, “NScale: Neighborhood-centric Large-Scale Graph Analytics in the Cloud”, VLDB J., vol. 25, no. 2, pp. 125–150, 2016..
  25. J. Rittinghouse, J.Ransome, “Cloud computing: implementation, management, and security”, CRC Press, Boca Raton, 2016.
  26. N.Bonvin, T.Papaioannou, K.Aber,” A self-organized, fault-tolerant and scalable replication scheme for cloud storage”. In Proc. the 1st ACM Symposium on Cloud Computing, Indianapolis, IN, USA, June 10-11, 2010, pp.205-216.
  27. L. C. Canon, E. Jeannot, and J. Weissman, “A scheduling and certification algorithm for defeating collusion in desktop grids,” In International Conference on Distributed Computing Systems, pp. 343–352, 2011.
  28. Jyothsna V., Rama Prasad V.V. FCAAIS: Anomaly based network intrusion detection through feature correlation analysis and association impact scale ICT Express, 2016, pp. 103-116.
  29. Mapper API for Google AppEngine. http://googleappengine.blogspot.com /2010/07/introducing-mapper-api.html (site visited January 2016).
  30. Y. Chen, V. Paxson, and R. Katz. 2010. What's New About Cloud Computing Security. Technical Report UCB/EECS-2010-5, Berkeley,2014, pp 20-29.
  31. A. Matsunaga, M. Tsugawa, and J. Fortes. Cloudblast: Combining mapreduce and virtualization on distributed resources for bioinformatics. Microsoft eScience Workshop, 2008, pp 222-229.
Index Terms

Computer Science
Information Sciences

Keywords

Cloud computing Cloud model Voting Collusion Attacks

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