Skip to main content
SpringerLink
Log in

A CSO-based approach for secure data replication in cloud computing environment

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Cloud computing has a significant impact on information technology solutions for both organizations and researchers. Different users share critical data over the cloud where failures are normal rather than exceptional. Therefore, data fragmentation and data replication algorithms are useful to enhance data security. Three important questions need to be answered carefully: (1) Which files should be replicated; (2) how many appropriate new replicas should be placed; (3) where the new replicas should be stored. In this paper, we propose a CSO-based approach for secure data replication (SDR) that determines suitable data center for new replica by designing a smart fuzzy inference system with four inputs as centrality, energy, storage usage, and load. In addition, a high-quality knowledge base is designed to describe the fuzzy system of CSO algorithm. To obtain a higher level of security, we partition each popular file into several fragments with different sizes based on the ability of data centers. Then, these fragments are stored based on the T-coloring concept to prevent an attacker from determining the locations of the fragments. Consequently, SDR protects the data file without any encryption technique since each data center has a single fragment of a particular file and no meaningful data are achieved in a successful attack. We evaluate the proposed algorithm with CloudSim toolkit, and the experiments show that SDR strategy can reduce the total energy consumption and response time by 31% and 28% (on average) compared to other related algorithms, respectively. In terms of storage usage, effective network usage, hit ratio, mean latency, load variance, number of replications, efficiency, and bandwidth consumption, the obtained results indicate that our strategy outperforms previous replication methods by a significant margin. The main reason is that SDR successfully balances the trade-offs among objectives by the fuzzy system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27

Similar content being viewed by others

References

  1. Wei J, Zeng X (2019) Optimal computing resource allocation algorithm in cloud computing based on hybrid differential parallel scheduling. Clust Comput 22:7577–7583

    Article  Google Scholar 

  2. Singh Gill S, Ouyang X, Garraghan P (2020) Tails in the cloud: a survey and taxonomy of straggler management within large-scale cloud data centres. J Supercomput 76:10050–10089

    Article  Google Scholar 

  3. AliKhan A, Zakarya M, Khan R (2019) Energy-aware dynamic resource management in elastic cloud datacenters. Simul Model Pract Theory 92:82–99

    Article  Google Scholar 

  4. Mansouri N, Javidi MM (2020) A review of data replication based on meta-heuristics approach in cloud computing and data grid. Soft Comput 24:14503–14530

    Article  Google Scholar 

  5. Mansouri N, Javidi MM (2018a) A hybrid data replication strategy with fuzzy-based deletion for heterogeneous cloud data centers. J Supercomput 74(10):5349–5372

    Article  Google Scholar 

  6. Liang B, Dong X, Wang Y, Zhang X (2020) Memory-aware resource management algorithm for low-energy cloud data centers. Future Gener Comput Syst 113:329–342

    Article  Google Scholar 

  7. Ardagna D, Panicucci B, Trubian M, Zhang L (2012) Energy-aware autonomic resource allocation in multitier virtualized environments. IEEE Trans Serv Comput 5‌(1):2–19

  8. Kelefouras V, Djemame K (2018) Workflow simulation aware and multi-threading effective task scheduling for heterogeneous computing. In: 25th International Conference on High Performance Computing (HiPC)

  9. Mansouri N (2016) QDR: a QoS-aware data replication algorithm for Data Grids considering security factors. Clust Comput 19(3):1071–1087

    Article  Google Scholar 

  10. Kang S, Veeravalli B, Aung KMM (2014) ESPRESSO: an encryption as a service for cloud storage systems. In: AIMS 2014, Brno, Czech Republic, pp 15–28

  11. Bhattacherjee S, Das R, Khatua S, Roy S (2020) Energy-efficient migration techniques for cloud environment: a step toward green computing. J Supercomput 76:5192–5220

    Article  Google Scholar 

  12. Mansouri N (2014) Network and data location aware approach for simultaneous job scheduling and data replication in large-scale data grid environments. Front Comput Sci 8:391–408

    Article  MathSciNet  Google Scholar 

  13. Mansouri N, Ghafari R, Mohammad Hasani Zade B (2020) Cloud computing simulators: a comprehensive review. Simul Model Pract Theory 104:102144

    Article  Google Scholar 

  14. Li C, Zhang J, Tang H (2019) Replica-aware task scheduling and load balanced cache placement for delay reduction in multi-cloud environment. J Supercomput 75:2805–2836

    Article  Google Scholar 

  15. Mansouri N, Mohammad Hasani Zade B, Javidi MM (2020) A multi-objective optimized replication using fuzzy based self-defense algorithm for cloud computing. J Netw Comput Appl 171:102811

    Article  Google Scholar 

  16. Long SQ, Zhao YL, Chen W (2014) MORM: a multi-objective optimized replication management strategy for cloud storage cluster. J Syst Architect 60:234–244

    Article  Google Scholar 

  17. Boru D, Kliazovich D, Granelli F, Bouvry P, Zomaya AY (2015) Energy-efficient data replication in cloud computing datacenters. Clust Comput 18:385–402

    Article  Google Scholar 

  18. Kliazovich D, Bouvry P, Khan SU (2012) GreenCloud: a packet-level simulator of energy-aware cloud computing data centers. J Supercomput 62(3):1263–1283

    Article  Google Scholar 

  19. Casas I, Taheri J, Ranjan R, Wang L, Zomaya AY (2017) A balanced scheduler with data reuse and replication for scientific workflows in cloud computing. Future Gener Comput Syst 74:1689–2178

    Article  Google Scholar 

  20. Manjula S, Indra Devi M, Swathiya R (2016) Division of data in cloud environment for secure data storage. In: International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE)

  21. Nivetha NK, Vijayakumar D (2016) Modeling fuzzy based replication strategy to improve data availability in cloud datacenter. In: International Conference on Computing Technologies and Intelligent Data Engineering

  22. Tos U, Mokadem R, Hameurlain A, Ayav T, Bora S (2016) A performance and profit oriented data replication strategy for cloud systems. In: International Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress, pp 780–787

  23. Mansouri N, Kuchaki Rafsanjani M, Javidi MM (2017) DPRS: a dynamic popularity aware replication strategy with parallel download scheme in cloud environments. Simul Model Pract Theory 77:177–196

    Article  Google Scholar 

  24. Li B, Song SL, Bezakova I, Cameron KW (2013) EDR: an energy-aware runtime load distribution system for data-intensive applications in the cloud. In: IEEE International Conference on Cluster Computing, pp 1–8

  25. Limam S, Mokadem R, Belalem G (2019) Data replication strategy with satisfaction of availability, performance and tenant budget requirements. Clust Comput 22:1–12

    Article  Google Scholar 

  26. Mansouri N, Javidi MM (2018b) A new Prefetching-aware Data Replication to decrease access latency in cloud environment. J Syst Softw 144:197–215

    Article  Google Scholar 

  27. Liang L, Xing L, Levitin G (2019) Optimizing dynamic survivability and security of replicated data in cloud systems under co-residence attacks. Reliab Eng Syst Saf 192:106265

    Article  Google Scholar 

  28. Sun SY, Yao WB, Li XY (2018) DARS: a dynamic adaptive replica strategy under high load Cloud-P2P. Future Gener Comput Syst 78:31–40

    Article  Google Scholar 

  29. He L, Qian Z, Shang F (2020) A novel predicted replication strategy in cloud storage. J Supercomput 76:4838–4856

    Article  Google Scholar 

  30. Xue L, Ni J, Li Y, Shen J (2017) Provable data transfer from provable data possession and deletion in cloud storage. Comput Standards Interfaces 54:46–54

    Article  Google Scholar 

  31. Ramanan M, Vivekanandan P (2019) Efficient data integrity and data replication in cloud using stochastic diffusion method. Clust Comput 22:14999–15006

    Article  Google Scholar 

  32. Antonio Parejo J, Ruiz-Corte’s A, Lozano S, Fernandez P (2012) Metaheuristic optimization frameworks: a survey and benchmarking. Soft Comput 16(3):527–561

    Article  Google Scholar 

  33. Mahdavi Jafari M, Khayati GR (2018) Prediction of hydroxyapatite crystallite size prepared by sol–gel route: gene expression programming approach. J Sol-Gel Sci Technol 86(1):112–125

    Article  Google Scholar 

  34. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp 39–43

  35. Cheng R, Jin Y (2015) A competitive swarm optimizer for large scale optimization. IEEE Trans Cybern 45(2):191–204

    Article  Google Scholar 

  36. Luo Y, Che X (2009) Chaos immune particle swarm optimization algorithm with hybrid discrete variables and its application to mechanical optimization. In:‬ Third International Symposium on Intelligent Information Technology Application Workshops

  37. Cheng R, Jin Y (2014) Demonstrator selection in a social learning particle swarm optimizer, In: IEEE Congress on Evolutionary Computation, pp 3103–3110

  38. Wang H, Sun H, Li C, Rahnamayan S, Pan JS (2013) Diversity enhanced particle swarm optimization with neighborhood search. Inf Sci 223:119–135

    Article  MathSciNet  Google Scholar 

  39. Phan DH, Suzuki J, Carroll R (2012) Evolutionary multi objective optimization for green clouds. In: Annual Conference Companion on Genetic and Evolutionary Computation, pp 19–26

  40. Jiang G (2009) Power and performance management of virtualized computing environments via look ahead control. Clust Comput 12(1):1–15

    Article  Google Scholar 

  41. Moran MJ, Shapiro HN (1995) Fundamentals of engineering thermodynamics. Wiley, Hoboken

    Google Scholar 

  42. Lub L, Chena D, Rend XL, Ming Zhang Q, Cheng Y (2016) Vital nodes identification in complex networks. Phys Rep 650:1–63

    Article  MathSciNet  Google Scholar 

  43. Hale WK (1980) Frequency assignment: theory and applications. Proc IEEE 68(12):1497–1514

    Article  Google Scholar 

  44. Wylie JJ, Bakkaloglu M, Pandurangan V, Bigrigg MW, Oguz S, Tew K, Williams C, Ganger GR, Khosla PK (2001) Selecting the right data distribution scheme for a survivable storage system, Carnegie Mellon University, Technical Report. CMU-CS-01-120

  45. Saadat N, Rahmani AM (2012) PDDRA: a new pre-fetching based dynamic data replication algorithm in data grids. Future Gener Comput Syst 28:666–681

    Article  Google Scholar 

  46. Jeffrey D, Sanjay G, MapReduce: simplified data processing on large clusters. In: Proceedings of the Conference on Operating System Design and Implementation, pp 137–150

  47. Ghemawat S, Gobioff H, Leung ST (2003) The Google file system. ACM SIGOPS Oper Syst Rev 37(5):29–43

    Article  Google Scholar 

  48. Shvachko K, Hairong K, Radia S, Chansler R (2010) The Hadoop distributed file system. In: Proceedings of the 26th Symposium on Mass Storage Systems and Technologies, pp 1–10

  49. Jararweh Y, Alshara Z, Jarrah M, Kharbutli M, Alsaleh MN (2013) TeachCloud: a cloud computing educational toolkit. Int J Cloud Comput. 2(2):237–257

    Article  Google Scholar 

  50. Gupta SKS, Robin Gilbert R, Banerjee A, Abbasi Z, Mukherjeey T, Varsamopoulos G (2011) GDCSim: a tool for analyzing green data center design and resource management techniques. In: International Green Computing Conference and Workshops

  51. Nunez A, Vazquez-Poletti JL, Caminero AC, Castane GG, Carretero J, Llorente IM (2012) iCanCloud: a flexible and scalable cloud infrastructure simulator. J Grid Comput 10(1):185–209

    Article  Google Scholar 

  52. Fittkau F, Frey S, Hasselbring W (2012) Cloud user-centric enhancements of the simulator CloudSim to improve cloud deployment option analysis. In: Proceedings of the 1st European Conference on Service-Oriented and Cloud Computing

  53. Garg S, Buyya R (2011) Networkcloudsim: modeling parallel applications in cloud simulations. In: Proceedings of the 4th IEEE/ACM International Conference on Utility and Cloud Computing, pp 105–113

  54. Lim S, Sharma B, Nam G, Kim E, Das C (2009) MDCSim: a multi-tier data center simulation, platform. In: Proceedings of IEEE International Conference on Cluster Computing and Workshops

  55. Kecskemeti G (2015) DISSECT-CF: a simulator to foster energy-aware scheduling in infrastructure clouds. Simul Model Pract Theory 58:188–218

    Article  Google Scholar 

  56. Teixeira T, Calheiros RN, Gomes DG (2014) CloudReports: an extensible simulation tool for energy-aware cloud computing environments. Cloud Computi, pp 127–142

  57. Barroso LA, Clidaras J, Holzle U (2013) The datacenter as a computer: an introduction to the design of warehouse-scale machines, 2nd ed. Morgan and Claypool Publishers

  58. Cameron DG, Carvajal-schiaffino R, Paul Millar A, Nicholson C, Stockinger K, Zini F (2003) UK Grid Simulation with OptorSim, UK e-Science All Hands Meeting

  59. Wen Y, Xu H, Yang J (2011) A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system. Inf Sci 181:567–581

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Shahid Bahonar University of Kerman, Box No. 76135-133, Kerman, Iran

    N. Mansouri, M. M. Javidi & B. Mohammad Hasani Zade

Authors
  1. N. Mansouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. M. Javidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. B. Mohammad Hasani Zade
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to N. Mansouri.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

figure l

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mansouri, N., Javidi, M.M. & Mohammad Hasani Zade, B. A CSO-based approach for secure data replication in cloud computing environment. J Supercomput 77, 5882–5933 (2021). https://doi.org/10.1007/s11227-020-03497-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03497-3

Keywords

Search

Navigation