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Efficient data integrity and data replication in cloud using stochastic diffusion method

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Abstract

Cloud computing will provide scalable computing as well as storage resources where more data intensive applications will be developed in a computing environment. Owing to the existence of such security threats in the cloud, several mechanisms are being proposed for allowing the users to audit the integrity of data along with the public key of the owner of the data even before making use of the cloud data. Replicating of data in cloud servers through multiple data centers offers better availability, scalability, and durability. The correctness of choice of the right type of public key of the previous mechanisms is based on the security of the public key infrastructure (PKI). Although traditional PKI has been widely used in the construction of public key cryptography, it still faces many security risks, especially in the aspect of managing certificates. There are different applications having different types of quality of service (QoS) needs. In order to support the QoS requirement continuously, the application of such data corruption for this work will be an efficient integrity of data replication that makes use of a stochastic diffusion search (SDS) algorithm that has been proposed. This SDS is that technique of a multi-agent global optimisation which has been based on the behaviour of ants that has been rooted in the partial evaluation of that of an objective function along with direct communication among agents. The proposed SDS algorithm will minimize the replication cost of data. The results of these experiments have shown that the mechanism will be able to demonstrate the effectiveness of this proposed algorithm which is in the replication of data as well as its recovery. The proposed method when appropriately compared with the cost effective replication of dynamic data given by Li et al. proves that the average recovery time is less by 18.18% for the 250 number of requested nodes, by 14.28% for the 500 number of requested nodes, by 11.11% for the 750 number of requested nodes and by 8.69% for the 1000 number of requested nodes.

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References

  1. Jouini, M., Rabai, L.B.A.: A security framework for secure cloud computing environments. Int. J. Cloud Appl. Comput. 6(3), 32–44 (2016)

    Google Scholar 

  2. Zunnurhain, K., Vrbsky, S. V.: Security in cloud computing. In: Proceedings of the 2011 International Conference on Security and Management (2011)

  3. Hussein, M.K., Mousa, M.H.: A light-weight data replication for cloud data centers environment. Int. J. Eng. Innov. Technol. 1(6), 169–175 (2012)

    Google Scholar 

  4. Boru, D., Kliazovich, D., Granelli, F., Bouvry, P., Zomaya, A.Y.: Energy-efficient data replication in cloud computing datacenters. Clust. Comput. 18(1), 385–402 (2015)

    Article  Google Scholar 

  5. Liu, C.W., Hsien, W.F., Yang, C.C., Hwang, M.S.: A survey of public auditing for shared data storage with user revocation in cloud computing. IJ Netw. Secur. 18(4), 650–666 (2016)

    Google Scholar 

  6. Zhang, J., Dong, Q.: Efficient ID-based public auditing for the outsourced data in cloud storage. Inf. Sci. 343, 1–14 (2016)

    MathSciNet  Google Scholar 

  7. Zhang, Y., Xu, C., Li, H., Liang, X.: Cryptographic public verification of data integrity for cloud storage systems. IEEE Cloud Comput. 3(5), 44–52 (2016)

    Article  Google Scholar 

  8. Darwazeh, N.S., Al-Qassas, R.S., AlDosari, F.: A secure cloud computing model based on data classification. Proc. Comput. Sci. 52, 1153–1158 (2015)

    Article  Google Scholar 

  9. Fabian, B., Ermakova, T., Junghanns, P.: Collaborative and secure sharing of healthcare data in multi-clouds. Inf. Syst. 48, 132–150 (2015)

    Article  Google Scholar 

  10. Jiang, T., Chen, X., Ma, J.: Public integrity auditing for shared dynamic cloud data with group user revocation. IEEE Trans. Comput. 65(8), 2363–2373 (2016)

    Article  MathSciNet  Google Scholar 

  11. Ali, M., Bilal, K., Khan, S., Veeravalli, B., Li, K., Zomaya, A.: DROPS: division and replication of data in the cloud for optimal performance and security. In: IEEE Transactions on Cloud computing (2015)

  12. Zhang, Y., Ni, J., Tao, X., Wang, Y., Yu, Y.: Provable multiple replication data possession with full dynamics for secure cloud storage. Concurr. Comput.: Pract. Exp. 28(4), 1161–1173 (2016)

    Article  Google Scholar 

  13. Gai, K., Qiu, L., Chen, M., Zhao, H., Qiu, M.: SA-EAST: security-aware efficient data transmission for ITS in mobile heterogeneous cloud computing. ACM Trans. Embed. Comput. Syst. 16(2), 60 (2017)

    Article  Google Scholar 

  14. Sookhak, M., Gani, A., Khan, M.K., Buyya, R.: Dynamic remote data auditing for securing big data storage in cloud computing. Inf. Sci. 380, 101–116 (2017)

    Article  Google Scholar 

  15. Dashti, S.E., Rahmani, A.M.: Dynamic VMs placement for energy efficiency by PSO in cloud computing. J. Exp. Theor. Artif. Intell. 28(1–2), 97–112 (2016)

    Article  Google Scholar 

  16. Li, H., Zhu, G., Cui, C., Tang, H., Dou, Y., He, C.: Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing. Computing 98(3), 303–317 (2016)

    Article  MathSciNet  Google Scholar 

  17. Lin, Y.K., Chong, C.S.: Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system. J. Intell. Manuf. 28(5), 1189–1201 (2017)

    Article  Google Scholar 

  18. Wang, B., Li, B., Li, H., Li, F.: Certificateless public auditing for data integrity in the cloud. In: 2013 IEEE Conference on Communications and Network Security (CNS), pp. 136–144. IEEE (2013)

  19. Williams, H., Bishop, M.: Stochastic diffusion search: a comparison of swarm intelligence parameter estimation algorithms with ransac. Algorithms 7(2), 206–228 (2014)

    Article  Google Scholar 

  20. El-henawy, I.M., Ismail, M.M.: A hybrid swarm intelligence technique for solving integer multi-objective problems. Int. J. Comput. Appl. (2014). https://doi.org/10.5120/15192-3571

    Article  Google Scholar 

  21. Al-Rifaie, M. M., Bishop, M. J., Blackwell, T.: An investigation into the merger of stochastic diffusion search and particle swarm optimisation. In: Proceedings of the 13th annual conference on Genetic and evolutionary computation, pp. 37–44. ACM (2011)

  22. Lin, J.W., Chen, C.H., Chang, J.M.: QoS-aware data replication for data-intensive applications in cloud computing systems. IEEE Trans. Cloud Comput. 1(1), 101–115 (2013)

    Article  Google Scholar 

Download references

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  1. Department of CSE, Park College of Engineering and Technology, Kaniyur, Coimbatore, India

    M. Ramanan & P. Vivekanandan

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  1. M. Ramanan
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  2. P. Vivekanandan
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Correspondence to M. Ramanan.

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Ramanan, M., Vivekanandan, P. Efficient data integrity and data replication in cloud using stochastic diffusion method. Cluster Comput 22 (Suppl 6), 14999–15006 (2019). https://doi.org/10.1007/s10586-018-2480-9

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  • DOI: https://doi.org/10.1007/s10586-018-2480-9

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