Oriental Journal of Computer Science and Technology

Open Access Open Access  Restricted Access Subscription Access

Maintaining the Data Integrity and Data Replication in Cloud using Modified Genetic Algorithm (Mga) and Greedy Search Algorithm (Gsa)


Affiliations
1 Department of Physical Sciences and IT,Agricultural Engineering College and Research Institute, Tamil Nadu Agricultural University, Coimbatore-641003, Tamil Nadu, India
 

Cloud computing is now most widely used platform to store and access data globally. Data Integrity is an important aspect which ensures the quality of the data stored in the cloud and maintaining integrity is a challenging task. Data Replication is the key factor which maintains replication of data stored in the cloud. The integrity of data will be affected by various factors like intruders, data corruption and suspicious entities posing threat to data in cloud. The prevailing data replication methods are not able to handle dynamically changing large volume of data and obtaining additional storage resources for creation of replicas. Owing to several security threats existing incloud, an effective mechanism is needed to audit the integrity of data. In this paper, approaches for maintaining the integrity of data and data replication are proposed to improve the service quality of the Cloud Provider. The MGA and GSA approaches are based on deterministic approach for finding near optimal solution. Both MGA and GSA methods reduces the lag in managing the remote data by storing the data closer to the applications.

Keywords

Cloud Computing; Data Integrity; Data Integrity; Data Replication; Modified Genetic Algorithm (Mga); Greedy Search Algorithm (Gsa).
User
Notifications
Font Size

  • Boru, D., Kliazovich, D., Granelli, F., Bouvry, P., & Zomaya, A. Y. (2015). Energy-efficient data replication in cloud computing datacenters. Cluster computing, 18(1), 385-402.

  • Liu, C. W., Hsien, W. F., Yang, C. C., & Hwang, M. S. (2016). A Survey of Public Auditing for Shared Data Storage with User Revocation in Cloud Computing. IJ Network Security, 18(4), 650-666.

  • Zhang, J., & Dong, Q. (2016). Efficient IDbased public auditing for the outsourced data in cloud storage. Information Sciences, 343, 1-14.

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

  • Lin, J. W., Chen, C. H., & Chang, J. M. (2013). QoS-aware data replication for data-intensive applications in cloud computing systems. IEEE Transactions on Cloud Computing, 1(1), 101-115.

  • Boru, D., Kliazovich, D., Granelli, F., Bouvry, P., & Zomaya, A. Y. (2015). Energy-efficient data replication in cloud computing datacenters. Cluster computing, 18(1), 385-402.

  • Zhang, Y., Xu, C., Li, H., & Liang, X. (2016). Cryptographic public verification of data integrity for cloud storage systems. IEEE Cloud Computing, 3(5), 44-52.

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

  • El-henawy, I. M., & Ismail, M. M. (2014). A Hybrid Swarm Intelligence Technique for Solving Integer Multi-objective Problems. International Journal of Computer Applications, 87(3).

  • Huo, Y., Zhuang, Y., Gu, J., Ni, S., & Xue, Y. (2015). Discrete gbest-guided artificial bee colony algorithm for cloud service composition. Applied Intelligence, 42(4), 661678.

  • Połap, D., Woźniak, M., Napoli, C., & Tramontana, E. (2015). Real-time cloudbased game management system via cuckoo search algorithm. International Journal of Electronics and Telecommunications, 61(4), 333-338.

  • Florence, A. P., & Shanthi, V. (2014). A load balancing model using firefly algorithm in cloud computing. Journal of Computer Science, 10(7), 1156.

  • Jacob, L., Jeyakrishanan, V., & Sengottuvelan, P. (2014). Resource scheduling in cloud using bacterial foraging optimization algorithm. International Journal of Computer Applications, 92(1).

  • Hu, X. (2015). Adaptive optimization of cloud security resource dispatching SFLA algorithm. Int. J. Eng. Sci.(IJES), 4(3), 39-43.

  • Dashti, S. E., & Rahmani, A. M. (2016). Dynamic VMs placement for energy efficiency by PSO in cloud computing. Journal of Experimental & Theoretical Artificial Intelligence, 28(1-2), 97-112.

  • Zhou, J., & Yao, X. (2017). A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition. International Journal of Production Research, 55(16), 4765-4784.

  • Zhou, J., & Yao, X. (2017). A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition. The International Journal of Advanced Manufacturing Technology, 88(912), 3371-3387.

  • Cavazos, J., Moss, J. E. B., & O’Boyle, M. F. (2006, January). Hybrid optimizations: Which optimization algorithm to use?. In Compiler Construction (pp. 124-138). Springer Berlin Heidelberg.

  • Sedano, A., Sancibrian, R., de Juan, A., Viadero, F., & Egana, F. (2012). Hybrid optimization approach for the design of mechanisms using a new error estimator. Mathematical Problems in Engineering, 2012.

  • Khanam, R., Kumar, R. R., & Kumar, C. (2018, March). QoS based cloud service composition with optimal set of services using PSO. In 2018 4th International Conference on Recent Advances in Information Technology (RAIT) (pp. 1-6). IEEE.

  • Dai, Y., Lou, Y., & Lu, X. (2015, August). A task scheduling algorithm based on genetic algorithm and ant colony optimization algorithm with multi-QoS constraints in cloud computing. In Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2015 7th International Conference on (Vol. 2, pp. 428-431). IEEE.

  • Arindam Das and Ajanta De Sarkar, “On Fault Tolerance of Resources in Computational Grids”, International Journal of Grid Computing and Applications, Vol.3, No.3, Sept. 2012, DOI: 10.5121/ijgca.2012.3301.

  • Grover J., Sharma, M.: Cloud computing and its security issues—a review (2014).

  • Kulkarni, G., Waghmare, R., Palwe, R., Waykule, V., Bankar, H., &Koli, K. (2012, October). Cloud storage architecture. In Telecommunication Systems, Services, and Applications (TSSA), 2012 7th International Conference on (pp. 76-81). IEEE.

  • Modi, C., Patel, D., Borisaniya, B., Patel, A., Rajarajan, M.: A survey on security issues and solutions at different layers of Cloud computing, pp. 1–32 (2012).

  • Mukundha, C., & Vidyamadhuri, K. (2017). Cloud Computing Models: A Survey. Advances in Computational Sciences and Technology, 10(5), 747-761.

  • Nepal, Surya, et al. "DIaaS: Data integrity as a service in the cloud." Cloud Computing (CLOUD), 2011 IEEE International Conference on. IEEE, 2011.

  • NIST Definition of Cloud Computinghttps://www.nist.gov/programsprojects/cloudcomputing


Abstract Views: 202

PDF Views: 1




  • Maintaining the Data Integrity and Data Replication in Cloud using Modified Genetic Algorithm (Mga) and Greedy Search Algorithm (Gsa)

Abstract Views: 202  |  PDF Views: 1

Authors

M. Ramanan
Department of Physical Sciences and IT,Agricultural Engineering College and Research Institute, Tamil Nadu Agricultural University, Coimbatore-641003, Tamil Nadu, India
J. Arockia Stephen Raj
Department of Physical Sciences and IT,Agricultural Engineering College and Research Institute, Tamil Nadu Agricultural University, Coimbatore-641003, Tamil Nadu, India

Abstract


Cloud computing is now most widely used platform to store and access data globally. Data Integrity is an important aspect which ensures the quality of the data stored in the cloud and maintaining integrity is a challenging task. Data Replication is the key factor which maintains replication of data stored in the cloud. The integrity of data will be affected by various factors like intruders, data corruption and suspicious entities posing threat to data in cloud. The prevailing data replication methods are not able to handle dynamically changing large volume of data and obtaining additional storage resources for creation of replicas. Owing to several security threats existing incloud, an effective mechanism is needed to audit the integrity of data. In this paper, approaches for maintaining the integrity of data and data replication are proposed to improve the service quality of the Cloud Provider. The MGA and GSA approaches are based on deterministic approach for finding near optimal solution. Both MGA and GSA methods reduces the lag in managing the remote data by storing the data closer to the applications.

Keywords


Cloud Computing; Data Integrity; Data Integrity; Data Replication; Modified Genetic Algorithm (Mga); Greedy Search Algorithm (Gsa).

References





DOI: https://doi.org/10.13005/ojcst13.0203.04