Improving Storage in Distributed Cloud Data Centers by Increasing Reliability Using Collective Intelligence Algorithms
Subject Areas : Computer Engineering
Alireza Chamkoori
1
,
Serajdean Katebi
2
1 - Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran
2 - Department of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
Keywords: Cloud storage, reliability, Multi-Objective Optimization, distributed cloud data centers, collective intelligence,
Abstract :
Data security and privacy in data centers is an important issue. The major anxiety in security and privacy is the result of the fact that the topography of important operas can be available to sensitive information. To improve storage in distributed cloud data centers, the Particle Swarm Optimization (PSO) algorithm has been used to copy data between data centers. This paper summarizes the objectives and constraints of the cloud storage problem in order to achieve good performance by considering the shortest data transmission distance, obtaining optimal storage in distributed data centers with reliability based on PSO algorithm between two central data sets. And then it provides an intelligent cryptographic approach that cloud service operators cannot directly access partial data. Numerical results show that the proposed method can provide a good cloud storage strategy when the number of distributed data centers is equal, the defense of the main threats in the clouds can be done effectively.
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_||_[1] J. Liu, T. Shi and P. Li, "Optimal cloud storage problem in the distributed cloud data centers by the discrete PSO algorithm," IEEE Congress on Evolutionary Computation; Sendai, Japan, 2015, pp. 156–163, doi: 10.1109/CEC.2015.7256887.
[2] H. Hu, Y. Wen and D. Niyato, "Public cloud storage-assisted mobile social video sharing: a supermodular game approach," IEEE Journal on Selected Areas in Communications , vol. 35, no.3, pp.345–556, 2017, doi: 10.1109/JSAC.2017.2659478.
[3] A. Alabdel-abass, L. Xiao, N. Mandayam and Z. Gajic, "Evolutionary game theoretic analysis of advanced persistent threats against cloud storage," IEEE Access, vol. 5, pp. 8482–8491, 2017, doi: 10.1109/ACCESS.2017.2691326.
[4] Y. Li, K. Gai, L. Qiu, M. Qiu and H. Zhao, "Intelligent cryptography approach for secure distributed big data storage in cloud computing," Information Sciences, vol. 387, pp.103–115, 2016, doi: 10.1016/j.ins.2016.09.005.
[5] W. Songa, et al. "A privacy-preserved full-text retrieval algorithm over encrypted data for cloud storage applications," Journal of Parallel and Distributed Computing , vol. 99, pp. 14–27, 2017, doi: 10.1016/j.jpdc.2016.05.017.
[6] X. Liu, L. Fan, L. Wang and S. Meng, "PSO based multiobjective reliable optimization model for cloud storage," in IEEE International Conference on Computer and Information Technology (CIT),Liverpool, UK, 2015, pp. 2263–2269.
[7] A. Abdulridha-Taha, D. Salama, A. Elminaam and K.M. Hosny, "An improved security schema for mobile cloud computing using hybrid cryptographic algorithms," Far East Journal of Electronics and Communications , vol. 18, no. 4, pp. 521–546, 2018, doi: 10.17654/EC018040521.
[8] S. Palani, E. Sangeetha and A. Archana, "Implementation of deduplication on encrypted big-data using signcryption for cloud storage applications," International Journal of Pure and Applied Mathematics , vol. 119, no. 12, pp.13409–13421, 2018.
[9] G. Tlili, M. Faten- Zhani and H. Elbiaze, "On providing deadline-aware cloud storage services," IEEE Conference on Innovation in Clouds, Internet and Networks (ICIN); Paris, France, 2018, pp. 1–8, doi: 10.1109/ICIN.2018.8401605.
[10] D. Hyseni, et al. "The proposed model to increase security of sensitive data in cloud computing," International Journal of Advanced Computer Science and Applications , vol. 9, no. 2, pp. 203–210, 2018, doi: 10.14569/IJACSA.2018.090229.
[11] J. Bi, H. Yuan, S. Duanmu, M. Zhou and A. Abusorrah, "Energy-Optimized Partial Computation Offloading in Mobile-Edge ComputingWith Genetic Simulated-Annealing-Based Particle SwarmOptimization," IEEE Internet of Things Journal , vol. 8, no. 5, pp. 3774–3785, 2021, doi: 10.1109/JIOT.2020.3024223.
[12] Q. H. Zhu, J. J. Huang and Y. Hou, "Multi-Objective Scheduling of Cloud Data Centers Prone to Failures," Journal of Information Science & Engineering, vol. 38, no. 1, pp. 17–39, 2022.
[13] H. Yuan, J. Bi, M. Zhou, Q. Liu and A. C. Ammari, "Biobjective Task Scheduling for Distributed Green Data Centers," IEEE Transactions on Automation Science and Engineering , vol. 18, no. 2, pp.731-742, 2021, doi: 10.1109/TASE.2019.2958979
[14] W. Yun and Z. Xingquan, "An Effective CloudWorkflow Scheduling Approach Combining PSO and Idle Time Slot-Aware Rules," IEEE/CAA Journal of Automatica Sinica , vol. 8, no. 5, pp. 1079–1094, 2021, doi: 10.1109/JAS.2021.1003982.
[15] S.C. Vimercati, et al., "A Fuzzy-Based Brokering Service for Cloud Plan Selection," IEEE Systems Journal , vol. 13, no. 4, pp. 4101–4109, 2019, doi: 10.1109/JSYST.2019.2893212.
[16] F.V. den-Bergh and P. Engelbrecht, "A study of particle swarm optimization particle trajectories," Information Sciences , vol. 176, no. 8, pp. 937–971, 2006, .doi: 10.1016/j.ins.2005.02.003.
[17] Z. Zheng, R. Kohavi and L. Mason, "Real world performance of association rule algorithms," in Proceedings of the 7th ACM SIGKDD international conference on Knowledge discovery and data mining; San Francisco California, USA, 2001, pp. 401–406, doi: 10.1145/502512.502572.
