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A Novel Multi-objective Particle Swarm Algorithm Based on a Neighborhood to Search Depth in Task Scheduling by Considering a New Security Model | ||
| مجله مهندسی برق دانشگاه تبریز | ||
| مقاله 11، دوره 51، شماره 1 - شماره پیاپی 95، اردیبهشت 1400، صفحه 109-119 اصل مقاله (980.64 K) | ||
| نوع مقاله: علمی-پژوهشی | ||
| نویسندگان | ||
| Maedeh Mehravaran؛ Fazlollah Adibnia* ؛ Mohammad-Reza Pajoohan | ||
| Faculty of Computer Engineering, Yazd University, Yazd, Iran. | ||
| چکیده | ||
| Cloud computing is a novel technology that provides users with better opportunities to gain access to services on the Internet. Users should utilize organizational services to meet their needs. They can also benefit from non-organizational services with high capacity but limited security. This study aims to provide a new security model that addresses security requirements for tasks and data as well as security strength for resources and communication paths. The proposed security model is defined security distance concept. Minimizing security distance has to do with task scheduling so that the resources can be matched with the security level and the data will be fitted into the appropriate communication path. The proposed scheduling algorithm takes the server profit into account in addition to the minimum security distance. The increased server profits can lead to higher resource sharing by the servers. The proposed scenario is implemented based on a neighborhood to search depth in task scheduling. This algorithm utilizes a novel ‘far and near neighborhood’ approach to select the best particle position. The approach generates both diversity and convergence in the set of answers. Finally, the proposed algorithm is compared with three other similar scheduling algorithms obtained by VNPSO, MPSO and NSGAII, considering the security of the cloud computing environment. The computational results show the effectiveness of the proposed algorithm to obtain resources with similar security and higher server profits. | ||
| کلیدواژه‌ها | ||
| Task scheduling Security requirement Security strength Security distance Multi؛ objective particle swarm optimization | ||
| مراجع | ||
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[1] C. Jianfang, C. Junjie, and Z. Qingshan, "An optimized scheduling algorithm on a cloud workflow using a discrete particle swarm," Cybernetics and Information Technologies, vol. 14, pp. 25-39, 2014. [2] M. Naghibzadeh, "Modeling Workflow of Tasks and Task Interaction Graphs to Schedule on the Cloud," CLOUD COMPUTING 2016, p. 81, 2016. [3] M. Yazdanbakhsh and R. Khorsand, "A Task Scheduling Strategy to Improve Qualitative Features in the Cloud Computing Environment," Tabriz Journal of Electrical Engineering, vol. 49, pp. 1427-1437, 2019 (in persian). [4] L. Singh and S. Singh, "A survey of workflow scheduling algorithms and research issues," International Journal of Computer Applications, vol. 74, 2013. [5] R. Gupta, "Above the Clouds: A View of Cloud Computing," Asian Journal of Research in Social Sciences and Humanities, vol. 2, pp. 84-110, 2012. [6] T. Ghafari and S. Bakhtiari Chehelcheshmeh, "Secure Outsourcing Cloud Data using Lattice-based Secret Sharing," Tabriz Journal of Electrical Engineering, vol. 49, pp1211-1221,2019(in persian). [7] H.Abrishami, A.Rezaeian, M.Naghibzadeh, "Scheduling in hybrid cloud to maintin data privacy", 20th National CSI Computer Conference, 2015. (In Persian) [8] H. Liu, A. Abraham, V. Snášel, and S. McLoone, "Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments," Information Sciences, vol. 192, pp. 228-243, 2012. [9] W. Liu, S. Peng, W. Du, W. Wang, and G. S. Zeng, "Security-aware intermediate data placement strategy in scientific cloud workflows," Knowledge and information systems, vol. 41, pp. 423-447, 2014. [10] H. Chen, X. Zhu, D. Qiu, L. Liu, and Z. Du, "Scheduling for workflows with security-sensitive intermediate data by selective tasks duplication in clouds," IEEE Transactions on Parallel and Distributed Systems, 2017. [11] Z. Li, J. Ge, H. Yang, L. Huang, H. Hu, H. Hu, et al., "A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds," Future Generation Computer Systems, 2016. [12] M. L. Pinedo, "Scheduling: theory, algorithms, and systems", Springer International Publishing, 2016. [13] F. Wu, Q. Wu, and Y. Tan, "Workflow scheduling in cloud: a survey," The Journal of Supercomputing, vol. 71, pp. 3373-3418, 2015. [14] M. Masdari, S. ValiKardan, Z. Shahi, and S. I. Azar, "Towards workflow scheduling in cloud computing: a comprehensive analysis," Journal of Network and Computer Applications, vol. 66, pp. 64-82, 2016. [15] W. Chen and E. Deelman, "Workflowsim: A toolkit for simulating scientific workflows in distributed environments," in 8th IEEE International Conference on E-science , pp. 1-8, 2012. [16] H. Abrishami, A. Rezaeian, and M. Naghibzadeh, "Workflow Scheduling on the Hybrid Cloud to Maintain Data Privacy under Deadline Constraint," Journal of Intelligent Computing Volume, vol. 6, p. 93, 2015. [17] H. Abrishami, A. Rezaeian, and M. Naghibzadeh, "A novel deadline-constrained scheduling to preserve data privacy in hybrid Cloud," in 5th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 234-23, 2015. [18] S. Sharif, J. Taheri, A. Y. Zomaya, and S. Nepal, "Mphc: Preserving privacy for workflow execution in hybrid clouds," in International Conference on Parallel and Distributed Computing, Applications and Technologies, pp. 272-280, 2013. [19] S. Abrishami, M. Naghibzadeh, and D. H. Epema, "Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds," Future Generation Computer Systems, vol. 29, pp. 158-169, 2013. [20] D. Fernández-Cerero, A. Jakóbik, D. Grzonka, J. Kołodziej, and A. Fernández-Montes, "Security supportive energy-aware scheduling and energy policies for cloud environments," Journal of Parallel and Distributed Computing, vol. 119, pp. 191-202, 2018. [21] Y. Wen, J. Liu, W. Dou, X. Xu, B. Cao, and J. Chen, "Scheduling workflows with privacy protection constraints for big data applications on cloud," Future Generation Computer Systems, 2018. [22] E. S. Alkayal, N. R. Jennings, and M. F. Abulkhair, "Efficient task scheduling multi-objective particle swarm optimization in cloud computing," in 41st IEEE Conference on Local Computer Networks Workshops (LCN Workshops), pp. 17-24, 2016. [23] K. Pradeep and T. P. Jacob, "CGSA scheduler: A multi-objective-based hybrid approach for task scheduling in cloud environment," Information Security Journal: A Global Perspective, vol. 27, pp. 77-91, 2018. [24] V. Arabnejad, K. Bubendorfer, and B. Ng, "Scheduling deadline constrained scientific workflows on dynamically provisioned cloud resources," Future Generation Computer Systems, Vol.75, pp. 348-364, 2017.
[25] N. Chopra and S. Singh, "HEFT based workflow scheduling algorithm for cost optimization within deadline in hybrid clouds," in Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp. 1-6, 2013.
[26] G. Kaur and M. Kalra, "Deadline constrained scheduling of scientific workflows on cloud using hybrid genetic algorithm," in 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence, pp. 276-280, 2017.
[27] Z. Fan, T. Wang, Z. Cheng, G. Li, and F. Gu, "An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to Line," Shock and Vibration, vol. 2017 . [28] R. Fan, L. Wei, X. Li, and Z. Hu, "A novel multi-objective PSO algorithm based on completion-checking," Journal of Intelligent & Fuzzy Systems, vol. 34, pp. 321-333, 2018. [29] B. Jana, M. Chakraborty, and T. Mandal, "A Task Scheduling Technique Based on Particle Swarm Optimization Algorithm in Cloud Environment," in Soft Computing: Theories and Applications, ed: Springer, pp. 525-536. 2019. [30] A. S. Kumar and M. Venkatesan, "Multi-Objective Task Scheduling Using Hybrid Genetic-Ant Colony Optimization Algorithm in Cloud Environment," Wireless Personal Communications, vol. 107, pp. 1835-1848, 2019. [31] B. Keshanchi, A. Souri, and N. J. Navimipour, "An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing," Journal of Systems and Software, vol. 124, pp. 1-21, 2017.
[32] G. Juve, A. Chervenak, E. Deelman, S. Bharathi, G. Mehta, and K. Vahi, "Characterizing and profiling scientific workflows," Future Generation Computer Systems, vol. 29, pp. 682-692, 2013. [33] P. S. Naidu and B. Bhagat, "Secure workflow scheduling in cloud environment using modified particle swarm optimization with scout adaptation," International Journal of Modeling, Simulation, and Scientific Computing, vol. 9, p. 1750064, 2018. [33] N. Sooezi, S. Abrishami, and M. Lotfian, "Scheduling Data-Driven Workflows in Multi-cloud Environment," in 2015 IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom), pp. 163-167, 2015 [35] M. Naghibzadeh, "Modeling and scheduling hybrid workflows of tasks and task interaction graphs on the cloud," Future Generation Computer Systems, vol. 65, pp. 33-45, 2016.
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آمار تعداد مشاهده مقاله: 496 تعداد دریافت فایل اصل مقاله: 450 |
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