الگوریتم زمانبندی کار مبتنی بر امنیت با استفاده از تکنیک بهینه‌سازی ازدحام ذرات و یادگیری انطباقی چندگانه

[1]     N. Mansouri and M. M. Javidi, “A review of data replication based on meta-heuristics approach in cloud computing and data grid,” Soft Comput., vol. 24, pp. 14503-14530, 2020.##

[2]     M. Bansal and S. K. Malik, “A multi-faceted optimization scheduling framework based on the particle swarm optimization algorithm in cloud computing,” Sustainable Comput. Inf. Syst., 2020.##

[3]     P. Mell and T. Grance, “The NIST definition of cloud computing,” 2011.##

[4]     F. Jauro, H. Chiroma, A. Y. Gital, M. Almutairi, S. M. Abdulhamid, and J. H. Abawajy, “Deep learning architectures in emerging cloud computing architectures: Recent development, challenges and next research trend,” Appl. Soft Comput., vol. 96, 2020.##

[5]     N. Mansouri and M. M. Javidi, “A hybrid data replication strategy with fuzzy-based deletion for heterogeneous cloud data centers,” J. Supercomput., vol. 74, pp. 5349-5372, 2018.##

[6]     N. Mansouri, R. Ghafari, and B. Mohammad Hasani Zade, “Cloud computing simulators: A comprehensive review,” Simul. Modell. Pract. Theory, vol. 104, 2020.##

[7]     A. A. Zubair, S. B. A. Razak, M. A. Bin Ngadi, A. Ahmed, and S. H. H. Madni, “Convergence-based task scheduling techniques in cloud computing: A review,” In: International Conference of Reliable Information and Communication Technology, pp. 227–234, 2020.##

[8]     S. R. Pakize, S. M. Arefi nejad, “A new scheduling algorithm to reduce computation time in Hadoop environment,” Journal of Electronical & Cyber Defence, vol. 8, 2020. (In Persian)##

[9]     F. Xin and L. Zhang, “The review of task scheduling in cloud computing,” In: International Conference on           Geo-informatics in Sustainable Ecosystem and Society, pp.     119–126, 2019.##

[10]  M. Mehravaran, M. R. Pajoohan, and F. Adibnia, “Secure and confidential workflow scheduling in hybrid cloud with improved particle swarm optimization algorithm,” Journal of Electronic and cyber defense, vol. 7, pp. 131-145, 2019. (In Persian)##

[11]  N. Mansouri, “Network and data location aware approach for simultaneous job scheduling and data replication in large-scale data grid environments,” Front. Comput. Sci., vol. 8, pp. 391-408, 2014.##

[12]  D. Ding, X. Fan, Y. Zhao, K. Kang, Q. Yin, and J. Zeng,   “Q-learning based dynamic task scheduling for            energy-efficient cloud computing,” Future Gener. Comput. Syst., vol. 108, pp. 361-371, 2020.##

[13]  S. Hammouti, B. Yagoubi, and S. A. Makhlouf, “Workflow security scheduling strategy in cloud computing,” In: International Symposium on Modelling and Implementation of Complex Systems, pp. 48-61, 2021.##

[14]  M. Kumar, S. C. Sharma, A. Goel, and S. P. Singh, “A comprehensive survey for scheduling techniques in cloud computing,” J. Netw. Comput. Appl., vol. 143, pp. 1-33, 2019.##

[15]  N. Subramanian and A. Jeyaraj, “Recent security challenges in cloud computing,” Comput. Electr. Eng., vol. 71, pp.      28-42, 2018.##

[16]  H. Tabrizchi and M. Kuchaki Rafsanjani, “A survey on security challenges in cloud computing: issues, threats, and solutions,” J. Supercomput., 2020.##

[17]  A. Bansal, A. K. Bairwa, and S. Hiranwal, “Security issues in cloud computing: A Review,” In: Proceedings of International Conference on Communication and Computational Technologies, pp. 515-521, 2021.##

[18]  H. Mouratidis, S. Shei, and A. Delaney, “A security requirements modelling language for cloud computing environments,” Softw. Syst. Model., vol. 19, pp. 271-295, 2020.##

[19]  D. A. B. Fernandes, L. F. B. Soares, J. V Gomes, M. M. Freire, and P. R. M. Inácio, “Security issues in cloud environments: a survey,” Int. J. Inf. Secur., vol. 13, pp.    113-170, 2014.##

[20]  S. Meng, W. Huang, X. Yin, M. R. Khosravi, Q. Li, S. Wan, and L. Qi, “Security-aware dynamic scheduling for real-time optimization in cloud-based industrial applications,” In: IEEE Transactions on Industrial Information, 2020.##

[21]  J. Zhou, J. Sun, P. Cong, Z. Liu, X. Zhou, T. Wei, and S. Hu, “Security-critical energy-aware task scheduling for heterogeneous real-time MPSoCs in IoT,” In: IEEE Transactions on Services Computing, 2020.##

[22]  R. Kumar and R. Goyal, “On cloud security requirements, threats, vulnerabilities and countermeasures: A survey,” Comput. Sci. Rev., vol. 33, pp. 1-48, 2019.##

[23]  N. Mansouri, G. Dastghaibyfard, and A. Horri, “A novel job scheduling algorithm for improving data grid’s performance,” In: 2011 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 142-147, 2011.##

[24]  J. Gąsior and F. Seredyński, “Security-aware distributed job scheduling in cloud computing systems: A game-theoretic cellular automata-based approach,” In: International Conference on Computational Science, pp. 449-462, 2019.##

[25]  D. Fernández-Cerero, A. Jakóbik, D. Grzonka, J. Kołodziej, and A. Fernández-Montes, “Security supportive          energy-aware scheduling and energy policies for cloud environments,” J. Parallel Distrib. Comput., vol. 119, pp. 191-202, 2018.##

[26]  H. Y. Shishido, J. C. Estrella, C. F. M. Toledo, and M. S. Arantes, “Genetic-based algorithms applied to a workflow scheduling algorithm with security and deadline constraints in clouds,” Comput. Electr. Eng., vol. 69, pp. 378-394, 2018.##

[27]  Z. Li, J. Ge, H. Yang, L. Huang, H. Hu, H. Hu, and B. Luo, “A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds,” Future Gener. Comput. Syst., vol. 65, pp. 140-152, 2016.##

[28]  L. Ismail and H. Materwala, “EATSVM: Energy-aware task scheduling on cloud virtual machines,” Procedia Comput. Sci., vol. 135, pp. 248-258, 2018.##

[29]  Y. C. Lee and A. Y. Zomaya, “Energy efficient utilization of resources in cloud computing systems,” J. Supercomput., vol. 60, pp. 268-280, 2012.##

[30]  H. Zhang, “Research on job security scheduling strategy in cloud computing model,” In: International Conference on Intelligent Transportation, Big Data & Smart City, pp.     649-652, 2015.##

[31]  X. Liu and Y. Zhou, “A Self-adaptive layered sleep-based method for security dynamic scheduling in cloud storage,” In: 4th International Conference on Information Science and Control Engineering, pp. 99-103, 2017.##

[32]  Y. Lou, T. Zhang, J. Yan, K. Li, Y. Jiang, H. Wang, and J. Cheng, “Dynamic scheduling strategy for testing task in cloud computing,” In: Sixth International Conference on Computational Intelligence and Communication Networks, pp. 633-636, 2014.##

[33]  N. Mansouri and M. M. Javidi, “Cost-based job scheduling strategy in cloud computing environments,” Distrib. Parallel Databases, pp. 1-36, 2019.##

[34]  R. Achar, P. S. Thilagam, D. Shwetha, and H. Pooja, “Optimal scheduling of computational task in cloud using virtual machine tree,” In: Third International Conference on Emerging Applications of Information Technology, pp.    143-146, 2012.##

[35]  M. A. Kacimi, O. Guenounou, L. Brikh, F. Yahiaoui, and N. Hadid, “New mixed-coding PSO algorithm for a  self-adaptive and automatic learning of Mamdani fuzzy rules,” Eng. Appl. Artif. Intell., vol. 89, 2020.##

[36]  A. R. Arunarani, D. Manjula, and V. Sugumaran, “Task scheduling techniques in cloud computing: A literature survey,” Future Gener. Comput. Syst., vol. 91, pp. 407-415, 2019.##

[37]  G. Xu, Q. Cui, X. Shi, H. Ge, Z. H. Zhan, H. P. Lee, Y. Liang, R. Tai, and C. Wu, “Particle swarm optimization based on dimensional learning strategy,”‌ Swarm Evol. Comput., vol. 45, pp. 33-51, 2019.##

[38]  Y. Zhang, X. Liu, F. Bao, J. Chi, C. Zhang, and P. Liu, “Particle swarm optimization with adaptive learning strategy,” Knowledge-Based Syst., vol. 196, 2020.##

[39]  M. S. Sanaj and P. M. Joe Prathap, “Nature inspired chaotic squirrel search algorithm (CSSA) for multi objective task scheduling in an IAAS cloud computing atmosphere,” Eng. Sci. Technol. an Int. J., vol. 23, pp. 891-902, 2020.##

[40]  A. Beloglazov, J. Abawajy, and R. Buyya, “Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing,” Future Gener. Comput. Syst., vol. 28, pp. 755-768, 2012.##

[41]  H. Liu, X.W. Zhang, and L.P. Tu, “A modified particle swarm optimization using adaptive strategy,” Expert Syst. Appl., vol. 152, 2020.##

[42]  I. B. Aydilek, “A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems,” Appl. Soft Comput., vol. 66, pp.  232-249, 2018.##

[43]  G. Wu, R. Mallipeddi, and P. N. Suganthan, “Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization,” National University of Defense Technology, Changsha, Hunan, PR China and Kyungpook National University, Daegu, South Korea and Nanyang Technological University, Singapore, Technical Report, 2017.##

[44]  A. W. Mohamed, A. A. Hadi, A. M. Fattouh, and K. M. Jambi, “LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems,” In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 145-152, 2017.##

[45]  N. Mansouri, “Adaptive data replication strategy in cloud computing for performance improvement,” Front. Comput. Sci., vol. 10, pp. 925-935, 2016.##