[1. Al-Dhuraibi, Y., F. Paraiso, N. Djarallah, P. Merle. Elasticity in Cloud Computing: State of the Art and Research Challenges. – IEEE Transactions on Services Computing, Vol. 11, 2018, pp. 430-447.10.1109/TSC.2017.2711009]Search in Google Scholar
[2. Ullah, A., J. Li., Y. Shen, A. Hussain. A Control Theoretical View of Cloud Elasticity: Taxonomy, Survey and Challenges. – Cluster Computing, Vol. 21, 2018, pp. 1735-1764.10.1007/s10586-018-2807-6]Search in Google Scholar
[3. Dar, A. R., D. Ravindran. A Comprehensive Study on Cloud Computing. – International Journal of Advance Research in Science and Engineering, Vol. 7, 2018, pp. 235-242.]Search in Google Scholar
[4. Babu, A. A., V. M. A. Rajam. Resource Scheduling Algorithms in Cloud Environment – A Survey. – In: Proc. of 2nd International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM), 2017, pp. 25-30.10.1109/ICRTCCM.2017.72]Search in Google Scholar
[5. Parikh, S. M., N. M. Patel, H. B. Prajapati. Resource Management in Cloud Computing: Classification and Taxonomy. – Distributed, Parallel, and Cluster Computing, 2017, pp. 1-10.]Search in Google Scholar
[6. Elkhalik, W. A., A. Salah, I. El-Henawy. A Survey on Cloud Computing Scheduling Algorithms. – International Journal of Engineering Trends and Technology (IJETT), Vol. 60, pp. 65-70.10.14445/22315381/IJETT-V60P209]Search in Google Scholar
[7. Pham, N. M. N., V. S. Le, H. H. C. Nguyen. Energy Efficient Resource Allocation for Virtual Services Based on Heterogeneous Shared Hosting Platforms in Cloud Computing. – Cybernetics and Information Technologies, Vol. 17, 2017, pp. 47-58.10.1515/cait-2017-0029]Search in Google Scholar
[8. Senthilkumar, M. Energy-AwareTask Scheduling Using Hybrid Firefly-BAT (FFABAT) in Big Data. – Cybernetics and Information Technologies, Vol. 18, 2018, pp. 98-111.10.2478/cait-2018-0031]Search in Google Scholar
[9. Gill, S. S., R. Buyya. Resource Provisioning based Scheduling Framework for Execution of Heterogeneous and Clustered Workloads in Clouds: From Fundamental to Autonomic Offering. – Journal of Grid Computing, 2018, pp.1-33.10.1007/s10723-017-9424-0]Search in Google Scholar
[10. Pham, N. M. N., H. H. C. Nguyen. Energy Efficient Resource Allocation for Virtual Services Based on Heterogeneous Shared Hosting Platforms in Cloud Computing. – Cybernetics and Information Technologies, Vol. 17, 2017, pp. 47-58.10.1515/cait-2017-0029]Search in Google Scholar
[11. Mezni, H., A. Hadjali, S. Aridhi. The Uncertain Cloud: State of the Art and Research Challenges. – International Journal of Approximate Reasoning, Vol. 103, 2018, pp. 139-151.10.1016/j.ijar.2018.09.009]Search in Google Scholar
[12. Cayirci, E., A. S. D. Oliveira. Modelling Trust and Risk for Cloud Services. – Journal of Cloud Computing Advances, Systems and Applications, Vol. 7, 2018, pp. 1-14.10.1186/s13677-018-0114-7]Search in Google Scholar
[13. Ouammou, A., B. T. Abdelghani, M. Hanini. Analytical Approach to Evaluate the Impact of Uncertainty in Virtual Machine Placement in a Cloud Computing Environment. 1st Winter School on Complex Systems, Modeling & Simulation, 2018, p. 1.]Search in Google Scholar
[14. Liu, Y., K. Qin, L. Martinez. Improving Decision Making Approaches Based on Fuzzy Soft Sets and Rough Soft Sets. – Applied Soft Computing, Vol. 65, 2018, pp. 320-332.10.1016/j.asoc.2018.01.012]Search in Google Scholar
[15. Danjuma, S., T. Herawan, M. A. Ismail, H. Chiroma, A. I. Abubakar, A. M. Zeki. A Review on Soft Set-Based Parameter Reduction and Decision Making. – IEEE Access, Vol. 5, 2017, pp. 4671-4689.10.1109/ACCESS.2017.2682231]Search in Google Scholar
[16. Nasef, A. A., M. K. El-Sayed. Molodtsov’s Soft Set Theory and Its Applications in Decision Making. – International Journal of Engineering Science Invention, Vol. 6, 2017, pp. 86-90.]Search in Google Scholar
[17. Riaz, M., M. R. Hashmi. Fixed Points of Fuzzy Neutrosophic Soft Mapping with Decision-Making. – Fixed Point Theory and Applications, Vol. 1, 2018, p. 7.10.1186/s13663-018-0632-5]Search in Google Scholar
[18. Deli, I. Interval-Valued Neutrosophic Soft Sets and Its Decision Making. – International Journal of Machine Learning and Cybernetics, Vol. 8, 2017, pp. 665-676.10.1007/s13042-015-0461-3]Search in Google Scholar
[19. Benifa, J. B., D. Dejey. RLPAS: Reinforcement Learning-Based Proactive Auto-Scaler for Resource Provisioning in Cloud Environment. – Mobile Networks and Applications, 2018, pp. 1-16.]Search in Google Scholar
[20. Cheng, M., J. Li, S. Nazarian. DRL-Cloud: Deep Reinforcement Learning-Based Resource Provisioning and Task Scheduling for Cloud Service Providers. – In: Proc. of 23rd Asia and South Pacific Design Automation Conference, 2018, pp. 129-134.10.1109/ASPDAC.2018.8297294]Search in Google Scholar
[21. Gong, Z., X. Gu, J. Wilkes, PRESS: PRedictive Elastic ReSource Scaling for Cloud Systems. – In: 6th IEEE/IFIP International Conference on Network and Service Management (CNSM), 2010, pp. 9-16.]Search in Google Scholar
[22. Ramirez-Velarde, R., A. Tchernykh, C. Barba-Jimenez, A. Hirales-Carba-jal, J. Nolazco-Flores. Adaptive Resource Allocation with Job Runtime Uncertainty. – Journal of Grid Computing, Vol. 15, 2017, pp. 415-434.10.1007/s10723-017-9410-6]Search in Google Scholar
[23. Gandhi, A., P. Dube, A. Karve, A. Kochut, L. Zhang. Model-Driven Optimal Resource Scaling in Cloud. – Software & Systems Modeling, Vol. 17, 2018, pp. 509-526.10.1007/s10270-017-0584-y]Search in Google Scholar
[24. Arabnejad, H., C. Pahl, P. Jamshidi, G. Estrada. A Comparison of Reinforcement Learning Techniques for Fuzzy Cloud Auto-Scaling. – In: Proc. of 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2017, pp. 64-73.10.1109/CCGRID.2017.15]Search in Google Scholar
[25. Sotiriadis, S., N. Bessis, R. Buyya. Self Managed Virtual Machine Scheduling in Cloud Systems. – Information Sciences, Vol. 433, 2018, pp. 381-400.10.1016/j.ins.2017.07.006]Search in Google Scholar
[26. Gawali, M. B., S. K. Shinde. Task Scheduling and Resource Allocation in Cloud Computing Using a Heuristic Approach. – Journal of Cloud Computing, Vol. 7, 2018, pp. 1-16.10.1186/s13677-018-0105-8]Search in Google Scholar
[27. Vozmediano, R. M., R. S. Montero, E. Huedo, I. M. Llorente. Efficient Resource Provisioning for Elastic Cloud Services Based on Machine Learning Techniques. – Journal of Cloud Computing: Advances, Systems and Applications, Vol. 8, 2019, pp. 1-18.10.1186/s13677-019-0128-9]Search in Google Scholar
[28. Bitsakos, C., I. Konstantinou, N. Koziris. A Deep Reinforcement Learning CloudSystem for Elastic Resource Provisioning. – In: Proc. of IEEE International Conference on Cloud Computing Technology and Science (CloudCom), 2018, pp. 21-29.10.1109/CloudCom2018.2018.00020]Search in Google Scholar
[29. Kumar, K. D., E. Umamaheswari. Resource Provisioning in Cloud Computing Using Prediction Models: A Survey. – International Journal of Pure and Applied Mathematics, Vol. 119, 2018, pp. 333-342.]Search in Google Scholar
[30. Thein, T., M. M. Myo, S. Parvin, A. Gawanmeh. Reinforcement Learning Based Methodology for Energy-Efficient Resource Allocation in Cloud Data Centers. – Journal of King Saud University – Computer and Information Sciences, 2018.]Search in Google Scholar
[31. NaIk, K. B., G. M. Gandhi, S. H. Patil. Pareto Based Virtual Machine Selection with Load Balancing in Cloud Data Centre. – Cybernetics and Information Technologies, Vol. 18, 2018, pp. 23-36.10.2478/cait-2018-0036]Search in Google Scholar
[32. Perumal, B., Ra. K. Saravanaguru, A. Murugaiyan. Fuzzy Bio-Inspired Hybrid Techniques for Server Consolidation and Virtual Machine Placement in Cloud Environment. – Cybernetics and Information Technologies, Vol. 17, 2017, pp. 52-68.10.1515/cait-2017-0041]Search in Google Scholar
[33. Mireslami. S., M. Wang, L. Rakai, B. H. Far. Dynamic Cloud Resource Allocation Considering Demand Uncertainty. – IEEE Transactions on Cloud Computing, 2019, pp. 1-14.]Search in Google Scholar