Open Access

Uncertainty Aware Resource Provisioning Framework for Cloud Using Expected 3-SARSA Learning Agent: NSS and FNSS Based Approach


Cite

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.2017.2711009Search 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/s10586-018-2807-6Search 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.2017.72Search 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-V60P209Search 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-2017-0029Search 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-2018-0031Search 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/s10723-017-9424-0Search 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-2017-0029Search 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.2018.09.009Search 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/s13677-018-0114-7Search 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.2018.01.012Search 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.2017.2682231Search 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/s13663-018-0632-5Search 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/s13042-015-0461-3Search 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.2018.8297294Search 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/s10723-017-9410-6Search 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-ySearch 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.2017.15Search 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.2017.07.006Search 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/s13677-018-0105-8Search 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/s13677-019-0128-9Search 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/CloudCom2018.2018.00020Search 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-2018-0036Search 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-2017-0041Search 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

eISSN:
1314-4081
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Computer Sciences, Information Technology