1. bookVolumen 22 (2022): Edición 4 (November 2022)
Detalles de la revista
License
Formato
Revista
eISSN
1314-4081
Primera edición
13 Mar 2012
Calendario de la edición
4 veces al año
Idiomas
Inglés
Acceso abierto

Fuzzy Neutrosophic Soft Set Based Transfer-Q-Learning Scheme for Load Balancing in Uncertain Grid Computing Environments

Publicado en línea: 10 Nov 2022
Volumen & Edición: Volumen 22 (2022) - Edición 4 (November 2022)
Páginas: 35 - 55
Recibido: 25 Mar 2022
Aceptado: 12 Oct 2022
Detalles de la revista
License
Formato
Revista
eISSN
1314-4081
Primera edición
13 Mar 2012
Calendario de la edición
4 veces al año
Idiomas
Inglés

1. Singh, M. An Overview of Grid Computing. – In: Proc. of International Conference on Computing, Communication, and Intelligent Systems (ICCCIS’19), 2019.10.1109/ICCCIS48478.2019.8974490 Search in Google Scholar

2. Sungkar, A., T. Kogoya. A Review of Grid Computing. – Computer Science & IT Research Journal, Vol. 1, 2020.10.51594/csitrj.v1i1.128 Search in Google Scholar

3. Dakkak, O., S. A. Nor, S. Arif, Y. Fazea. Improving QoS for Non-Trivial Applications in Grid Computing. – In: Proc. of International Conference of Reliable Information and Communication Technology, 2019.10.1007/978-3-030-33582-3_52 Search in Google Scholar

4. Foster, I., C. Kesselman. Translating the Grid: How a Translational Approach Shaped the Development of Grid Computing. – Journal of Computational Science, Vol. 52, 2021.10.1016/j.jocs.2020.101214 Search in Google Scholar

5. Aswal, M. S. VM Consolidation Plan for Improving the Energy Efficiency of Cloud. – Cybernetics and Information Technologies, Vol. 21, 2021, No 3, pp. 145-159.10.2478/cait-2021-0035 Search in Google Scholar

6. Dhingra, S., P. Bansal. Employing Divergent Machine Learning Classifiers to Upgrade the Preciseness of Image Retrieval Systems. – Cybernetics and Information Technologies, Vol. 20, 2020, No 3.10.2478/cait-2020-0029 Search in Google Scholar

7. Kara, N., H. G. Kocken. A Fuzzy Approach to Multi-Objective Solid Transportation Problem with Mixed Constraints Using Hyperbolic Membership Function. – Cybernetics and Information Technologies, Vol. 21, 2021, No 4, pp. 158-167.10.2478/cait-2021-0049 Search in Google Scholar

8. Kouadri, A., M. Hajji, M. F. Harkat, K. Abodayeh, M. Mansouri, H. Nounou, M. Nounou. Hidden Markov Model Based Principal Component Analysis for Intelligent Fault Diagnosis of Wind Energy Converter Systems. – Renewable Energy, Vol. 150, 2020.10.1016/j.renene.2020.01.010 Search in Google Scholar

9. Goh, C. Y., J. Dauwels, N. Mitrovic, M. T. Asif, A. Oran, P. Jaillet. Online Map-Matching Based on Hidden Markov Model for Real-Time Traffic Sensing Applications. – In: Proc. of 15th International IEEE Conference on Intelligent Transportation Systems, 2012.10.1109/ITSC.2012.6338627 Search in Google Scholar

10. Mor, B., S. Garhwal, A. Kumar. A Systematic Review of Hidden Markov Models and Their Applications. – Archives of Computational Methods in Engineering, Vol. 28, 2021.10.1007/s11831-020-09422-4 Search in Google Scholar

11. Deli, I., S. Broumi. Neutrosophic Soft Matrices and NSM-Decision Making. – Journal of Intelligent & Fuzzy Systems, Vol. 28, 2015.10.3233/IFS-141505 Search in Google Scholar

12. Kokoç, M., S. Ersoz. New Ranking Functions for Interval-Valued Intuitionistic Fuzzy Sets and Their Application to Multi-Criteria Decision-Making Problem. – Cybernetics and Information Technologies, Vol. 21, 2021, No 1, pp. 3-18.10.2478/cait-2021-0001 Search in Google Scholar

13. Deli, I., S. Eraslan, N. Çagman. IVNPIV-Neutrosophic Soft Sets and Their Decision Making Based on Similarity Measure. – Neural Computing and Applications, Vol. 29, 2018.10.1007/s00521-016-2428-z Search in Google Scholar

14. Ali, M., L. H. Son, I. Deli, N. D. Tien. Bipolar Neutrosophic Soft Sets and Applications in Decision Making. – Journal of Intelligent & Fuzzy Systems, Vol. 33, 2017.10.3233/JIFS-17999 Search in Google Scholar

15. Deli, I., S. Broumi. Neutrosophic Soft Relations and Some Properties. – Annals of Fuzzy Mathematics and Informatics, Vol. 9, 2015. Search in Google Scholar

16. Singh, S., S. Lalotra, A. H. Ganie. On Some Knowledge Measures of Intuitionistic Fuzzy Sets of Type-Two with Application to MCDM. – Cybernetics and Information Technologies, Vol. 20, 2020, No 1, pp. 3-20.10.2478/cait-2020-0001 Search in Google Scholar

17. Naeem, K., M. Riaz, D. Afzal. Fuzzy Neutrosophic Soft σ-Algebra and Fuzzy Neutrosophic Soft Measure with Applications. – Journal of Intelligent & Fuzzy Systems, Vol. 39, 2020.10.3233/JIFS-191062 Search in Google Scholar

18. Fan, J., Z. Wang, Y. Xie, Z. Yang. A Theoretical Analysis of Deep Q-Learning. – In: Learning for Dynamics and Control, 2020. Search in Google Scholar

19. Samma, H., J. Mohamad-Saleh, S. A. Suandi, B. Lahasan. Q-Learning-Based Simulated Annealing Algorithm for Constrained Engineering Design Problems. – Neural Computing and Applications, Vol. 32, 2020, pp. 5147-5161.10.1007/s00521-019-04008-z Search in Google Scholar

20. Wang, Y., Y. Liu, W. Chen, Z. M. Ma, T. Y. Liu. Target Transfer Q-Learning and Its Convergence Analysis. – Neurocomputing, Vol. 392, 2020.10.1016/j.neucom.2020.02.117 Search in Google Scholar

21. Jeong, G., H. Y. Kim. Improving Financial Trading Decisions Using Deep Q-Learning: Predicting the Number of Shares, Action Strategies, and Transfer Learning. – Expert Systems with Applications, Vol. 117, 2019.10.1016/j.eswa.2018.09.036 Search in Google Scholar

22. Khan, S., B. Nazir, I. A. Khan, S. Shamshirband, A. T. Chronopoulos. Load Balancing in Grid Computing: Taxonomy, Trends and Opportunities. – Journal of Network and Computer Applications, Vol. 88, 2017.10.1016/j.jnca.2017.02.013 Search in Google Scholar

23. Wenjie, T., Y. Yiping, Z. Feng, L. Tianlin, S. Xiao. A Work-Stealing Based Dynamic Load Balancing Algorithm for Conservative Parallel Discrete Event Simulation. – In: Proc. of Winter Simulation Conference (WSC’17), 2017.10.1109/WSC.2017.8247833 Search in Google Scholar

24. Wu, J., X. Xu, P. Zhang, C. Liu. A Novel Multi-Agent Reinforcement Learning Approach for Job Scheduling in Grid Computing. – Future Generation Computer Systems, Vol. 27, 2011.10.1016/j.future.2010.10.009 Search in Google Scholar

25. Hajoui, Y., O. Bouattane, M. Youssfi, E. Illoussamen. Q-Learning Applied to the Problem of Scheduling on Heterogeneous Architectures. – International Journal of Computer Science and Network Security, Vol. 18, 2018. Search in Google Scholar

26. Garcia-Galan, S., R. P. Prado, J. M. Expósito. Fuzzy Scheduling with Swarm Intelligence-Based Knowledge Acquisition for Grid Computing. – Engineering Applications of Artificial Intelligence, Vol. 25, 2012.10.1016/j.engappai.2011.11.002 Search in Google Scholar

27. Tang, K., W. Jiang, R. Cui, Y. Wu. A Memory-Based Task Scheduling Algorithm for Grid Computing Based on Heterogeneous Platform and Homogeneous Tasks. – International Journal of Web and Grid Services, Vol. 16, 2020.10.1504/IJWGS.2020.109473 Search in Google Scholar

28. Patni, J. C. Centralized Approach of Load Balancing in Homogenous Grid Computing Environment. – In: Proc. of 3rd International Conference on Computers in Management and Business, 2020, pp. 151-156.10.1145/3383845.3383877 Search in Google Scholar

29. Ali, W., F. Bouakkaz. Agent Based Load Balancing in Grid Computing. – In: Proc. of Multi-Agent Systems-Theory, Implementation and Applications. IntechOpen, 2020.10.5772/intechopen.94219 Search in Google Scholar

30. Liu, F., D. Janssens, J. Cui, G. Wets, M. Cools. Characterizing Activity Sequences Using Profile Hidden Markov Models. – Expert Systems with Applications, Vol. 42, 2015.10.1016/j.eswa.2015.02.057 Search in Google Scholar

31. Walker, C. R., A. Scally, N. De Maio, N. Goldman. Short-Range Template Switching in Great Ape Genomes Explored Using Pair Hidden Markov Models. – PloS Genetics, Vol. 17, 2021.10.1371/journal.pgen.1009221795435633651813 Search in Google Scholar

32. Braun, T. D., et al. A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems. – J. Parallel Distrib. Comput., Vol. 61, 2001, No 6, pp. 810-837.10.1006/jpdc.2000.1714 Search in Google Scholar

33. Lebre, A., A. Legrand, F. Suter, P. Veyre. Adding Storage Simulation Capacities to the Simgrid Toolkit: Concepts, Models, and Api. – In: Proc. of 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2015, pp. 251-260. Search in Google Scholar

34. Cordery, J. L., D. Morrison, B. M. Wright, T. D. Wall. The Impact of Autonomy and Task Uncertainty on Team Performance: A Longitudinal Field Study. – Journal of Organizational Behavior, Vol. 31, 2010.10.1002/job.657 Search in Google Scholar

35. Real, R., A. Yamin, L. da Silva, G. Frainer, I. Augustin, J. Barbosa, C. Geyer. Resource Scheduling on Grid: Handling Uncertainty. – In: Proc. of 1st Latin American Web Congress, 2003. Search in Google Scholar

Artículos recomendados de Trend MD

Planifique su conferencia remota con Sciendo