Uma Revisão Sistemática sobre Consolidação de Servidores em Ambientes de Computação em Nuvem via Lógica Fuzzy
Resumo
Esta revisão sistemática investigou o uso da Lógica Fuzzy na consolidação de servidores em Computação em Nuvem. A análise de 7 estudos revelou o estado da arte nessa área e demonstrou que a Lógica Fuzzy é utilizada para a definição dinâmica dos limites e cargas de trabalho de máquinas físicas e virtuais, permitindo a adaptação do modelo às flutuações e facilitando a tomada de decisão sobre migração. Os trabalhos abordam a otimização ou redução do consumo energético como uma preocupação comum. Esse trabalho contribui para a compreensão do papel da Lógica Fuzzy na gestão eficiente de recursos na Computação em Nuvem, fornecendo entendimento sobre as abordagens e aplicações mais relevantes nesse contexto.
Palavras-chave:
Consolidação de servidores, Computação em nuvem, Lógica Fuzzy
Referências
Beloglazov, A. and Buyya, R. (2013). Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Transactions on Parallel and Distributed Systems, 24(7):1366–1379. https://doi.org/10.1109/TPDS.2012.240
Braiki, K. and Youssef, H. (2020). Fuzzy-logic-based multi-objective best-fit-decreasing virtual machine reallocation. The Journal of Supercomputing, 76:427–454. https://doi.org/10.1007/s11227-019-03029-8
Ferdaus, M. H., Murshed, M., Calheiros, R. N., and Buyya, R. (2014). Virtual machine consolidation in cloud data centers using aco metaheuristic. In European conference on parallel processing, pages 306–317. Springer. https://doi.org/10.1007/978-3-319-09873-9_26
Font, J. M. and Hájek, P. (2002). On łukasiewicz’s four-valued modal logic. Studia Logica, 70(2):157–182. https://doi.org/10.1023/A:1015111314455
Jumnal, A. and Kumar, S. D. (2021). Optimal vm placement approach using fuzzy reinforcement learning for cloud data centers. In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV). IEEE. https://doi.org/10.1109/ICICV50876.2021.9388424
Keele, S. et al. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical report, Technical report, v. 2.3 EBSE Technical Report. EBSE.
Long, S., Li, Y., Huang, J., Li, Z., and Li, Y. (2022). A review of energy efficiency evaluation technologies in cloud data centers. Energy and Buildings, page 111848. https://doi.org/10.1016/j.enbuild.2022.111848
Mittal, M., Balas, V. E., Goyal, L. M., and Kumar, R. (2019). Big data processing using spark in cloud. Springer. https://doi.org/10.1007/978-981-13-0550-4
Mongia, V. and Sharma, A. (2021). Performance and resource-aware virtual machine selection using fuzzy in cloud environment. In Progress in Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2020, pages 413–426. Springer. https://doi.org/10.1007/978-981-33-4299-6_34
Moura, B. M. P. d. (2022). Uma Abordagem Flexı́vel para Consolidação Dinâmica de Servidores na Computação em Nuvem Explorando Lógica Fuzzy Valorada Intervalarmente. PhD thesis, Programa de Pós-Graduação em Computação. Universidade Federal de Pelotas.
Moura, B. M., Schneider, G. B., Yamin, A. C., Santos, H., Reiser, R. H., and Bedregal, B. (2022). Interval-valued fuzzy logic approach for overloaded hosts in consolidation of virtual machines in cloud computing. Fuzzy Sets and Systems, 446:144–166. https://doi.org/10.1016/j.fss.2021.03.001
Negi, S., Rauthan, M. M. S., Vaisla, K. S., and Panwar, N. (2021). Cmodlb: an efficient load balancing approach in cloud computing environment. The Journal of Supercomputing, 77. https://doi.org/10.1007/s11227-020-03601-7
Rozehkhani, S. M. and Mahan, F. (2022). Vm consolidation improvement approach using heuristics granular rules in cloud computing environment. Information Sciences, 596. https://doi.org/10.1016/j.ins.2022.02.042
Samriya, J. K., Tiwari, R., Obaidat, M. S., and Bathla, G. (2023). Fuzzy-epo optimization technique for optimised resource allocation and minimum energy consumption with the brownout algorithm. Wireless Personal Communications, 129(4):2633–2651. https://doi.org/10.1007/s11277-023-10250-5
Sowrirajan, R. (2022). A literature based study on cyber security vulnerabilities.
Von Altrock, C. (1996). Fuzzy logic and neurofuzzy applications in business and finance. Prentice-Hall, Inc.
Xu, M., Toosi, A. N., and Buyya, R. (2020). A self-adaptive approach for managing applications and harnessing renewable energy for sustainable cloud computing. IEEE Transactions on Sustainable Computing, 6(4):544–558. https://doi.org/10.1109/TSUSC.2020.3014943
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3):338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex systems and decision processes. Systems, Man and Cybernetics, IEEE Transactions on, (1):28–44. https://doi.org/10.1109/TSMC.1973.5408575
Zadeh, L. A. (1994). Fuzzy logic, neural networks, and soft computing. Communications of the ACM, 37(3):77–84. https://doi.org/10.1145/175247.175255
Zadeh, L. A. (2006). Granular computing–the concept of generalized constraint-based computation. In Rough Sets and Current Trends in Computing: 5th International Conference, RSCTC 2006 Kobe, Japan, November 6-8, 2006 Proceedings 5, pages 12–14. Springer. https://doi.org/10.1007/11908029_2
Braiki, K. and Youssef, H. (2020). Fuzzy-logic-based multi-objective best-fit-decreasing virtual machine reallocation. The Journal of Supercomputing, 76:427–454. https://doi.org/10.1007/s11227-019-03029-8
Ferdaus, M. H., Murshed, M., Calheiros, R. N., and Buyya, R. (2014). Virtual machine consolidation in cloud data centers using aco metaheuristic. In European conference on parallel processing, pages 306–317. Springer. https://doi.org/10.1007/978-3-319-09873-9_26
Font, J. M. and Hájek, P. (2002). On łukasiewicz’s four-valued modal logic. Studia Logica, 70(2):157–182. https://doi.org/10.1023/A:1015111314455
Jumnal, A. and Kumar, S. D. (2021). Optimal vm placement approach using fuzzy reinforcement learning for cloud data centers. In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV). IEEE. https://doi.org/10.1109/ICICV50876.2021.9388424
Keele, S. et al. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical report, Technical report, v. 2.3 EBSE Technical Report. EBSE.
Long, S., Li, Y., Huang, J., Li, Z., and Li, Y. (2022). A review of energy efficiency evaluation technologies in cloud data centers. Energy and Buildings, page 111848. https://doi.org/10.1016/j.enbuild.2022.111848
Mittal, M., Balas, V. E., Goyal, L. M., and Kumar, R. (2019). Big data processing using spark in cloud. Springer. https://doi.org/10.1007/978-981-13-0550-4
Mongia, V. and Sharma, A. (2021). Performance and resource-aware virtual machine selection using fuzzy in cloud environment. In Progress in Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2020, pages 413–426. Springer. https://doi.org/10.1007/978-981-33-4299-6_34
Moura, B. M. P. d. (2022). Uma Abordagem Flexı́vel para Consolidação Dinâmica de Servidores na Computação em Nuvem Explorando Lógica Fuzzy Valorada Intervalarmente. PhD thesis, Programa de Pós-Graduação em Computação. Universidade Federal de Pelotas.
Moura, B. M., Schneider, G. B., Yamin, A. C., Santos, H., Reiser, R. H., and Bedregal, B. (2022). Interval-valued fuzzy logic approach for overloaded hosts in consolidation of virtual machines in cloud computing. Fuzzy Sets and Systems, 446:144–166. https://doi.org/10.1016/j.fss.2021.03.001
Negi, S., Rauthan, M. M. S., Vaisla, K. S., and Panwar, N. (2021). Cmodlb: an efficient load balancing approach in cloud computing environment. The Journal of Supercomputing, 77. https://doi.org/10.1007/s11227-020-03601-7
Rozehkhani, S. M. and Mahan, F. (2022). Vm consolidation improvement approach using heuristics granular rules in cloud computing environment. Information Sciences, 596. https://doi.org/10.1016/j.ins.2022.02.042
Samriya, J. K., Tiwari, R., Obaidat, M. S., and Bathla, G. (2023). Fuzzy-epo optimization technique for optimised resource allocation and minimum energy consumption with the brownout algorithm. Wireless Personal Communications, 129(4):2633–2651. https://doi.org/10.1007/s11277-023-10250-5
Sowrirajan, R. (2022). A literature based study on cyber security vulnerabilities.
Von Altrock, C. (1996). Fuzzy logic and neurofuzzy applications in business and finance. Prentice-Hall, Inc.
Xu, M., Toosi, A. N., and Buyya, R. (2020). A self-adaptive approach for managing applications and harnessing renewable energy for sustainable cloud computing. IEEE Transactions on Sustainable Computing, 6(4):544–558. https://doi.org/10.1109/TSUSC.2020.3014943
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3):338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex systems and decision processes. Systems, Man and Cybernetics, IEEE Transactions on, (1):28–44. https://doi.org/10.1109/TSMC.1973.5408575
Zadeh, L. A. (1994). Fuzzy logic, neural networks, and soft computing. Communications of the ACM, 37(3):77–84. https://doi.org/10.1145/175247.175255
Zadeh, L. A. (2006). Granular computing–the concept of generalized constraint-based computation. In Rough Sets and Current Trends in Computing: 5th International Conference, RSCTC 2006 Kobe, Japan, November 6-8, 2006 Proceedings 5, pages 12–14. Springer. https://doi.org/10.1007/11908029_2
Publicado
09/10/2023
Como Citar
BASTOS, Rafael R.; SEIBERT, Vagner A.; SILVA, Gabriel R.; MOURA, Bruno M. P.; LUCCA, Giancarlo; YAMIN, Adenauer C.; REISER, Renata H. R..
Uma Revisão Sistemática sobre Consolidação de Servidores em Ambientes de Computação em Nuvem via Lógica Fuzzy. In: WORKSHOP-ESCOLA DE INFORMÁTICA TEÓRICA (WEIT), 7. , 2023, Rio Grande/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 120-128.
DOI: https://doi.org/10.5753/weit.2023.26605.
