Hybrid-FLBCC: Integrando Aprendizado de Máquina e Lógica Fuzzy para Consolidação de Servidores em Nuvem
Resumo
A abordagem Hybrid-FLBCC propõe um modelo fuzzy integrado a algoritmos de Aprendizado de Máquina, permitindo a seleção dinâmica de variáveis e a geração automática de regras básicas para prever migrações ideais de Máquinas Virtuais. Como extensão da Int-FLBCC, adota o conceito de Computação Flexível para aprimorar a consolidação de servidores em ambientes de Computação em Nuvem, com foco no refinamento do processo de inferência sobre os níveis de utilização de Máquinas Físicas. A seleção de variáveis identifica atributos mais relevantes para a consolidação, orientando a definição de critérios decisórios. Esse aprimoramento gera regras fuzzy mais interpretáveis, simplifica a implementação do sistema e contribui para a redução do consumo de recursos computacionais. Os resultados demonstram o potencial de abordagens híbridas na tomada de decisões inteligentes em infraestruturas em nuvem.
Palavras-chave:
computação flexível, lógica fuzzy, aprendizado de máquina, computação em nuvem, consolidação de servidores
Referências
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Bradley, A. P. (1997). The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7):1145–1159. DOI: 10.1016/S0031-3203(96)00142-2
Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., and Buyya, R. (2010). Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 41(1):23–50. DOI: 10.1002/spe.995
Chandrashekar, G. and Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1):16–28. 40th-year commemorative issue. DOI: 10.1016/j.compeleceng.2013.11.024
Chi, Z., Yan, H., and Pham, T. (1996). Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition. World Scientific, Singapore.
Choudhary, A., Rajak, R., and Prakash, S. (2024). A critical review and analysis of load balancing methods in cloud computing environment. Wirel. Pers. Commun., 137(4):2145–2165. DOI: 10.1007/s11277-024-11479-4
Cordón, O., del Jesús, M. J., and Herrera, F. (1998). Analyzing the reasoning mechanisms in fuzzy rule based classification systems. Mathware and Soft Computing, 5(2-3):321–332.
Faceli, K., Lorena, A. C., Gama, J., de Carvalho, A. C. P. d. L. F., and de Almeida, T. A. (2021). Inteligência Artificial - Uma Abordagem de Aprendizado de Máquina. LTC, 2a edição edition.
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Gourisaria, M. K., Samanta, A., Saha, A., Patra, S. S., and Khilar, P. M. (2020). An extensive review on cloud computing. Data Engineering and Communication Technology, pages 53–78. DOI: 10.1007/978-981-15-1097-7_6
He, T. and Buyya, R. (2023). A taxonomy of live migration management in cloud computing. ACM Computing Surveys, 56(3):1–33. DOI: 10.1145/3615353
Hühn, J. and Hüllermeier, E. (2009). Furia: an algorithm for unordered fuzzy rule induction. Data Mining and Knowledge Discovery, 19(3):293–319. DOI: 10.1007/s10618-009-0131-8
Ishibuchi, H., Nakashima, T., and Nii, M. (2005). Classification and Modeling with Linguistic Information Granules Advanced Approaches to Linguistic Data Mining. Advanced Information Processing. Springer, Berlin. DOI: 10.1007/b138232
Lucca, G., Sanz, J. A., Dimuro, G. P., Bedregal, B., and Bustince, H. (2018). A proposal for tuning the α parameter in cαc-integrals for application in fuzzy rule-based classification systems. Natural Computing, 19(3):533–546. DOI: 10.1007/s11047-018-9678-x
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. DOI: 10.1016/j.fss.2021.03.001
Nathani, A., Chaudhary, S., and Somani, G. (2012). Policy based resource allocation in iaas cloud. Future Generation Computer Systems, 28(1):94–103. DOI: 10.1016/j.future.2011.05.016
Raschka, S. (2020). Python machine learning. Packt Publishing, Birmingham, 3rd ed. edition. Includes bibliographical references and index.
Rjeib, H. D. and Kecskemeti, G. (2024). VMP-ER: an efficient virtual machine placement algorithm for energy and resources optimization in cloud data center. Algorithms, 17(7):295. DOI: 10.3390/a17070295
Sambuc, R. (1975). Fonctions ϕ-floues. Application l’aide au Diagnostic en Pathologie Thyroidienne. PhD thesis, Marseille.
Sanz, J., Sesma-Sara, M., and Bustince, H. (2021). A fuzzy association rule-based classifier for imbalanced classification problems. Information Sciences, 577:265–279. DOI: 10.1016/j.ins.2021.07.019
Sanz, J. A., Fernández, A., Bustince, H., and Herrera, F. (2013). Ivturs: A linguistic fuzzy rule-based classification system based on a new interval-valued fuzzy reasoning method with tuning and rule selection. IEEE Transactions on Fuzzy Systems, 21(3):399–411. DOI: 10.1109/TFUZZ.2013.2243153
Souza, A. R. R. d. (2024). Deterioração Clínica em Unidades de Terapia Intensiva: uma Abordagem de Predição de Situação dos Pacientes Baseada em Inteligência Computacional. PhD thesis, Universidade Federal de Pelotas.
Triguero, I., González, S., Moyano, J. M., García López, S., Alcalá Fernández, J., Luengo Martín, J., Fernández Hilario, A. L., Jesús Díaz, M. J. d., Sánchez, L., Herrera Triguero, F., et al. (2017). Keel 3.0: An open source software for multi-stage analysis in data mining. International Journal of Computational Intelligence Systems, 10(1):1238–1249. DOI: 10.2991/ijcis.10.1.82
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3):338–353. DOI: 10.1016/S0019-9958(65)90241-X
Arshad, U., Aleem, M., Srivastava, G., and Lin, J. C.-W. (2022). Utilizing power consumption and sla violations using dynamic vm consolidation in cloud data centers. Renewable and Sustainable Energy Reviews, 167:112782. DOI: 10.1016/j.rser.2022.112782
Banerjee, S., Roy, S., and Khatua, S. (2024). Towards energy and qos aware dynamic vm consolidation in a multi-resource cloud. Future Generation Computer Systems, 157:376–391. DOI: 10.1016/j.future.2024.03.058
Bastos, R., Seibert, V., Maia, G., de Moura, B. P., Lucca, G., Yamin, A., and Reiser, R. (2024). Exploratory data analysis in cloud computing environments for server consolidation via fuzzy classification models. In Proceedings of the 26th International Conference on Enterprise Information Systems, pages 636–643. INSTICC, SCITEPRESS - Science and Technology Publications. DOI: 10.5220/0012615900003690
Bradley, A. P. (1997). The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7):1145–1159. DOI: 10.1016/S0031-3203(96)00142-2
Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., and Buyya, R. (2010). Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 41(1):23–50. DOI: 10.1002/spe.995
Chandrashekar, G. and Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1):16–28. 40th-year commemorative issue. DOI: 10.1016/j.compeleceng.2013.11.024
Chi, Z., Yan, H., and Pham, T. (1996). Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition. World Scientific, Singapore.
Choudhary, A., Rajak, R., and Prakash, S. (2024). A critical review and analysis of load balancing methods in cloud computing environment. Wirel. Pers. Commun., 137(4):2145–2165. DOI: 10.1007/s11277-024-11479-4
Cordón, O., del Jesús, M. J., and Herrera, F. (1998). Analyzing the reasoning mechanisms in fuzzy rule based classification systems. Mathware and Soft Computing, 5(2-3):321–332.
Faceli, K., Lorena, A. C., Gama, J., de Carvalho, A. C. P. d. L. F., and de Almeida, T. A. (2021). Inteligência Artificial - Uma Abordagem de Aprendizado de Máquina. LTC, 2a edição edition.
Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly, Beijing, second edition edition.
Gourisaria, M. K., Samanta, A., Saha, A., Patra, S. S., and Khilar, P. M. (2020). An extensive review on cloud computing. Data Engineering and Communication Technology, pages 53–78. DOI: 10.1007/978-981-15-1097-7_6
He, T. and Buyya, R. (2023). A taxonomy of live migration management in cloud computing. ACM Computing Surveys, 56(3):1–33. DOI: 10.1145/3615353
Hühn, J. and Hüllermeier, E. (2009). Furia: an algorithm for unordered fuzzy rule induction. Data Mining and Knowledge Discovery, 19(3):293–319. DOI: 10.1007/s10618-009-0131-8
Ishibuchi, H., Nakashima, T., and Nii, M. (2005). Classification and Modeling with Linguistic Information Granules Advanced Approaches to Linguistic Data Mining. Advanced Information Processing. Springer, Berlin. DOI: 10.1007/b138232
Lucca, G., Sanz, J. A., Dimuro, G. P., Bedregal, B., and Bustince, H. (2018). A proposal for tuning the α parameter in cαc-integrals for application in fuzzy rule-based classification systems. Natural Computing, 19(3):533–546. DOI: 10.1007/s11047-018-9678-x
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. DOI: 10.1016/j.fss.2021.03.001
Nathani, A., Chaudhary, S., and Somani, G. (2012). Policy based resource allocation in iaas cloud. Future Generation Computer Systems, 28(1):94–103. DOI: 10.1016/j.future.2011.05.016
Raschka, S. (2020). Python machine learning. Packt Publishing, Birmingham, 3rd ed. edition. Includes bibliographical references and index.
Rjeib, H. D. and Kecskemeti, G. (2024). VMP-ER: an efficient virtual machine placement algorithm for energy and resources optimization in cloud data center. Algorithms, 17(7):295. DOI: 10.3390/a17070295
Sambuc, R. (1975). Fonctions ϕ-floues. Application l’aide au Diagnostic en Pathologie Thyroidienne. PhD thesis, Marseille.
Sanz, J., Sesma-Sara, M., and Bustince, H. (2021). A fuzzy association rule-based classifier for imbalanced classification problems. Information Sciences, 577:265–279. DOI: 10.1016/j.ins.2021.07.019
Sanz, J. A., Fernández, A., Bustince, H., and Herrera, F. (2013). Ivturs: A linguistic fuzzy rule-based classification system based on a new interval-valued fuzzy reasoning method with tuning and rule selection. IEEE Transactions on Fuzzy Systems, 21(3):399–411. DOI: 10.1109/TFUZZ.2013.2243153
Souza, A. R. R. d. (2024). Deterioração Clínica em Unidades de Terapia Intensiva: uma Abordagem de Predição de Situação dos Pacientes Baseada em Inteligência Computacional. PhD thesis, Universidade Federal de Pelotas.
Triguero, I., González, S., Moyano, J. M., García López, S., Alcalá Fernández, J., Luengo Martín, J., Fernández Hilario, A. L., Jesús Díaz, M. J. d., Sánchez, L., Herrera Triguero, F., et al. (2017). Keel 3.0: An open source software for multi-stage analysis in data mining. International Journal of Computational Intelligence Systems, 10(1):1238–1249. DOI: 10.2991/ijcis.10.1.82
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3):338–353. DOI: 10.1016/S0019-9958(65)90241-X
Publicado
10/09/2025
Como Citar
BASTOS, Rafael R.; MOURA, Bruno M. P.; LUCCA, Giancarlo; SANTOS, Helida S.; YAMIN, Adenauer C.; REISER, Renata H. R..
Hybrid-FLBCC: Integrando Aprendizado de Máquina e Lógica Fuzzy para Consolidação de Servidores em Nuvem. In: WORKSHOP-ESCOLA DE INFORMÁTICA TEÓRICA (WEIT), 8. , 2025, Ponta Grossa/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 74-83.
