Estudo de Casos para Consolidação de Servidores na Computação em Nuvem baseada em ANFIS
Resumo
A Computação em Nuvem é viabilizada por centros de dados e infraestruturas computacionais, com a premissa de que os custos estão diretamente relacionados às taxas de uso dos recursos, com controle expressivo do consumo de energia gerado pelas demandas de serviços. Este artigo explora os desafios para obter a consolidação de servidores na Computação em Nuvem, com ênfase na otimização do consumo energético. O trabalho visa desenvolver uma abordagem flexível que promova a otimização do gerenciamento de recursos na Computação em Nuvem, via técnicas de Redes Neurais, considerando novos resultados aplicando um sistema de inferência neuro-fuzzy adaptativo.
Palavras-chave:
Lógica Fuzzy, Computação em Nuvem, Máquina Virtual, Rede Neural, ANFIS
Referências
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Alaei, M., Khorsand, R., and Ramezanpour, M. (2021). An adaptive fault detector strategy for scientific workflow scheduling based on improved differential evolution algorithm in cloud. Applied Soft Computing, 99:106895. DOI: 10.1016/j.asoc.2020.106895
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
Bastos, R. R. (2025). Hybrid-FLBCC: Uma Abordagem para o Gerenciamento de Recursos na Computação em Nuvem integrando a Lógica Fuzzy Valorada Intervalarmente e Aprendizado de Máquina. Tese, Centro de Desenvolvimento Tecnológico. Universidade Federal de Pelotas, Pelotas. Tese de Doutorado em desenvolvimento.
Beloglazov, A. and Buyya, R. (2012). Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency and Computation: Practice and Experience, 24(13):1397–1420. DOI: 10.1002/cpe.1867
Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., and Buyya, R. (2011). 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
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321–357. DOI: 10.1613/jair.953
Costache, R., T, ı̂ncu, R., Elkhrachy, I., Pham, Q. B., Popa, M. C., Diaconu, D. C., Avand, M., Costache, I., Arabameri, A., and Bui, D. T. (2020). New neural fuzzy-based machine learning ensemble for enhancing the prediction accuracy of flood susceptibility mapping. Hydrological Sciences Journal, 65(16):2816–2837. DOI: 10.1080/02626667.2020.1842412
Fawcett, T. (2006). An introduction to roc analysis. Pattern recognition letters, 27(8):861–874. DOI: 10.1016/j.patrec.2005.10.010
Guyon, I. and Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(Mar):1157–1182.
Jang, J., Sun, C., and Mizutani, E. (1997). Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence. IEEE Transactions on Automatic Control, 42(10):1482–1484. DOI: 10.1109/TAC.1997.633847
Jang, J.-S. (1993). Anfis: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3):665–685. DOI: 10.1109/21.256541
Jang, J.-S. (1996). Input selection for anfis learning. In Proceedings of IEEE 5th International Fuzzy Systems, volume 2, pages 1493–1499 vol.2. DOI: 10.1109/FUZZY.1996.552396
Karaboga, D. and Kaya, E. (2019). Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artificial Intelligence Review, 52:2263–2293. DOI: 10.1007/s10462-017-9610-2
Kavis, M. J. (2014). Architecting the cloud: design decisions for cloud computing service models (SaaS, PaaS, and IaaS). John Wiley & Sons.
Khan, A. A. and Zakarya, M. (2021). Energy, performance and cost efficient cloud data-centres: A survey. Computer Science Review, 40:100390. DOI: 10.1016/j.cosrev.2021.100390
Kuhn, M., Johnson, K., et al. (2013). Applied predictive modeling, volume 26. Springer. DOI: 10.1007/978-1-4614-6849-3
Kusumadewi, S., Rosita, L., and Wahyuni, E. G. (2023). Stability of classification performance on an adaptive neuro fuzzy inference system for disease complication prediction. Int J Artif Intell ISSN, 2252(8938):8938. DOI: 10.11591/ijai.v12.i2.pp532-542
Lemaître, G., Nogueira, F., and Aridas, C. (2024). Common pitfalls — imbalanced-learn 0.11.0 documentation. [link]. Acesso em: 13 junho. 2025.
Markoulidakis, I., Kopsiaftis, G., Rallis, I., and Georgoulas, I. (2021). Multi-class confusion matrix reduction method and its application on net promoter score classification problem. In Proceedings of the 14th PErvasive technologies related to assistive environments conference, pages 412–419. DOI: 10.1145/3453892.3461323
Moura, B. (2022). Uma Abordagem Flexível para Consolidação Dinâmica de Servidores na Computação em Nuvem Explorando Lógica Fuzzy Valorada Intervalarmente. Tese, Centro de Desenvolvimento Tecnológico. Universidade Federal de Pelotas, Pelotas. Disponível em: [link]. Acesso em: 11 março. 2025.
Park, K. and Pai, V. S. (2006). Comon: a mostly-scalable monitoring system for planetlab. ACM SIGOPS operating systems review, 40(1):65–74. DOI: 10.1145/1113361.1113374
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2025a). skle-arn.metrics.accuracy score - scikit-learn 1.7.1 documentation. Acesso em: 27 agosto. 2025. [link]
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2025b). sskle-arn.metrics.classification report - scikit-learn 1.7.1 documentation. Acesso em: 27 agosto. 2025. DOI: 10.1016/j.measen.2023.100965
Prathiba, S. and Sankar, S. (2024). Energy-efficient resource allocation in cloud infrastructure using L3F-MGA and E-ANFIS. Measurement: Sensors, 31:100965. DOI: 10.1002/9781119994374.ch3
Ross, T. J. (2010). Classical Relations and Fuzzy Relations. John Wiley and Sons, Ltd, USA. DOI: 10.3390/info15100656
Saatchi, R. (2024). Fuzzy logic concepts, developments and implementation. Information, 15(10). DOI: 10.53555/AJBR.v27i4S.4345
Sathyanarayanan, S. and Tantri, B. R. (2024). Confusion matrix-based performance evaluation metrics. African Journal of Biomedical Research, pages 4023–4031. DOI: 10.36227/techrxiv.170473729.96561302/v1
Saxena, D. and Singh, A. K. (2024). A comprehensive survey on sustainable resource management in cloud computing environments. DOI: 10.1016/j.bspc.2021.103417
Shoeibi, A., Ghassemi, N., Khodatars, M., Moridian, P., Alizadehsani, R., Zare, A., Khosravi, A., Subasi, A., Acharya, U. R., and Gorriz, J. M. (2022). Detection of epileptic seizures on eeg signals using anfis classifier, autoencoders and fuzzy entropies. Biomedical Signal Processing and Control, 73:103417.
Singh, H. and Lone, Y. A. (2020). Deep neuro-fuzzy systems with python. Apress, Berkeley. DOI: 10.1142/S0218001409007326
Sun, Y., Wong, A. K., and Kamel, M. S. (2009). Classification of imbalanced data: A review. International journal of pattern recognition and artificial intelligence, 23(04):687–719. DOI: 10.1109/TSMC.1985.6313399
Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE transactions on systems, man, and cybernetics, pages 116–132.
Tan, P.-N., Steinbach, M., and Kumar, V. (2005). Introduction to Data Mining, (First Edition). Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA. DOI: 10.3390/w10091210
Tien Bui, D., Khosravi, K., Li, S., Shahabi, H., Panahi, M., Singh, V. P., Chapi, K., Shirzadi, A., Panahi, S., Chen, W., and Bin Ahmad, B. (2018). New hybrids of anfis with several optimization algorithms for flood susceptibility modeling. Water, 10(9). DOI: 10.1109/ICOCI.2006.5276608
Toosi, A. N., Kahani, M., and Monsefi, R. (2006). Network intrusion detection based on neuro-fuzzy classification. In 2006 International Conference on Computing & Informatics, pages 1–5. IEEE. DOI: 10.1177/1550147721997218
Uddin, M., Hamdi, M., Alghamdi, A., Alrizq, M., Memon, M. S., Abdelhaq, M., and Alsaqour, R. (2021). Server consolidation: A technique to enhance cloud data center power efficiency and overall cost of ownership. International Journal of Distributed Sensor Networks, 17(3):1550147721997218. DOI: 10.5120/ijca2015905635
Walia, N., Singh, H., and Sharma, A. (2015). ANFIS: Adaptive Neuro-Fuzzy Inference System - A Survey. International Journal of Computer Applications, 123(13). DOI: 10.1007/978-1-4615-0377-4_5
Wang, S.-C. (2003). Artificial Neural Network, pages 81–100. Springer US, Boston, MA. DOI: 10.1007/s12083-020-00885-7
Wu, X., Wang, H., Tan, W., Wei, D., and Shi, M. (2020a). Dynamic allocation strategy of VM resources with fuzzy transfer learning method. Peer-to-Peer Networking and Applications, 13(6):2201 – 2213. DOI: 10.1016/j.comcom.2019.11.015
Wu, X., Wang, H., Wei, D., and Shi, M. (2020b). ANFIS with natural language processing and gray relational analysis based cloud computing framework for real time energy efficient resource allocation. Computer Communications, 150:122–130. DOI: 10.1016/S0019-9958(65)90241-X
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3):338–353. DOI: 10.1109/2.53
Zadeh, L. A. (1988). Fuzzy logic. Computer, 21(4):83–93. DOI: 10.1145/175247.175255
Zadeh, L. A. (1994). Fuzzy logic, neural networks, and soft computing. Communications of the ACM, 37(3):77–84.
Alaei, M., Khorsand, R., and Ramezanpour, M. (2021). An adaptive fault detector strategy for scientific workflow scheduling based on improved differential evolution algorithm in cloud. Applied Soft Computing, 99:106895. DOI: 10.1016/j.asoc.2020.106895
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
Bastos, R. R. (2025). Hybrid-FLBCC: Uma Abordagem para o Gerenciamento de Recursos na Computação em Nuvem integrando a Lógica Fuzzy Valorada Intervalarmente e Aprendizado de Máquina. Tese, Centro de Desenvolvimento Tecnológico. Universidade Federal de Pelotas, Pelotas. Tese de Doutorado em desenvolvimento.
Beloglazov, A. and Buyya, R. (2012). Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency and Computation: Practice and Experience, 24(13):1397–1420. DOI: 10.1002/cpe.1867
Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., and Buyya, R. (2011). 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
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321–357. DOI: 10.1613/jair.953
Costache, R., T, ı̂ncu, R., Elkhrachy, I., Pham, Q. B., Popa, M. C., Diaconu, D. C., Avand, M., Costache, I., Arabameri, A., and Bui, D. T. (2020). New neural fuzzy-based machine learning ensemble for enhancing the prediction accuracy of flood susceptibility mapping. Hydrological Sciences Journal, 65(16):2816–2837. DOI: 10.1080/02626667.2020.1842412
Fawcett, T. (2006). An introduction to roc analysis. Pattern recognition letters, 27(8):861–874. DOI: 10.1016/j.patrec.2005.10.010
Guyon, I. and Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(Mar):1157–1182.
Jang, J., Sun, C., and Mizutani, E. (1997). Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence. IEEE Transactions on Automatic Control, 42(10):1482–1484. DOI: 10.1109/TAC.1997.633847
Jang, J.-S. (1993). Anfis: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3):665–685. DOI: 10.1109/21.256541
Jang, J.-S. (1996). Input selection for anfis learning. In Proceedings of IEEE 5th International Fuzzy Systems, volume 2, pages 1493–1499 vol.2. DOI: 10.1109/FUZZY.1996.552396
Karaboga, D. and Kaya, E. (2019). Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artificial Intelligence Review, 52:2263–2293. DOI: 10.1007/s10462-017-9610-2
Kavis, M. J. (2014). Architecting the cloud: design decisions for cloud computing service models (SaaS, PaaS, and IaaS). John Wiley & Sons.
Khan, A. A. and Zakarya, M. (2021). Energy, performance and cost efficient cloud data-centres: A survey. Computer Science Review, 40:100390. DOI: 10.1016/j.cosrev.2021.100390
Kuhn, M., Johnson, K., et al. (2013). Applied predictive modeling, volume 26. Springer. DOI: 10.1007/978-1-4614-6849-3
Kusumadewi, S., Rosita, L., and Wahyuni, E. G. (2023). Stability of classification performance on an adaptive neuro fuzzy inference system for disease complication prediction. Int J Artif Intell ISSN, 2252(8938):8938. DOI: 10.11591/ijai.v12.i2.pp532-542
Lemaître, G., Nogueira, F., and Aridas, C. (2024). Common pitfalls — imbalanced-learn 0.11.0 documentation. [link]. Acesso em: 13 junho. 2025.
Markoulidakis, I., Kopsiaftis, G., Rallis, I., and Georgoulas, I. (2021). Multi-class confusion matrix reduction method and its application on net promoter score classification problem. In Proceedings of the 14th PErvasive technologies related to assistive environments conference, pages 412–419. DOI: 10.1145/3453892.3461323
Moura, B. (2022). Uma Abordagem Flexível para Consolidação Dinâmica de Servidores na Computação em Nuvem Explorando Lógica Fuzzy Valorada Intervalarmente. Tese, Centro de Desenvolvimento Tecnológico. Universidade Federal de Pelotas, Pelotas. Disponível em: [link]. Acesso em: 11 março. 2025.
Park, K. and Pai, V. S. (2006). Comon: a mostly-scalable monitoring system for planetlab. ACM SIGOPS operating systems review, 40(1):65–74. DOI: 10.1145/1113361.1113374
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2025a). skle-arn.metrics.accuracy score - scikit-learn 1.7.1 documentation. Acesso em: 27 agosto. 2025. [link]
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2025b). sskle-arn.metrics.classification report - scikit-learn 1.7.1 documentation. Acesso em: 27 agosto. 2025. DOI: 10.1016/j.measen.2023.100965
Prathiba, S. and Sankar, S. (2024). Energy-efficient resource allocation in cloud infrastructure using L3F-MGA and E-ANFIS. Measurement: Sensors, 31:100965. DOI: 10.1002/9781119994374.ch3
Ross, T. J. (2010). Classical Relations and Fuzzy Relations. John Wiley and Sons, Ltd, USA. DOI: 10.3390/info15100656
Saatchi, R. (2024). Fuzzy logic concepts, developments and implementation. Information, 15(10). DOI: 10.53555/AJBR.v27i4S.4345
Sathyanarayanan, S. and Tantri, B. R. (2024). Confusion matrix-based performance evaluation metrics. African Journal of Biomedical Research, pages 4023–4031. DOI: 10.36227/techrxiv.170473729.96561302/v1
Saxena, D. and Singh, A. K. (2024). A comprehensive survey on sustainable resource management in cloud computing environments. DOI: 10.1016/j.bspc.2021.103417
Shoeibi, A., Ghassemi, N., Khodatars, M., Moridian, P., Alizadehsani, R., Zare, A., Khosravi, A., Subasi, A., Acharya, U. R., and Gorriz, J. M. (2022). Detection of epileptic seizures on eeg signals using anfis classifier, autoencoders and fuzzy entropies. Biomedical Signal Processing and Control, 73:103417.
Singh, H. and Lone, Y. A. (2020). Deep neuro-fuzzy systems with python. Apress, Berkeley. DOI: 10.1142/S0218001409007326
Sun, Y., Wong, A. K., and Kamel, M. S. (2009). Classification of imbalanced data: A review. International journal of pattern recognition and artificial intelligence, 23(04):687–719. DOI: 10.1109/TSMC.1985.6313399
Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE transactions on systems, man, and cybernetics, pages 116–132.
Tan, P.-N., Steinbach, M., and Kumar, V. (2005). Introduction to Data Mining, (First Edition). Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA. DOI: 10.3390/w10091210
Tien Bui, D., Khosravi, K., Li, S., Shahabi, H., Panahi, M., Singh, V. P., Chapi, K., Shirzadi, A., Panahi, S., Chen, W., and Bin Ahmad, B. (2018). New hybrids of anfis with several optimization algorithms for flood susceptibility modeling. Water, 10(9). DOI: 10.1109/ICOCI.2006.5276608
Toosi, A. N., Kahani, M., and Monsefi, R. (2006). Network intrusion detection based on neuro-fuzzy classification. In 2006 International Conference on Computing & Informatics, pages 1–5. IEEE. DOI: 10.1177/1550147721997218
Uddin, M., Hamdi, M., Alghamdi, A., Alrizq, M., Memon, M. S., Abdelhaq, M., and Alsaqour, R. (2021). Server consolidation: A technique to enhance cloud data center power efficiency and overall cost of ownership. International Journal of Distributed Sensor Networks, 17(3):1550147721997218. DOI: 10.5120/ijca2015905635
Walia, N., Singh, H., and Sharma, A. (2015). ANFIS: Adaptive Neuro-Fuzzy Inference System - A Survey. International Journal of Computer Applications, 123(13). DOI: 10.1007/978-1-4615-0377-4_5
Wang, S.-C. (2003). Artificial Neural Network, pages 81–100. Springer US, Boston, MA. DOI: 10.1007/s12083-020-00885-7
Wu, X., Wang, H., Tan, W., Wei, D., and Shi, M. (2020a). Dynamic allocation strategy of VM resources with fuzzy transfer learning method. Peer-to-Peer Networking and Applications, 13(6):2201 – 2213. DOI: 10.1016/j.comcom.2019.11.015
Wu, X., Wang, H., Wei, D., and Shi, M. (2020b). ANFIS with natural language processing and gray relational analysis based cloud computing framework for real time energy efficient resource allocation. Computer Communications, 150:122–130. DOI: 10.1016/S0019-9958(65)90241-X
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3):338–353. DOI: 10.1109/2.53
Zadeh, L. A. (1988). Fuzzy logic. Computer, 21(4):83–93. DOI: 10.1145/175247.175255
Zadeh, L. A. (1994). Fuzzy logic, neural networks, and soft computing. Communications of the ACM, 37(3):77–84.
Publicado
10/09/2025
Como Citar
BUSS, Juliano et al.
Estudo de Casos para Consolidação de Servidores na Computação em Nuvem baseada em ANFIS. In: WORKSHOP-ESCOLA DE INFORMÁTICA TEÓRICA (WEIT), 8. , 2025, Ponta Grossa/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 130-140.
