FC-DT: Otimização Proativa de Recursos em Ambientes Névoa-Nuvem com Suporte de Gêmeos Digitais
Resumo
Ambientes de computação em névoa e nuvem são essenciais para aplicações sensíveis à latência, mas variações dinâmicas de carga dificultam o cumprimento de Acordos de Nível de Serviço (SLA). Estratégias de alocação estática ou reativa podem resultar em violações de SLA ou sobreprovisionamento, elevando os custos operacionais. Este trabalho propõe o FC-DT, um Gêmeo Digital (GD) fundamentado em Redes de Petri Estocásticas (SPNs) para o gerenciamento preditivo e dinâmico de recursos na névoa-nuvem. A SPN fornece uma base formal para simular a dinâmica estocástica do sistema. Explorando essa capacidade, o FC-DT incorpora um mecanismo de tomada de decisão proativa que executa simulações de cenários alternativos em tempo de execução. Com base nos resultados das simulações, o FC-DT ajusta a alocação de recursos de forma coordenada entre as camadas de névoa e nuvem, antecipando possíveis violações de SLA e mantendo a configuração mínima necessária para seu cumprimento. Resultados experimentais mostram que a abordagem garante o cumprimento do SLA mesmo sob picos de carga, reduzindo o uso médio de recursos em 29,65% quando comparado a uma configuração estática mínima necessária para atender ao SLA.Referências
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Ramesh, K., Sasirekha, G., Rao, M., Bapat, J., and Das, D. (2024). Digital twin based what-if simulation of security attacks in smart irrigation systems. In 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), pages 1–6. IEEE.
Silva, L. G., Barbosa, V., Cardoso, I., Alves, M., Lopes, L. S., Rego, P. A., and Silva, F. A. (2025). Dynamically adaptive rsus in vanets: An approach focused on sustainability. International Journal of Communication Systems, 38(16):e70283.
Van Huynh, D., Nguyen, V.-D., Khosravirad, S. R., Karagiannidis, G. K., and Duong, T. Q. (2023). Distributed communication and computation resource management for digital twin-aided edge computing with short-packet communications. IEEE Journal on Selected Areas in Communications, 41(10):3008–3021.
Yang, T.-T., Shen, S.-Y., and Huang, S.-X. (2025). Energy optimization and overheating mitigation using digital twins in fog computing networks. In IET Conference Proceedings CP929, volume 2025, pages 561–567. IET.
Yao, J.-F., Yang, Y., Wang, X.-C., and Zhang, X.-P. (2023). Systematic review of digital twin technology and applications. Visual computing for industry, biomedicine, and art, 6(1):10.
Araújo, J. M., Lopes, L. S., Lima, L. N., Barbosa, V., Sabino, A., Feitosa, L., Delicato, F. C., Nguyen, T. A., and Silva, F. A. (2025). Optimizing intelligent camera surveillance in smart buildings: An spn-based edge-fog analysis. In Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), pages 15–28. SBC.
Bittencourt, L. F., Diaz-Montes, J., Buyya, R., Rana, O. F., and Parashar, M. (2017). Mobility-aware application scheduling in fog computing. IEEE Cloud Computing, 4(2):26–35.
Bittencourt, L. F., Rodrigues-Filho, R., Spillner, J., De Turck, F., Santos, J., Fonseca, N. L., Rana, O., and Parashar, M. (2025). The computing continuum: Past, present, and future. Computer Science Review, 58.
Bonomi, F., Milito, R., Zhu, J., and Addepalli, S. (2012). Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing, pages 13–16.
Boscaro, M., Mason, F., Chiariotti, F., and Zanella, A. (2025). To train or not to train: Balancing efficiency and training cost in deep reinforcement learning for mobile edge computing. In ICC 2025-IEEE International Conference on Communications, pages 2352–2357. IEEE.
Carvalho, M., Menascé, D. A., and Brasileiro, F. (2017). Capacity planning for iaas cloud providers offering multiple service classes. Future Generation Computer Systems, 77:97–111.
Fernando, N., Shrestha, S., Loke, S. W., and Lee, K. (2025). On edge-fog-cloud collaboration and reaping its benefits: a heterogeneous multi-tier edge computing architecture. Future Internet, 17(1):22.
Guo, H., Zhou, X., Wang, J., Liu, J., and Benslimane, A. (2023). Intelligent task offloading and resource allocation in digital twin based aerial computing networks. IEEE Journal on Selected Areas in Communications, 41(10):3095–3110.
Guo, J. and Yang, C. (2018). Predictive resource allocation with deep learning. In 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), pages 1–7. IEEE.
Hayawi, K., Sajid, J., Malik, A. W., and Mathew, S. S. (2025). Digital twin assisted task offloading for workload management at fog nodes. IEEE Internet of Things Journal.
Lakhan, A., Lateef, A. A. A., Abd Ghani, M. K., Abdulkareem, K. H., Mohammed, M. A., Nedoma, J., Martinek, R., and Garcia-Zapirain, B. (2023). Secure-fault-tolerant efficient industrial internet of healthcare things framework based on digital twin federated fog-cloud networks. Journal of King Saud University-Computer and Information Sciences, 35(9):101747.
Little, J. D. and Graves, S. C. (2008). Little’s law. In Building intuition: insights from basic operations management models and principles, pages 81–100. Springer.
Lorido-Botran, T., Miguel-Alonso, J., and Lozano, J. A. (2014). A review of auto-scaling techniques for elastic applications in cloud environments. Journal of grid computing, 12(4):559–592.
Lv, Z. and Lou, R. (2022). Edge-fog-cloud secure storage with deep-learning-assisted digital twins. IEEE Internet of Things Magazine, 5(2):36–40.
Maciel, P., Matos, R., Silva, B., Figueiredo, J., Oliveira, D., Fé, I., Maciel, R., and Dantas, J. (2017). Mercury: Performance and dependability evaluation of systems with exponential, expolynomial, and general distributions. In 2017 IEEE 22nd Pacific Rim international symposium on dependable computing (PRDC), pages 50–57. IEEE.
Mahmud, R., Koch, F. L., and Buyya, R. (2018). Cloud-fog interoperability in iot-enabled healthcare solutions. In Proceedings of the 19th international conference on distributed computing and networking, pages 1–10.
Marsan, M. A., Balbo, G., Conte, G., Donatelli, S., and Franceschinis, G. (1998). Modelling with generalized stochastic petri nets. ACM SIGMETRICS performance evaluation review, 26(2):2.
Nielsen, J. (1994). Usability engineering. Morgan Kaufmann.
Pires, F., Ahmad, B., Moreira, A. P., and Leitão, P. (2021). Digital twin based what-if simulation for energy management. In 2021 4th IEEE international conference on industrial cyber-physical systems (ICPS), pages 309–314. IEEE.
Qadir, M. A., Naeem, M., and Ejaz, W. (2025). Digital twin-assisted multi-layer networks for low-latency and energy-efficient communication. Computer Communications, page 108219.
Ramesh, K., Sasirekha, G., Rao, M., Bapat, J., and Das, D. (2024). Digital twin based what-if simulation of security attacks in smart irrigation systems. In 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), pages 1–6. IEEE.
Silva, L. G., Barbosa, V., Cardoso, I., Alves, M., Lopes, L. S., Rego, P. A., and Silva, F. A. (2025). Dynamically adaptive rsus in vanets: An approach focused on sustainability. International Journal of Communication Systems, 38(16):e70283.
Van Huynh, D., Nguyen, V.-D., Khosravirad, S. R., Karagiannidis, G. K., and Duong, T. Q. (2023). Distributed communication and computation resource management for digital twin-aided edge computing with short-packet communications. IEEE Journal on Selected Areas in Communications, 41(10):3008–3021.
Yang, T.-T., Shen, S.-Y., and Huang, S.-X. (2025). Energy optimization and overheating mitigation using digital twins in fog computing networks. In IET Conference Proceedings CP929, volume 2025, pages 561–567. IET.
Yao, J.-F., Yang, Y., Wang, X.-C., and Zhang, X.-P. (2023). Systematic review of digital twin technology and applications. Visual computing for industry, biomedicine, and art, 6(1):10.
Publicado
25/05/2026
Como Citar
LOPES, Lucas Silva; MIQUEIAS, José; FÉ, Iure; NUNES, Jonas; BITTENCOURT, Luiz Fernando; VALDEMIR JUNIOR, José; SILVA, Francisco Airton.
FC-DT: Otimização Proativa de Recursos em Ambientes Névoa-Nuvem com Suporte de Gêmeos Digitais. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 44. , 2026, Praia do Forte/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 772-785.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2026.19313.
