Exploring Offloading Strategies in the Analysis of Vital Signs in Fog Computing
Resumo
This paper proposes the Decentralized Adaptive Priority-based Hierarchical Offloading (DAPHO) model for offloading strategies in fog computing applied to healthcare in smart cities. The model handles large volumes of IoT data by prioritizing critical signals in the fog while less critical ones are processed in the cloud. Unlike centralized or single-path approaches, DAPHO enables autonomous offloading decisions among fog nodes hierarchically organized (e.g., neighborhoods, cities). The flexibility of multiple destinations and dynamic thresholds improved response time and prioritized signal processing, although fixed thresholds showed better performance in some scenarios.
Palavras-chave:
Scheduling and Load Balancing
Referências
Adhikari, M., Mukherjee, M., and Srirama, S. N. (2020). Dpto: A deadline and priorityaware task offloading in fog computing framework leveraging multilevel feedback queueing. IEEE Internet of Things Journal, 7(7):5773–5782.
André Setti Cassel, G., da Rosa Righi, R., André da Costa, C., Rosecler Bez, M., and Pasin, M. (2024). Towards providing a priority-based vital sign offloading in healthcare with serverless computing and a fog-cloud architecture. Future Generation Computer Systems, 157:51–66.
da Rosa Righi, R., Rodrigues, V. F., Rostirolla, G., André da Costa, C., Roloff, E., and Navaux, P. O. A. (2018). A lightweight plug-and-play elasticity service for self-organizing resource provisioning on parallel applications. Future Generation Computer Systems, 78:176–190.
Das, R. and Inuwa, M. M. (2023). A review on fog computing: Issues, characteristics, challenges, and potential applications. Telematics and Informatics Reports, 10:100049.
Inzole, A. and Sonwane, S. (2024). Iot in healthcare: Applications challenges. In 2024 IEEE 3rd International Conference on Electrical Power and Energy Systems (ICEPES), pages 1–5.
Mattia, G. P., Magnani, M., and Beraldi, R. (2022). A latency-levelling load balancing algorithm for fog and edge computing. In Proceedings of the 25th International ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems, MSWiM ’22, page 5–14, New York, NY, USA. Association for Computing Machinery.
Phan, L.-A., Nguyen, D.-T., Lee, M., Park, D.-H., and Kim, T. (2021). Dynamic fog-to-fog offloading in sdn-based fog computing systems. Future Generation Computer Systems, 117:486–497.
Robles-Enciso, A. and Skarmeta, A. F. (2023). A multi-layer guided reinforcement learning-based tasks offloading in edge computing. Computer Networks, 220:109476.
Sarkar, I., Adhikari, M., Kumar, N., and Kumar, S. (2022). Dynamic task placement for deadline-aware iot applications in federated fog networks. IEEE Internet of Things Journal, 9(2):1469–1478.
Zare, M., Elmi Sola, Y., and Hasanpour, H. (2023). Towards distributed and autonomous iot service placement in fog computing using asynchronous advantage actor-critic algorithm. Journal of King Saud University - Computer and Information Sciences, 35(1):368–381.
André Setti Cassel, G., da Rosa Righi, R., André da Costa, C., Rosecler Bez, M., and Pasin, M. (2024). Towards providing a priority-based vital sign offloading in healthcare with serverless computing and a fog-cloud architecture. Future Generation Computer Systems, 157:51–66.
da Rosa Righi, R., Rodrigues, V. F., Rostirolla, G., André da Costa, C., Roloff, E., and Navaux, P. O. A. (2018). A lightweight plug-and-play elasticity service for self-organizing resource provisioning on parallel applications. Future Generation Computer Systems, 78:176–190.
Das, R. and Inuwa, M. M. (2023). A review on fog computing: Issues, characteristics, challenges, and potential applications. Telematics and Informatics Reports, 10:100049.
Inzole, A. and Sonwane, S. (2024). Iot in healthcare: Applications challenges. In 2024 IEEE 3rd International Conference on Electrical Power and Energy Systems (ICEPES), pages 1–5.
Mattia, G. P., Magnani, M., and Beraldi, R. (2022). A latency-levelling load balancing algorithm for fog and edge computing. In Proceedings of the 25th International ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems, MSWiM ’22, page 5–14, New York, NY, USA. Association for Computing Machinery.
Phan, L.-A., Nguyen, D.-T., Lee, M., Park, D.-H., and Kim, T. (2021). Dynamic fog-to-fog offloading in sdn-based fog computing systems. Future Generation Computer Systems, 117:486–497.
Robles-Enciso, A. and Skarmeta, A. F. (2023). A multi-layer guided reinforcement learning-based tasks offloading in edge computing. Computer Networks, 220:109476.
Sarkar, I., Adhikari, M., Kumar, N., and Kumar, S. (2022). Dynamic task placement for deadline-aware iot applications in federated fog networks. IEEE Internet of Things Journal, 9(2):1469–1478.
Zare, M., Elmi Sola, Y., and Hasanpour, H. (2023). Towards distributed and autonomous iot service placement in fog computing using asynchronous advantage actor-critic algorithm. Journal of King Saud University - Computer and Information Sciences, 35(1):368–381.
Publicado
23/04/2025
Como Citar
THOMAS, Thiago Henrique; RIGHI, Rodrigo da Rosa.
Exploring Offloading Strategies in the Analysis of Vital Signs in Fog Computing. In: ESCOLA REGIONAL DE ALTO DESEMPENHO DA REGIÃO SUL (ERAD-RS), 25. , 2025, Foz do Iguaçu/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 69-72.
ISSN 2595-4164.
DOI: https://doi.org/10.5753/eradrs.2025.6753.
