Alocação Adaptativa de Tarefas na Névoa em Ambientes de Saúde Inteligente
Resumo
A integração de IoT com computação em Névoa e Nuvem permite a implantação de ambientes de saúde pervasivos para auxiliar na prevenção e diagnóstico de doenças. Contudo, é necessário um gerenciamento mais inteligente para realizar o offload de tarefas computacionais nestes ambientes. Este trabalho propõe uma otimização para alocar e re-alocar requisições de processamento em máquinas na Névoa e Nuvem utilizando modelagem matemática. O objetivo é minimizar o custo de utilização da infraestrutura enquanto garante os requisitos das aplicações. Resultados apontam que a otimização gera soluções próximas do ótimo e a reotimização gera soluções ótimas em menos de 7 e 2,8 segundos em média, respectivamente, para instâncias com até 200 pacientes.
Palavras-chave:
Computação em Névoa, healthcare, alocação de recursos
Referências
Arney, D., Plourde, J., and Goldman, J. (2017). Openice medical device interoperability platform overview and requirement analysis. Biomedical Engineering / Biomedizinische Technik, 63.
Gross, J. and Geyer, C. F. (2020). A cost efficient model for minimizing energy consumption and processing time for iot tasks in a mobile edge computing environment. In Anais do XII Simpósio Brasileiro de Computacão Ubíqua e Pervasiva, pages 41–50.
Li, Q., Zhao, J., Gong, Y., and Zhang, Q. (2019). Energy-efficient computation offloading and resource allocation in fog computing for internet of everything. China Communications, 16(3):32–41.
Liu, C., Xiang, F., Wang, P., and Sun, Z. (2019). A review of issues and challenges in fog computing environment. In 2019 IEEE DASC/PiCom/CBDCom/CyberSciTech, pages 232–237.
N. Jayasena, K. P. and Thisarasinghe, B. S. (2019). Optimized task scheduling on fog computing environment using meta heuristic algorithms. In 2019 IEEE International Conference on Smart Cloud (SmartCloud), pages 53–58.
Peralta, G., Iglesias-Urkia, M., Barcelo, M., Gomez, R., Moran, A., and Bilbao, J. (2017). Fog computing based efficient iot scheme for the industry 4.0. In 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM), pages 1–6.
Rahabri, D. and NICKRAY, M. (2019). Low-latency and energy-efficient scheduling in fog-based iot applications. TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 27:1406–1427
Rahbari, D. and Nickray, M. (2017). Scheduling of fog networks with optimized knapsack by symbiotic organisms search. In 2017 21st Conference of Open Innovations Association (FRUCT), pages 278–283.
Redmon, J. and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv.
Rezazadeh, Z., Rezaei, M., and Nickray, M. (2019). Lamp: A hybrid fog-cloud latency-aware module placement algorithm for iot applications. In 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), pages 845–850.
Souza, V. B. C., Ramírez, W., Masip-Bruin, X., Marín-Tordera, E., Ren, G., and Tashakor, G. (2016). Handling service allocation in combined fog-cloud scenarios. In 2016 IEEE International Conference on Communications (ICC), pages 1–5
Vishnu, S., Ramson, S. J., and Jegan, R. (2020). Internet of medical things (iomt) - an overview. In 2020 5th International Conference on Devices, Circuits and Systems (ICDCS), pages 101–104
Wang, X. and Jin, Z. (2019). An overview of mobile cloud computing for pervasive healthcare. IEEE Access, 7:66774–66791
Wen, Z., Yang, R., Garraghan, P., Lin, T., xu, J., and Rovatsos, M. (2017). Fog orchestration for internet of things services. IEEE Internet Computing, 21:16–24
Zhao, X. and Huang, C. (2020). Microservice based computational offloading framework and cost efficient task scheduling algorithm in heterogeneous fog cloud network. IEEE Access, 8:56680–56694.
Gross, J. and Geyer, C. F. (2020). A cost efficient model for minimizing energy consumption and processing time for iot tasks in a mobile edge computing environment. In Anais do XII Simpósio Brasileiro de Computacão Ubíqua e Pervasiva, pages 41–50.
Li, Q., Zhao, J., Gong, Y., and Zhang, Q. (2019). Energy-efficient computation offloading and resource allocation in fog computing for internet of everything. China Communications, 16(3):32–41.
Liu, C., Xiang, F., Wang, P., and Sun, Z. (2019). A review of issues and challenges in fog computing environment. In 2019 IEEE DASC/PiCom/CBDCom/CyberSciTech, pages 232–237.
N. Jayasena, K. P. and Thisarasinghe, B. S. (2019). Optimized task scheduling on fog computing environment using meta heuristic algorithms. In 2019 IEEE International Conference on Smart Cloud (SmartCloud), pages 53–58.
Peralta, G., Iglesias-Urkia, M., Barcelo, M., Gomez, R., Moran, A., and Bilbao, J. (2017). Fog computing based efficient iot scheme for the industry 4.0. In 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM), pages 1–6.
Rahabri, D. and NICKRAY, M. (2019). Low-latency and energy-efficient scheduling in fog-based iot applications. TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 27:1406–1427
Rahbari, D. and Nickray, M. (2017). Scheduling of fog networks with optimized knapsack by symbiotic organisms search. In 2017 21st Conference of Open Innovations Association (FRUCT), pages 278–283.
Redmon, J. and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv.
Rezazadeh, Z., Rezaei, M., and Nickray, M. (2019). Lamp: A hybrid fog-cloud latency-aware module placement algorithm for iot applications. In 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), pages 845–850.
Souza, V. B. C., Ramírez, W., Masip-Bruin, X., Marín-Tordera, E., Ren, G., and Tashakor, G. (2016). Handling service allocation in combined fog-cloud scenarios. In 2016 IEEE International Conference on Communications (ICC), pages 1–5
Vishnu, S., Ramson, S. J., and Jegan, R. (2020). Internet of medical things (iomt) - an overview. In 2020 5th International Conference on Devices, Circuits and Systems (ICDCS), pages 101–104
Wang, X. and Jin, Z. (2019). An overview of mobile cloud computing for pervasive healthcare. IEEE Access, 7:66774–66791
Wen, Z., Yang, R., Garraghan, P., Lin, T., xu, J., and Rovatsos, M. (2017). Fog orchestration for internet of things services. IEEE Internet Computing, 21:16–24
Zhao, X. and Huang, C. (2020). Microservice based computational offloading framework and cost efficient task scheduling algorithm in heterogeneous fog cloud network. IEEE Access, 8:56680–56694.
Publicado
18/07/2021
Como Citar
LIMA, Robertson; SUBRAMANIAN, Anand; MATOS, Fernando.
Alocação Adaptativa de Tarefas na Névoa em Ambientes de Saúde Inteligente. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 13. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 122-131.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2021.16010.
