Um Classificador de Prioridade de Requisições para Alocação de Recursos na Computação em Borda
Resumo
A Computação em Borda permite o processamento de dados e tarefas próximo aos dispositivos finais. No entanto, devido aos recursos limitados dos nós de Borda, priorizar as tarefas que serão executadas é uma técnica eficiente de gerenciar recursos. Implementamos e comparamos três classificadores de prioridade baseados em regras, para determinar os tipos de serviços prioritários, de modo que os recursos dos nós de Borda possam ser alocados de forma otimizada. A proposta explora como os recursos disponíveis podem ser distribuídos entre os serviços em um ambiente simulado. Os resultados mostram que o classificador com melhor desempenho obteve uma acurácia de 94% e um erro geral de 0.22, comprovado com o consumo de CPU dos nós de Borda.
Referências
Bhushan, S. and Mat, M. (2021). Priority-queue based dynamic scaling for efficient resource allocation in fog computing. In 2021 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), pages 1–6.
Bui, T. B., Sakr, A., Castrillón, J., and Schuster, R. (2021). Six-factors score-based match-making based on priority and preemption for resource allocation in edge computing. In 2021 IEEE International Conference on Edge Computing (EDGE), pages 44–50.
Coutinho, A. A., Carneiro, E., and Greve, F. (2016). Computação em Névoa: Conceitos, Aplicações e Desafios, pages 266–315.
Gupta, H., Dastjerdi, A., Ghosh, S., and Buyya, R. (2016). ifogsim: A toolkit for modeling and simulation of resource management techniques in internet of things, edge and fog computing environments. Software: Practice and Experience, 47.
Hong, C.-H. and Varghese, B. (2019). Resource management in fog/edge computing: A survey on architectures, infrastructure, and algorithms. ACM Comput. Surv., 52(5).
Jr., J. B. and Araújo, A. (2021). Uma análise sobre gerenciamento de recursos na computação em névoa. In Anais da IV Escola Regional de Alto Desempenho do Centro-Oeste, pages 7–11, Porto Alegre, RS, Brasil. SBC.
Li, X., Liu, Y., Ji, H., Zhang, H., and Leung, V. C. M. (2019). Optimizing resources allocation for fog computing-based internet of things networks. IEEE Access, 7:64907–64922.
Madej, A., Wang, N., Athanasopoulos, N., Ranjan, R., and Varghese, B. (2020). Priority-based fair scheduling in edge computing. In 2020 IEEE 4th International Conference on Fog and Edge Computing (ICFEC), pages 39–48.
Ribeiro, F. M., Prati, R., Bianchi, R., and Kamienski, C. (2020). A nearest neighbors based data filter for fog computing in iot smart agriculture. In 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pages 63–67.
Rusman, J., Tahir, Z., and Salam, A. E. U. (2019). Fog computing concept implementation in work error detection system of the industrial machine using support vector machine (svm). In 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pages 160–164.
Sharif, Z., Jung, L. T., Razzak, I., and Alazab, M. (2023). Adaptive and priority-based resource allocation for efficient resources utilization in mobile-edge computing. IEEE Internet of Things Journal, 10(4):3079–3093.
Statista (2020). Data volume of iot connected devices worldwide 2019 and 2025. [link]. Acessado em 10/02/2023.
Wang, K., Tan, Y., Shao, Z., Ci, S., and Yang, Y. (2019). Learning-based task offloading for delay-sensitive applications in dynamic fog networks. IEEE Transactions on Vehicular Technology, 68(11):11399–11403.
Zhao, Z., Barijough, K. M., and Gerstlauer, A. (2018). Deepthings: Distributed adaptive deep learning inference on resource-constrained iot edge clusters. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 37(11):2348–2359.
Bui, T. B., Sakr, A., Castrillón, J., and Schuster, R. (2021). Six-factors score-based match-making based on priority and preemption for resource allocation in edge computing. In 2021 IEEE International Conference on Edge Computing (EDGE), pages 44–50.
Coutinho, A. A., Carneiro, E., and Greve, F. (2016). Computação em Névoa: Conceitos, Aplicações e Desafios, pages 266–315.
Gupta, H., Dastjerdi, A., Ghosh, S., and Buyya, R. (2016). ifogsim: A toolkit for modeling and simulation of resource management techniques in internet of things, edge and fog computing environments. Software: Practice and Experience, 47.
Hong, C.-H. and Varghese, B. (2019). Resource management in fog/edge computing: A survey on architectures, infrastructure, and algorithms. ACM Comput. Surv., 52(5).
Jr., J. B. and Araújo, A. (2021). Uma análise sobre gerenciamento de recursos na computação em névoa. In Anais da IV Escola Regional de Alto Desempenho do Centro-Oeste, pages 7–11, Porto Alegre, RS, Brasil. SBC.
Li, X., Liu, Y., Ji, H., Zhang, H., and Leung, V. C. M. (2019). Optimizing resources allocation for fog computing-based internet of things networks. IEEE Access, 7:64907–64922.
Madej, A., Wang, N., Athanasopoulos, N., Ranjan, R., and Varghese, B. (2020). Priority-based fair scheduling in edge computing. In 2020 IEEE 4th International Conference on Fog and Edge Computing (ICFEC), pages 39–48.
Ribeiro, F. M., Prati, R., Bianchi, R., and Kamienski, C. (2020). A nearest neighbors based data filter for fog computing in iot smart agriculture. In 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pages 63–67.
Rusman, J., Tahir, Z., and Salam, A. E. U. (2019). Fog computing concept implementation in work error detection system of the industrial machine using support vector machine (svm). In 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pages 160–164.
Sharif, Z., Jung, L. T., Razzak, I., and Alazab, M. (2023). Adaptive and priority-based resource allocation for efficient resources utilization in mobile-edge computing. IEEE Internet of Things Journal, 10(4):3079–3093.
Statista (2020). Data volume of iot connected devices worldwide 2019 and 2025. [link]. Acessado em 10/02/2023.
Wang, K., Tan, Y., Shao, Z., Ci, S., and Yang, Y. (2019). Learning-based task offloading for delay-sensitive applications in dynamic fog networks. IEEE Transactions on Vehicular Technology, 68(11):11399–11403.
Zhao, Z., Barijough, K. M., and Gerstlauer, A. (2018). Deepthings: Distributed adaptive deep learning inference on resource-constrained iot edge clusters. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 37(11):2348–2359.
Publicado
06/08/2023
Como Citar
ARAÚJO, Guilherme A. de; BEZERRA, Sandy F. C.; ROCHA, Atslands R. da.
Um Classificador de Prioridade de Requisições para Alocação de Recursos na Computação em Borda. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 15. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 131-140.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2023.230787.
