Alocação de Tarefas com Simulated Annealing na Borda da Rede para Internet Industrial
Resumo
Aplicações de Internet Industrial requerem o atendimento de tarefas críticas com baixo tempo de resposta. A Computação na Borda oferece uma alternativa ao processamento de dados na nuvem computacional, pois possibilita reduzir a latência. Este trabalho traz uma abordagem para a alocação de tarefas de veículos industriais. O nó de borda recebe as tarefas dos veículos e deve estabelecer a melhor sequência de atendimento considerando o tempo limite e a prioridade de cada tarefa. A solução foi modelada com base na heurística Simulated Annealing, e os resultados demonstram que a abordagem pôde selecionar combinações de processamento que minimizam o tempo de resposta.Referências
Bai, Y. (2020). 5g industrial iot and edge computing based coal slime flotation foam image processing system. IEEE Access, 8:137606–137615.
Davis, L. (1991). Handbook of genetic algorithms.
Gale, D. and Shapley, L. S. (1962). College admissions and the stability of marriage. The American Mathematical Monthly, 69(1):9–15.
Gupta, H., Vahid Dastjerdi, A., Ghosh, S. K., and Buyya, R. (2017). ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience.
He, J. (2022). Optimization of edge delay sensitive task scheduling based on genetic algorithm. In 2022 International Conference on Algorithms, Data Mining, and Information Technology (ADMIT), pages 155–159.
Hoare, C. A. R. (1961). Algorithm 64: Quicksort. Commun. ACM, 4(7):321.
Hou, W., Wen, H., Zhang, N., Wu, J., Lei, W., and Zhao, R. (2022). Incentive-driven task allocation for collaborative edge computing in industrial internet of things. IEEE Internet of Things Journal, 9(1):706–718.
Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598):671–680.
Matrouk, K. e. (2023). Mobility aware-task scheduling and virtual fog for offloading in iot-fog-cloud environment. Wireless Personal Communications, 130:801–836.
Pearl, J. (1985). Bayesian Networks: A Model of Self-activated Memory for Evidential Reasoning. Report (University of California, Los Angeles. Computer Science Dept.). UCLA Computer Science Department.
Salle, L. and Lefschetz (1961). Stability by Liapunov’s Direct Method: With Applications. Academic Press.
Shi, W., Cao, J., Zhang, Q., Li, Y., and Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5):637–646.
Sun, L., Wang, J., and Lin, B. (2021). Task allocation strategy for mec-enabled iiots via bayesian network based evolutionary computation. IEEE Transactions on Industrial Informatics, 17(5):3441–3449.
Tanenbaum, A. and Bos, H. (2015). Modern Operating Systems. Always learning. Pearson.
Xue, Y., Wu, X., and Yue, J. (2020). An offloading algorithm of dense-tasks for mobile edge computing. In ACM, New York, USA. Association for Computing Machinery.
You, Q. and Tang, B. (2021). Efficient task offloading using particle swarm optimization algorithm in edge computing for industrial internet of things. Cloud Computing.
Davis, L. (1991). Handbook of genetic algorithms.
Gale, D. and Shapley, L. S. (1962). College admissions and the stability of marriage. The American Mathematical Monthly, 69(1):9–15.
Gupta, H., Vahid Dastjerdi, A., Ghosh, S. K., and Buyya, R. (2017). ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience.
He, J. (2022). Optimization of edge delay sensitive task scheduling based on genetic algorithm. In 2022 International Conference on Algorithms, Data Mining, and Information Technology (ADMIT), pages 155–159.
Hoare, C. A. R. (1961). Algorithm 64: Quicksort. Commun. ACM, 4(7):321.
Hou, W., Wen, H., Zhang, N., Wu, J., Lei, W., and Zhao, R. (2022). Incentive-driven task allocation for collaborative edge computing in industrial internet of things. IEEE Internet of Things Journal, 9(1):706–718.
Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598):671–680.
Matrouk, K. e. (2023). Mobility aware-task scheduling and virtual fog for offloading in iot-fog-cloud environment. Wireless Personal Communications, 130:801–836.
Pearl, J. (1985). Bayesian Networks: A Model of Self-activated Memory for Evidential Reasoning. Report (University of California, Los Angeles. Computer Science Dept.). UCLA Computer Science Department.
Salle, L. and Lefschetz (1961). Stability by Liapunov’s Direct Method: With Applications. Academic Press.
Shi, W., Cao, J., Zhang, Q., Li, Y., and Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5):637–646.
Sun, L., Wang, J., and Lin, B. (2021). Task allocation strategy for mec-enabled iiots via bayesian network based evolutionary computation. IEEE Transactions on Industrial Informatics, 17(5):3441–3449.
Tanenbaum, A. and Bos, H. (2015). Modern Operating Systems. Always learning. Pearson.
Xue, Y., Wu, X., and Yue, J. (2020). An offloading algorithm of dense-tasks for mobile edge computing. In ACM, New York, USA. Association for Computing Machinery.
You, Q. and Tang, B. (2021). Efficient task offloading using particle swarm optimization algorithm in edge computing for industrial internet of things. Cloud Computing.
Publicado
21/07/2024
Como Citar
BARBOZA, Vitor Gabriel Reis Lux; KNIESS, Janine.
Alocação de Tarefas com Simulated Annealing na Borda da Rede para Internet Industrial. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 51. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 264-275.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2024.3058.
